Premium SSD v2 Is Now Generally Available for Azure Database for PostgreSQL
We are excited to announce the General Availability (GA) of Premium SSD v2 for Azure Database for PostgreSQL flexible server. With Premium SSD v2, you can achieve up to 4× higher IOPS, significantly lower latency, and better price-performance for I/O-intensive PostgreSQL workloads. With independent scaling of storage and performance, you can now eliminate overprovisioning and unlock predictable, high-performance PostgreSQL at scale. This release is especially impactful for OLTP, SaaS, and high‑concurrency applications that require consistent performance and reliable scaling under load. In this post, we will cover: Why Premium SSD v2: Core capabilities such as flexible disk sizing, higher performance, and independent scaling of capacity and I/O. Premium SSD v2 vs. Premium SSD: A side‑by‑side overview of what’s new and what’s improved. Pricing: Pricing estimates. Performance: Benchmarking results across two workload scenarios. Migration options: How to move from Premium SSD to Premium SSD v2 using restore and read‑replica approaches. Availability and support: Regional availability, supported features, current limitations, and how to get started. Why Premium SSD v2? Flexible Disk Size - Storage can be provisioned from 32 GiB to 64 TiB in 1 GiB increments, allowing you to pay only for required capacity without scaling disk size for performance. High Performance -Achieve up to 80,000 IOPS and 1,200 MiB/s throughput on a single disk, enabling high-throughput OLTP and mixed workloads. Adapt instantly to workload changes: With Premium SSD v2, performance is no longer tied to disk size. Independently tune IOPS and throughput without downtime, ensuring your database keeps up with real-time demand. Free baseline performance: Premium SSD v2 includes built-in baseline performance at no additional cost. Disks up to 399 GiB automatically include 3,000 IOPS and 125 MiB/s, while disks sized 400 GiB and larger include up to 12,000 IOPS and 500 MiB/s. Premium SSD v2 vs. Premium SSD: What’s new? Pricing Pricing for Premium SSD v2 is similar to Premium SSD, but will vary depending on the storage, IOPS, and bandwidth configuration set for a Premium SSD v2 disk. Pricing information is available on the pricing page or pricing calculator. Performance Premium SSD v2 is designed for IO‑intensive workloads that require sub‑millisecond disk latencies, high IOPS, and high throughput at a lower cost. To demonstrate the performance impact, we ran pgbench on Azure Database for PostgreSQL using the test profile below. Test Setup To minimize external variability and ensure a fair comparison: Client virtual machines and the database server were deployed in the same availability zone in the East US region. Compute, region, and availability zones were kept identical. The only variable changed was the storage tier. TPC-B benchmark using pgbench with a database size of 350 GiB. Test Scenario 1: Breaking the IOPS Ceiling with Premium SSD v2 Premium SSD v2 eliminates the traditional storage bottleneck by scaling linearly up to 80,000 IOPS, while Premium SSD plateaus early due to fixed performance limits. To demonstrate this, we configured each storage tier with its maximum supported IOPS and throughput while keeping all other variables constant. Premium SSD v2 achieves up to 4x higher IOPS at nearly half the cost, without requiring large disk sizes. Note: Premium SSD requires a 32 TiB disk to reach 20K IOPS, while SSD v2 achieves 80K IOPS even on a 160 GiB disk though we used 1 TiB disk in this test for a bigger scaling factor for pgbench test. We ran pgbench across five workload profiles, ranging from 32 to 256 concurrent clients, with each test running for 20 minutes. The results go beyond incremental improvements and highlight a material shift in how applications scale with Premium SSD v2. Throughput Scaling As concurrency increases, Premium SSD quickly reaches its IOPS limits while Premium SSD v2 continues to scale. At 32 clients: Premium SSD v2 achieved 10,562 TPS vs 4,123 TPS on Premium SSD representing a 156% performance improvement. At 256 clients: At higher load, Premium SSD v2 achieved over 43,000 TPS representing a 279% improvement compared to the 11,465 TPS observed on Premium SSD. Latency Stability Throughput is an indication of how much work is done while latency reflects how quickly users experience it. Premium SSD v2 maintains consistently low latency even as workload increases. Reduced Wait Times: 61–74% lower latency across all test phases. Consistency under Load: Premium SSD latency increased to 22.3 ms, while Premium SSD v2 maintained a latency of 5.8 ms, remaining stable even under peak load. IOPS Behavior The table below illustrates the IOPS behavior observed during benchmarking for both storage tiers. Dimension Premium SSD Premium SSD v2 IOPS Lower baseline performance, Hits limits early ~2× higher IOPS at low concurrency, Up to 4× higher IOPS at peak load IOPS Plateau Throughput stalls at ~20k IOPS for 64 clients -256 clients Scales from ~29k IOPS (32 clients) to ~80k IOPS (256 clients) Additional Clients Adding clients does not increase throughput Additional clients continue to drive higher throughput Primary Bottleneck Storage becomes the bottleneck early No single bottleneck observed Scaling Behavior Stops scaling early True linear scaling with workload demand Resource Utilization Disk saturation leaves CPU and memory underutilized Balanced utilization across IOPS, CPU, and memory Key Takeaway Storage limits performance before compute is fully used Unlocks higher throughput and lower latency by fully utilizing compute resources Test Scenario 2: Better Performance at same price At the same price point, Premium SSD v2 delivers higher throughput and lower latency than Premium SSD without requiring any application changes. To demonstrate this, we ran multiple pgbench tests using two workload configurations 8 clients / 8 threads and 32 clients / 32 threads with each run lasting 20 minutes. Results were consistent across all runs, with Premium SSD v2 consistently outperforming Premium SSD. Both configurations cost $578/month, the only difference is storage performance. Results: Moderate concurrency (8 clients) Premium SSD v2 delivered approximately 154% higher throughput (Transactions Per Second) than Premium SSD (1,813 TPS vs. 715 TPS), while average latency decreased by about 60% (from ~11.1 ms to ~4.4 ms). High concurrency (32 clients) The performance gap increases as concurrency grows, Premium SSD v2 delivered about 169% higher throughput than Premium SSD (3,643 TPS vs. ~1,352 TPS) and reduced average latency by around 67% (from ~26.3 ms to ~8.7 ms). IOPS Behavior In the 8‑client, 8‑thread test, Premium SSD reached its IOPS ceiling early, operating at 100% utilization, while Premium SSD v2 retained approximately 30% headroom under the same workload delivering 8,037 IOPS vs 3,761 IOPS with Premium SSD. When the workload increased to 32 clients and 32 threads, both tiers approached their IOPS limits however, Premium SSD v2 sustained a significantly higher performance ceiling, delivering approximately 2.75x higher IOPS (13,620 vs. 4,968) under load. Key Takeaway: With Premium SSD v2, you do not need to choose between cost and performance you get both. At the same price, applications run faster, scale further, and maintain lower latency without any code changes. Migrate from Premium SSD to Premium SSD v2 Migrating is simple and fast. You can migrate from Premium SSD to Premium SSD v2 using the two strategies below with minimal downtime. These methods are generally quicker than logical migration strategies, such as exporting and restoring data using pg_dump and pg_restore. Restore from Premium SSD to Premium SSD v2 Migrate using Read Replicas When migrating from Premium SSD to Premium SSD v2, using a virtual endpoint helps keep downtime to a minimum and allows applications to continue operating without requiring configuration changes after the migration. After the migration completes, you can stop the original server until your backup requirements are met. Once the required backup retention period has elapsed and all new backups are available on the new server, the original server can be safely deleted. Region Availability & Features Supported Premium SSD v2 is available in 48 regions worldwide for Azure Database for PostgreSQL – Flexible Server. For the most up‑to‑date information on regional availability, supported features, and current limitations, refer to the official Premium SSD v2 documentation. Getting Started: To learn more, review the official documentation for storage configuration available with Azure Database for PostgreSQL. Your feedback is important to us, have suggestions, ideas, or questions? We would love to hear from you: https://aka.ms/pgfeedback.Bidirectional Replication with pglogical on Azure Database for PostgreSQL - a VNET guide
Editor’s Note: This article was written by Raunak Jhawar, a Chief Architect. Paula Berenguel and Guy Bowerman assisted with the final review, formatting and publication. Overview Bidirectional replication is one of the most requested topologies requiring writes in multiple locations, selective sync, geo-distributed active-active, or even accepting eventual consistency. This is a deep technical walkthrough for implementing bidirectional (active‑active) replication on private Azure Database for PostgreSQL Server using pglogical, with a strong emphasis on VNET‑injected architectures. It explains the underlying networking and execution model covering replication worker placement, DNS resolution paths, outbound connectivity, and conflict resolution mechanics to show why true private, server‑to‑server replication is only achievable with VNET injection and not with Private Endpoints. It also analyzes the operational and architectural trade‑offs needed to safely run geo distributed, multi write PostgreSQL workloads in production. This blog post focus on pglogical however, if you are looking for steps to implement it with logical replication or pros and cons of which approach, please refer to my definitive guid to bi-directional replication in Azure Database for PostgreSQL blog post Why this is important? This understanding prevents fundamental architectural mistakes (such as assuming Private Endpoints provide private outbound replication), reduces deployment failures caused by hidden networking constraints, and enables teams to design secure, compliant, low‑RPO active/active or migration architectures that behave predictably under real production conditions. It turns a commonly misunderstood problem into a repeatable, supportable design pattern rather than a trial‑and‑error exercise. Active-Active bidirectional replication between instances Architecture context This scenario targets a multi-region active-active write topology where both nodes are injected into the same Azure VNET (example - peered VNETs on Azure or even peered on-premises), both accept writes. Common use case: Geo distributed OLTP with regional write affinity. Step 1: Azure Infrastructure Prerequisites Both server instances must be deployed with VNET injection. This is a deploy time decision and you cannot migrate a publicly accessible instance (with or without private endpoint) to VNET injection post creation without rebuilding it. Each instance must live in a delegated subnet: Microsoft.DBforPostgreSQL/Servers. The subnet delegation is non-negotiable and prevents you from placing other resource types in the same subnet, so plan your address space accordingly. If nodes are in different VNETs, configure VNET peering before continuing along with private DNS integration. Ensure there are no overlapping address spaces amongst the peered networks. NSG rules must allow port 5432 between the two delegated subnets, both inbound and outbound. You may choose to narrow down the NSG rules to meet your organization requirements and policies to a specific source/target combination allow or deny list. Step 2: Server Parameter Configuration On both nodes, configure the following server parameters via the Azure Portal (Server Parameters blade) or Azure CLI. These cannot be set via ALTER SYSTEM SET commands. wal_level = logical -- This setting enables logical replication, which is required for pglogical to function. max_worker_processes = 16 -- This setting allows for more worker processes, which can help with replication performance. max_replication_slots = 10 -- This setting allows for more replication slots, which are needed for pglogical to manage replication connections. max_wal_senders = 10 -- This setting allows for more WAL sender processes, which are responsible for sending replication data to subscribers. track_commit_timestamp = on -- This setting allows pglogical to track commit timestamps, which can be useful for conflict resolution and monitoring replication lag. shared_preload_libraries = pglogical -- This setting loads the pglogical extension at server startup, which is necessary for it to function properly. azure.extensions = pglogical -- This setting allows the pglogical extension to be used in the Azure Postgres PaaS environment. Both nodes require a restart after shared_preload_libraries and wal_level changes. Note that max_worker_processes is shared across all background workers in the instance. Each pglogical subscription consumes workers. If you are running other extensions, account for their worker consumption here or you will hit startup failures for pglogical workers. Step 3: Extension and Node Initialization Create a dedicated replication user on both nodes. Do not use the admin account for replication. CREATE ROLE replication_user WITH LOGIN REPLICATION PASSWORD 'your_password'; GRANT USAGE ON SCHEMA public TO replication_user; GRANT SELECT ON ALL TABLES IN SCHEMA public TO replication_user; ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO replication_user; Log into Server A either via a VM in the specified VNET or Azure Bastion Host and run the following which creates the extension, a replication set and policies. CREATE EXTENSION IF NOT EXISTS pglogical; SELECT pglogical.create_node(node_name := 'node_a', dsn := 'host.fqdn-for-server-a port=5432 dbname=preferred-database user=replication_user password=<strong_password>'); -- Define the replication set for Server A, specifying which tables to replicate and the types of operations to include (inserts, updates, deletes). SELECT pglogical.create_replication_set(set_name := 'node_a_set', replicate_insert := true, replicate_update := true, replicate_delete := true, replicate_truncate := false); -- Add sales_aus_central table explicitly SELECT pglogical.replication_set_add_table(set_name := 'node_a_set', relation := 'public.sales_aus_central', synchronize_data := true); -- Add purchase_aus_central table explicitly SELECT pglogical.replication_set_add_table(set_name := 'node_a_set', relation := 'public.purchase_aus_central', synchronize_data := true); -- OR add all tables in the public schema SELECT pglogical.replication_set_add_all_tables('default', ARRAY['public']); -- This command adds all tables in the public schema to the default replication set. -- Now, repeat this on Server B using the same method above i.e. via a VM in the specified VNET or Azure Bastion Host CREATE EXTENSION IF NOT EXISTS pglogical; -- Define the replication set for Server B, specifying which tables to replicate and the types of operations to include (inserts, updates, deletes) SELECT pglogical.create_node(node_name := 'node_b', dsn := 'host-fqdn-for-server-b port=5432 dbname=preferred-database user=replication_user password=<strong_password>'); SELECT pglogical.create_replication_set( set_name := 'node_b_set', replicate_insert := true, replicate_update := true, replicate_delete := true, replicate_truncate := false); -- Add sales_aus_east table explicitly SELECT pglogical.replication_set_add_table( set_name := 'node_b_set', relation := 'public.sales_aus_east', synchronize_data := true); -- Add purchase_aus_east table explicitly SELECT pglogical.replication_set_add_table( set_name := 'node_b_set', relation := 'public.purchase_aus_east', synchronize_data := true); -- OR add all tables in the public schema SELECT pglogical.replication_set_add_all_tables('default', ARRAY['public']); -- This command adds all tables in the public schema to the default replication set. It is recommended that you confirm the DNS resolution on all server’s involved as part of the replication process. For a VNET injected scenarios – you must get back the private IP. As a sanity check, you can run the nslookup on the target server’s FQDN or even use the \conninfo command to see the connection details. One such example is here: Step 4: Configuring the subscribers SELECT pglogical.create_subscription ( -- Create a subscription on Server A to receive changes from Server B subscription_name := 'node_a_to_node_b', replication_sets := array['default'], synchronize_data := true, forward_origins := '{}', provider_dsn := 'host=fqdn-for-server-b port=5432 dbname=preferred-database user=replication_user password=<strong_password>'); -- Run this on Server B to subscribe to changes from Server A SELECT pglogical.create_subscription ( -- Create a subscription on Server B to receive changes from Server A subscription_name := 'node_b_to_node_a', replication_sets := array['default'], synchronize_data := true, forward_origins := '{}', provider_dsn := 'host=fqdn-for-server-a port=5432 dbname=preferred-database user=replication_user password=<strong_password>'); For most OLTP workloads, last_update_wins using the commit timestamp is the most practical choice. It requires track_commit_timestamp = on, which you must set as a server parameter. The FQDN must be used rather than using the direct private IP of the server itself. Bidirectional replication between server instances with private endpoints – does this work and will this make your server security posture weak? Where do pglogical workers run? With VNET injection, the server's network interface lives inside your delegated subnet which is a must do. The PostgreSQL process including all pglogical background workers starts connections from within your VNET (delegated subnet). The routing tables, NSGs, and peering apply to both inbound and outbound traffic from the server. With Private Endpoint, the architecture is fundamentally different: Private endpoint is a one-way private channel for your clients or applications to reach the server securely. It does not give the any of server’s internal processes access to your VNET for outbound connectivity. pglogical subscription workers trying to connect to another server are starting those connections from Microsoft's managed infrastructure and not from your VNET. What works? Scenario A: Client connectivity via private endpoint Here you have application servers or VMs in your VNET connecting to a server configured with a private endpoint, your app VM connects to 10.0.0.15 (the private endpoint NIC), traffic flows over Private Link to the server, and everything stays private. This is not server-to-server replication. Scenario B: Two servers, both with private endpoints Here both servers are in Microsoft's managed network. They can reach each other's public endpoints, but not each other's private endpoints (which are in customer VNETs). The only path for bidirectional replication worker connections is to enable public network access on both servers with firewall rules locked down to Azure service IP. Here you have private endpoints deployed alongside public access. Inside your VNET, SERVER A resolves to the private endpoint IP via the privatelink.postgres.database.azure.com private DNS zone. But the pglogical worker running in Microsoft's network does not have access to your private DNS zone and it resolves via public DNS, which returns the public IP. This means if you are using the public FQDN for replication, the resolution path is consistent from the server's perspective (always public DNS, always public IP using the allow access to Azure services flag as shown above). Your application clients in the VNET will still resolve to the private endpoint. If your requirement is genuinely private replication with no public endpoint exposure, VNET injection is the correct answer, and private endpoint cannot replicate that capability for pglogical. Conclusion The most compelling benefit in the VNET-injected topology is network isolation without sacrificing replication capability. You get the security posture of private connectivity i.e. no public endpoints, NSG controlled traffic, private DNS resolution all while keeping a live bidirectional data pipeline. This satisfies most enterprise compliance requirements around data transit encryption and network boundary control. The hub/spoke migration (specifically, on-premises or external cloud to Azure) scenarios are where this approach shines. The ability to run both systems in production simultaneously, with live bidirectional sync during the cutover window, reduces migration risk when compared to a hard cutover. From a DR perspective, bidirectional pglogical gives you an RPO measured in seconds (replication lag dependent) without the cost of synchronous replication. For workloads that can tolerate eventual consistency and have well-designed conflict avoidance this is a compelling alternative to synchronous streaming replication via read replicas, which are strictly unidirectional.Microsoft at PGConf India 2026
I’m genuinely excited about PGConf India 2026. Over the past few editions, the conference has continued to grow year over year—both in size and in impact—and it has firmly established itself as one of the key events on the global PostgreSQL calendar. That momentum was very evident again in the depth, breadth, and overall quality of the program for PGConf India 2026. Microsoft is proud to be a diamond sponsor for the conference again this year. At Microsoft, we continue our contributions to the upstream PostgreSQL open-source project—as well as to serve our customers with our Postgres managed service offerings, both Azure Database for PostgreSQL and our newest Postgres offering, Azure HorizonDB . On the open-source front, Microsoft had 540 commits in PG18, including major features like Asynchronous IO. We’re also excited to grow our Postgres open-source contributors team, and so happy to welcome Noah Misch to our team. Noah is a Postgres committer who has deep expertise in PostgreSQL security and is focused on correctness and reliability in PostgreSQL’s core. Microsoft at PGConf India 2026: Highlights from Our Speakers PGConf India has several tracks, all of which have some great talks I am looking forward to. First, the plug. 😊 Microsoft has some amazing talks this year, and we have 8 different talks spread across all the tracks. Postgres on Azure : Scaling with Azure HorizonDB, AI, and Developer Workflows, by Aditya Duvuri & Divya Bhargov Resizing shared buffer pool in a running PostgreSQL server: important, yet impossible, by Ashutosh Bapat Ten Postgres Hacker Journeys—and what they teach us, by Claire Giordano How Postgres can leverage disk bandwidth for better TPS, by Nikhil Chawla AWSM FSM! Free Space Maps Decoded by Nikhil Sontakke Journey of developing a Performance Optimization Feature in PostgreSQL, by Rahila Syed Build Agentic AI with Semantic Kernel and Graph RAG on PostgreSQL, by Shriram Muthukrishnan & Palak Chaturvedi All things Postgres @ Microsoft (2026 edition) by Sumedh Pathak Claire is an amazing speaker and has done a lot of work over the last several years documenting and understanding PostgreSQL committers and hackers. Her talk will definitely have some key insights and nuggets of information. Rahila’s talk will go in depth on performance optimization features and how best to test and benchmark them, and all the tools and tricks she has used as part of the feature development. This should be a must-see talk for anyone doing performance work. Diving Deep: Case Studies & Technical Tracks One of the tracks I’m really excited about is the Case Study track. I see these as similar to ‘Experience’ papers in academia. An experience paper documents what actually happened when applying a technique or system in the real world, what worked, what didn’t, and why. One of the talks I’m looking forward to is ‘Operating Postgres Logical Replication at Massive Scale’ by Sai Srirampur from Clickhouse. Logical Replication is an extremely useful tool, and I’m curious to learn more about pitfalls and lessons learnt when running this at large scale. Another interesting one I’m curious to hear is ‘Understanding the importance of the commit log through a database corruption’ by Amit Kumar Singh from EDB. The Database Engine Developers track allows us to go deep into the PostgreSQL code base and get a better understanding of how PostgreSQL is built. Even if you are not a database developer, this track is useful to understand how and why PostgreSQL does things, helping you be a better user of the database. With the rise of larger machines and memory available in the Cloud, different and newer memory architectures/tiers and serverless product offerings, there is a lot of deep dive in PostgreSQL’s memory architecture. There are some great talks focused on this area, which should be must-see for anyone interested in this topic: Resizing shared buffer pool in a running PostgreSQL server: important, yet impossible by Ashutosh Bapat from Microsoft From Disk to Data: Exploring PostgreSQL's Buffer Management by Lalit Choudhary from PurnaBIT Beyond shared_buffers: On-Demand Memory in Modern PostgreSQL by Vaibhav Popat from Google Finally, the Database Administration and Application Developer tracks have some really great content as well. They cover a wide range of topics, from PII data, HA/DR, Query Tuning to connection pooling and understanding conflict detection and resolution. PostgreSQL in India: A Community Effort Worth Celebrating Conferences like these are a rich source of information, dramatically increasing my personal understanding of the product and the ecosystem. Separately, they are also a great way to meet other practitioners in the space and connect with people in the industry. For people in Bangalore, another great option is the PostgreSQL Bangalore Meetup, and I’m super happy that Microsoft was able to join the ranks of other companies to host the eighth iteration of this meetup. Finally, I would be remiss in not mentioning the hard work done by the PGConf India organizing team including Pavan Deolasse, Ashish Mehra, Nikhil Sontakke, Hari Kiran, and Rushabh Lathia who are making all of this happen. Also, a big shout out to the PGConf India Program Committee (Amul Sul, Dilip Kumar, Marc Linster, Thomas Munro, Vigneshwaran C) for putting together an amazing set of talks. I look forward to meeting all of you in Bangalore! Be sure to drop by the Microsoft booth to say hello (and to snag a free pair of our famous socks). I’d love to learn more about how you’re using Postgres.Distribute PostgreSQL 18 with Citus 14
The Citus 14.0 release is out and includes PostgreSQL 18 support! We know you've been waiting, and we've been hard at work adding features we believe will take your experience to the next level, focusing on bringing the Postgres 18 exciting improvements to you at distributed scale. The Citus database is an open-source extension of Postgres that brings the power of Postgres to any scale, from a single node to a distributed database cluster. Since Citus is an extension, using Citus means you're also using Postgres, giving you direct access to the Postgres features. And the latest of such features came with Postgres 18 release! PostgreSQL 18 is a substantial release: asynchronous I/O (AIO), skip-scan for multicolumn B-tree indexes, uuidv7(), virtual generated columns by default, OAuth authentication, RETURNING OLD/NEW, and temporal constraints. For those of you who are interested in upgrading to Postgres 18 and scaling these new features of Postgres: you can upgrade to Citus 14.0! Let's take a closer look at what's new in Citus 14.0. Postgres 18 support in Citus 14.0 Citus 14.0 introduces support for PostgreSQL 18. This means that just by enabling PG18 in Citus 14.0, all the query performance improvements directly reflect on the Citus distributed queries, and several optimizer improvements benefit queries in Citus out of the box! Among the many new features in PG 18, the following capabilities enabled in Citus 14.0 are especially noteworthy for Citus users. To learn more about how you can use Citus 14.0 + PostgreSQL 18, as well as currently unsupported features and future work, you can consult the Citus 14.0 Updates page, which gives you detailed release notes. PostgreSQL 18 highlights that benefit Citus clusters Because Citus is implemented as a Postgres extension, the following PG18 improvements benefit your distributed cluster automatically, no Citus-specific changes needed. Faster scans and maintenance via AIO Postgres 18 adds an asynchronous I/O subsystem that can improve sequential scans, bitmap heap scans, and vacuuming—workloads that show up constantly in shard-heavy distributed clusters. This means your Citus cluster can benefit from faster table scans and more efficient maintenance operations without any code changes. You can control the I/O method via the new io_method GUC: -- Check the current I/O method SHOW io_method; Better index usage with skip-scan Postgres 18 expands when multicolumn B-tree indexes can be used via skip scan, helping common multi-tenant schemas where predicates don't always constrain the leading index column. This is particularly valuable for Citus users with multi-tenant applications where queries often filter by non-leading columns. -- Multi-tenant index: (tenant_id, created_at) -- PG18 skip-scan lets this query use the index even without tenant_id SELECT * FROM events WHERE created_at > now() - interval '1 day' ORDER BY created_at DESC LIMIT 100; uuidv7() for time-ordered UUIDs Time-ordered UUIDs can reduce index churn and improve locality; Postgres 18 adds uuidv7(). This is especially useful for distributed tables where you want predictable ordering and better index performance across shards. -- Use uuidv7() as a time-ordered primary key CREATE TABLE events ( id uuid DEFAULT uuidv7() PRIMARY KEY, tenant_id bigint, payload jsonb ); SELECT create_distributed_table('events', 'tenant_id'); OAuth authentication support Postgres 18 adds OAuth authentication, making it easier to plug database auth into modern SSO flows often a practical requirement in multi-node deployments. This simplifies authentication management across your Citus coordinator and worker nodes. What Citus 14 adds for PostgreSQL 18 compatibility While the highlights above work out of the box, PG18 also introduces new SQL syntax and behavior changes that require Citus-specific work parsing/deparsing, DDL propagation across coordinator + workers, and distributed execution correctness. Here's what we built to make these work end-to-end. JSON_TABLE() COLUMNS PG18 expands SQL/JSON JSON_TABLE() with a richer COLUMNS clause, making it easy to extract multiple fields from JSON documents in a single, typed table expression. Citus 14 ensures the syntax can be parsed/deparsed and executed consistently in distributed queries. CREATE TABLE pg18_json_test (id serial PRIMARY KEY, data JSON); SELECT jt.name, jt.age FROM pg18_json_test, JSON_TABLE( data, '$.user' COLUMNS ( age INT PATH '$.age', name TEXT PATH '$.name' ) ) AS jt WHERE jt.age BETWEEN 25 AND 35 ORDER BY jt.age, jt.name; Temporal constraints Postgres 18 adds temporal constraint syntax that Citus must propagate and preserve correctly: WITHOUT OVERLAPS for PRIMARY KEY / UNIQUE PERIOD for FOREIGN KEY CREATE TABLE temporal_rng ( id int4range, valid_at daterange, CONSTRAINT temporal_rng_pk PRIMARY KEY (id, valid_at WITHOUT OVERLAPS) ); SELECT create_distributed_table('temporal_rng', 'id'); CREATE FOREIGN TABLE ... LIKE Postgres 18 supports CREATE FOREIGN TABLE ... LIKE, letting you define a foreign table by copying the column layout (and optionally defaults/constraints/indexes) from an existing table. Citus 14 includes coverage so FDW workflows remain compatible in distributed environments. -- Copy column layout from an existing table CREATE FOREIGN TABLE my_ft (LIKE my_local_table EXCLUDING ALL) SERVER foreign_server OPTIONS (schema_name 'public', table_name 'my_local_table'); Generated columns (Virtual by Default) PostgreSQL 18 changes generated column behavior significantly: Virtual by default: Generated columns are now computed on read rather than stored, reducing write amplification Logical replication support: New publish_generated_columns publication option for replicating generated values CREATE TABLE events ( id bigint, payload jsonb, payload_hash text GENERATED ALWAYS AS (md5(payload::text)) VIRTUAL ); SELECT create_distributed_table('events', 'id'); VACUUM/ANALYZE ONLY semantics Postgres 18 introduces ONLY for VACUUM and ANALYZE so you can explicitly target only the parent of a partitioned/inheritance tree without automatically processing children. Citus 14 adapts distributed utility-command behavior so ONLY works as intended. -- Parent-only: do not recurse into partitions/children VACUUM (ANALYZE) ONLY metrics; ANALYZE ONLY metrics; Constraints: NOT ENFORCED + partitioned-table additions Postgres 18 expands constraint syntax in several ways that Citus must parse/deparse and propagate across coordinator + workers: CHECK constraints can be marked NOT ENFORCED FOREIGN KEY constraints can be marked NOT ENFORCED NOT VALID foreign keys on partitioned tables DROP CONSTRAINT ONLY on partitioned tables ALTER TABLE orders ADD CONSTRAINT orders_amount_positive CHECK (amount > 0) NOT ENFORCED; ALTER TABLE orders ADD CONSTRAINT orders_customer_fk FOREIGN KEY (customer_id) REFERENCES customers(id) NOT ENFORCED; DML: RETURNING OLD/NEW Postgres 18 lets RETURNING reference both the previous (old) and new (new) row values in INSERT/UPDATE/DELETE/MERGE. Citus 14 preserves these semantics in distributed execution. UPDATE t SET v = v + 1 WHERE id = 42 RETURNING old.v AS old_v, new.v AS new_v; COPY expansions PG18 adds two useful COPY improvements that Citus 14 supports in distributed queries: COPY ... REJECT_LIMIT: set a threshold for how many rows can be rejected before the COPY fails, useful for resilient bulk loading into sharded tables COPY table TO from materialized views: export data directly from materialized views -- Tolerate up to 10 bad rows during bulk load COPY my_distributed_table FROM '/data/import.csv' WITH (FORMAT csv, REJECT_LIMIT 10); MIN()/MAX() on arrays and composite types PG18 extends MIN() and MAX() aggregates to work on arrays and composite types. Citus 14 ensures these aggregates work correctly in distributed queries. CREATE TABLE sensor_data ( tenant_id bigint, readings int[] ); SELECT create_distributed_table('sensor_data', 'tenant_id'); -- Now works with array columns SELECT MIN(readings), MAX(readings) FROM sensor_data; Nondeterministic collations PG18 extends LIKE and text-position search functions to work with nondeterministic collations. Citus 14 verifies these work correctly across distributed queries. sslkeylogfile connection parameter PG18 adds the sslkeylogfile libpq connection parameter for dumping SSL key material, useful for debugging encrypted connections. Citus 14 allows configuring this via citus.node_conn_info so it works across inter-node connections. Planner fix: enable_self_join_elimination PG18 introduces the enable_self_join_elimination planner optimization. Citus 14 ensures this works correctly for joins between distributed and local tables, avoiding wrong results that could occur in early PG18 integration. Utility/Ops plumbing and observability Citus 14 adapts to PG18 interface/output changes that affect tooling and extension plumbing: New GUC file_copy_method for CREATE DATABASE ... STRATEGY=FILE_COPY EXPLAIN (WAL) adds a "WAL buffers full" field; Citus propagates it through distributed EXPLAIN output New extension macro PG_MODULE_MAGIC_EXT so extensions can report name/version metadata New libpq parameter sslkeylogfile support via citus.node_conn_info Diving deeper into Citus 14.0 and distributed Postgres To learn more about Citus 14.0, you can: Check out the 14.0 Updates page to get the detailed release notes. As of this release, Citus documentation is now hosted on Microsoft Learn. With Citus 14, elastic clusters will soon support PostgreSQL 18, now available in Azure Database for PostgreSQL. You can stay connected on the Citus Slack and visit the Citus open source GitHub repo to see recent developments as well. If there's something you'd like to see next in Citus, feel free to also open a feature request issue :)Supporting ChatGPT on PostgreSQL in Azure
Affan Dar, Vice President of Engineering, PostgreSQL at Microsoft Adam Prout, Partner Architect, PostgreSQL at Microsoft Panagiotis Antonopoulos, Distinguished Engineer, PostgreSQL at Microsoft The OpenAI engineering team recently published a blog post describing how they scaled their databases by 10x over the past year, to support 800 million monthly users. To do so, OpenAI relied on Azure Database for PostgreSQL to support important services like ChatGPT and the Developer API. Collaborating with a customer experiencing rapid user growth has been a remarkable journey. One key observation is that PostgreSQL works out of box for very large-scale points. As many in the public domain have noted, ChatGPT grew to 800M+ users before OpenAI started moving new and shardable workloads to Azure Cosmos DB. Nevertheless, supporting the growth of one of the largest Postgres deployments was a great learning experience for both of our teams. Our OpenAI friends did an incredible job at reacting fast and adjusting their systems to handle the growth. Similarly, the Postgres team at Azure worked to further tune the service to support the increasing OpenAI workload. The changes we made were not limited to OpenAI, hence all our Azure Database for PostgreSQL customers with demanding workloads have benefited. A few of the enhancements and the work that led to these are listed below. Changing the network congestion protocol to reduce replication lag Azure Database for PostgreSQL used the default CUBIC congestion control algorithm for replication traffic to replicas both within and outside the region. Leading up to one of the OpenAI launch events, we observed that several geo-distributed read replicas occasionally experienced replication lag. Replication from the primary server to the read replicas would typically operate without issues; however, at times, the replicas would unexpectedly begin falling behind the primary for reasons that were not immediately clear. This lag would not recover on its own and would grow to a point when, eventually, automation would restart the read replica. Once restarted, the read replica would once again catch up, only to repeat this cycle again within a day or less. After an extensive debugging effort, we traced the root cause to how the TCP congestion control algorithm handled a higher rate of packet drops. These drops were largely a result of high point-to-point traffic between the primary server and its replicas, compounded by the existing TCP window settings. Packet drops across regions are not unexpected; however, the default congestion control algorithm (CUBIC) treats packet loss as a sign of congestion and does an aggressive backoff. In comparison, the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm is less sensitive to packet drops. Switching to BBR, adding SKU specific TCP window settings, and switching to fair queuing network discipline (which can control pacing of outgoing packets at hardware level) resolved this issue. We’ll also note that one of our seasoned PostgreSQL committers provided invaluable insights during this process, helping us pinpoint the issue more effectively. Scaling out with Read replicas PostgreSQL primaries, if configured properly, work amazingly well in supporting a large number of read replicas. In fact, as noted in the OpenAI engineering blog, a single primary has been able to power around 50+ replicas across multiple regions. However, going beyond this increases the chance of impacting the primary. For this reason, we added the cascading replica support to scale out reads even further. But this brings in a number of additional failure modes that need to be handled. The system must carefully orchestrate repairs around lagging and failing intermediary nodes, safely repointing replicas to new intermediary nodes while performing catch up or rewind in a mission critical setup. Furthermore, disaster recovery (DR) scenarios can require a fast rebuild of a replica and as data movement across regions is a costly and time-consuming operation, we developed the ability to create a geo replica from a snapshot of another replica in the same region. This feature avoids the traditional full data copy process, which may take hours or even days depending on the size of the data, by leveraging data for that cluster that already exists in that region. This feature will soon be available for all our customers as well. Scaling out Writes These improvements solved the read replica lag problems and read scale but did not help address the growing write scale for OpenAI. At some point, the balance tipped and it was obvious that the IOPs limits of a single PostgreSQL primary instance will not cut it anymore. As a result OpenAI decided to move new and shardable workloads to Azure Azure Cosmos DB, which is our default recommended NoSQL store for fully elastic workloads. However, some workloads, as noted in the OpenAI blog are much harder to shard. While OpenAI is using Azure Database for PostgreSQL flexible server, several of the write scaling requirements that came up have been baked into our new Azure HorizonDB offering, which entered private preview in November 2025. Some of the architectural innovations are described in the following sections. Azure HorizonDB scalability design To better support more demanding workloads, Azure HorizonDB introduces a new storage layer for Postgres that delivers significant performance and reliability enhancements: More efficient read scale out. Postgres read replicas no longer need to maintain their own copy of the data. They can read pages from the single copy maintained by the storage layer. Lower latency Write-Ahead Logging (WAL) writes and higher throughput page reads via two purpose-built storage services designed for WAL storage and Page storage. Durability and high availability responsibilities are shifted from the Postgres primary to the storage layer, allowing Postgres to dedicate more resources to executing transactions and queries. Postgres failovers are faster and more reliable. To understand how Azure HorizonDB delivers these capabilities, let’s look at its high‑level architecture as shown in Figure 1. It follows a log-centric storage model, where the PostgreSQL writeahead log (WAL) is the sole mechanism used to durably persist changes to storage. PostgreSQL compute nodes never write data pages to storage directly in Azure HorizonDB. Instead, pages and other on-disk structures are treated as derived state and are reconstructed and updated from WAL records by the data storage fleet. Azure HorizonDB storage uses two separate storage services for WAL and data pages. This separation allows each to be designed and optimized for the very different patterns of reads and writes PostgreSQL does against WAL files in contrast to data pages. The WAL server is optimized for very low latency writes to the tail of a sequential WAL stream and the Page server is designed for random reads and writes across potentially many terabytes of pages. These two separate services work together to enable Postgres to handle IO intensive OLTP workloads like OpenAI’s. The WAL server can durably write a transaction across 3 availability zones using a single network hop. The typical PostgreSQL replication setup with a hot standby (Figure 2) requires 4 hops to do the same work. Each hop is a component that can potentially fail or slow down and delay a commit. Azure HorizonDB page service can scale out page reads to many hundreds of thousands of IOPs for each Postgres instance. It does this by sharding the data in Postgres data files across a fleet of page servers. This spreads the reads across many high performance NVMe disks on each page server. 2 - WAL Writes in HorizonDB Another key design principle for Azure HorizonDB was to move durability and high availability related work off PostgreSQL compute allowing it to operate as a stateless compute engine for queries and transactions. This approach gives Postgres more CPU, disk and network to run your application’s business logic. Table 1 summarizes the different tasks that community PostgreSQL has to do, which Azure HorizonDB moves to its storage layer. Work like dirty page writing and checkpointing are no longer done by a Postgres primary. The work for sending WAL files to read replicas is also moved off the primary and into the storage layer – having many read replicas puts no load on the Postgres primary in Azure HorizonDB. Backups are handled by Azure Storage via snapshots, Postgres isn’t involved. Task Resource Savings Postgres Process Moved WAL sending to Postgres replicas Disk IO, Network IO Walsender WAL archiving to blob storage Disk IO, Network IO Archiver WAL filtering CPU, Network IO Shared Storage Specific (*) Dirty Page Writing Disk IO background writer Checkpointing Disk IO checkpointer PostgreSQL WAL recovery Disk IO, CPU startup recovering PostgreSQL read replica redo Disk IO, CPU startup recovering PostgreSQL read replica shared storage Disk IO background, checkpointer Backups Disk IO pg_dump, pg_basebackup, pg_backup_start, pg_backup_stop Full page writes Disk IO Backends doing WAL writing Hot standby feedback Vacuum accuracy walreceiver Table 1 - Summary of work that the Azure HorizonDB storage layer takes over from PostgreSQL The shared storage architecture of Azure HorizonDB is the fundamental building block for delivering exceptional read scalability and elasticity which are critical for many workloads. Users can spin up read replicas instantly without requiring any data copies. Page Servers are able to scale and serve requests from all replicas without any additional storage costs. Since WAL replication is entirely handled by the storage service, the primary’s performance is not impacted as the number of replicas changes. Each read replica can scale independently to serve different workloads, allowing for workload isolation. Finally, this architecture allows Azure HorizonDB to substantially improve the overall experience around high availability (HA). HA replicas can now be added without any data copying or storage costs. Since the data is shared between the replicas and continuously updated by Page Servers, secondary replicas only replay a portion of the WAL and can easily keep up with the primary, reducing failover times. The shared storage also guarantees that there is a single source of truth and the old primary never diverges after a failover. This prevents the need for expensive reconciliation, using pg_rewind, or other techniques and further improves availability. Azure HorizonDB was designed from the ground up with learnings from large scale customers, to meet the requirements of the most demanding workloads. The improved performance, scalability and availability of the Azure HorizonDB architecture make Azure a great destination for Postgres workloads.From Oracle to Azure: How Quadrant Technologies accelerates migrations
This blog was authored by Manikyam Thukkapuram, Director, Alliances & Engineering at Quadrant Technologies; and Thiwagar Bhalaji, Migration Engineer and DevOps Architect at Quadrant Technologies Over the past 20+ years, Quadrant Technologies has accelerated database modernization for hundreds of organizations. As momentum to the cloud continues to grow, a major focus for our business has been migrating on-premises Oracle databases to Azure. We’ve found that landing customers in Azure Database for PostgreSQL has been the best option both in terms of cost savings and efficiency. Azure Migrate is by far the best way to get them there. With Azure Migrate, we’re able to streamline migrations that traditionally took months, into weeks. As a Microsoft solutions partner, we help customers migrate to Azure and develop Azure-based solutions. We’re known as “the great modernization specialists” because many of our customers come to us with complex legacy footprints, outdated infrastructure, and monolithic applications that can be challenging to move to the cloud. But we excel at untangling these complex environments. And with our Q-Migrator tool, which is a wrapper around Azure Migrate, we’re able to automate and accelerate these kinds of migrations. Manual steps slowed down timelines In general, each migration we lead includes a discovery phase, a compatibility assessment, and the migration execution. In discovery, we identify every server, database, and application in a customer’s environment and map their interactions. Next, we assess each asset’s readiness for Azure and plan for optimal cloud configurations. Finally, we bring the plan to life, integrating applications, moving workloads, and validating performance. Before adopting Azure Migrate, each of these phases involved manual tasks for our team. During our discovery process we manually collected inventory and wrote custom scripts to track server relationships and database dependencies. Our engineers also had to dig through configuration files and use third-party assessment tools for aspects like VM utilization and Oracle schema. When we mapped compatibility, we worked from static data to predict cost estimates and sizing, as opposed to operating from real-time telemetry. By the time we reached the migration phase, fragmented tooling and inconsistent assessments made it difficult to maintain accuracy and efficiency. Hidden dependencies sometimes surfaced late in the process, causing unexpected rework and delays. Streamlining migrations with Azure Migrate To automate and streamline these manual tasks, we developed Q-Migrator, which is our in-house framework built around Azure Migrate. Now we can offer clients an efficient, agentless approach to discovery, assessment, and migration. As part of our on-premises database migration initiatives, we rely on Azure Migrate to seamlessly migrate a wide range of structured databases (including MySQL, Microsoft SQL Server, PostgreSQL, and Oracle) from on-premises environments to Azure IaaS and PaaS. For instance, for an on-premises PostgreSQL migration, we begin by setting up an Azure Migrate appliance in the client’s environment to automatically discover servers, databases, and applications. That generates a complete inventory and dependency map that identifies every relationship between servers and databases. From there, we run an assessment through Azure Migrate to check compatibility, identify blockers, and right-size target environments for Azure Database for PostgreSQL. By integrating Azure Database Migration Service (DMS), we can replicate data continuously until cutover, ensuring near-zero downtime. In addition, Azure DMS provides robust telemetry and analytics for deep visibility into every stage of the process. This unified and automated workflow not only replaces manual steps but also increases reliability and accelerates delivery. Teams benefit from a consolidated dashboard for planning, execution, and performance tracking, driving efficiency throughout the migration lifecycle. 75% faster deployment, 60% cost savings Since implementing Azure Migrate, which now facilitates discovery and assessment for on-premises PostgreSQL workloads, we’ve accelerated deployment by 75% compared to traditional migration methods. We’ve also reduced costs for our clients by up to 60 percent. Automated discovery alone reduces that phase by nearly 40%, and dependency mapping now takes a fraction of the effort. With the integrated dashboard in Azure Migrate we can also track progress across discovery, assessment, and migration in one place. This eliminates the need for multiple third-party tools. These efficiencies allow us to deliver complex migrations on tighter timelines without sacrificing quality or reliability. Rounding out the modernization journey with AKS As “the great modernization specialists,” we’re often asked which is the best database for landing Oracle workloads in the cloud. From our experience, Azure Database for PostgreSQL is ideal for enterprises seeking cost-efficient and secure PostgreSQL deployments. Its managed services reduce operational overhead while maintaining high availability, compliance, and scalability. Plus, seamless integration with Azure AI services allows us to innovate for clients and keep them ahead of the curve. We also recognize that database migration is only the first step for many clients—modernizing the application layer delivers even greater scalability, security, and manageability. When clients come to Quadrant for a broader modernization strategy, we often use Azure Kubernetes Service (AKS) to containerize their applications and break monoliths into microservices. AKS delivers a cloud-native architecture alongside database modernization. This integration supports DevOps practices, simplifies deployments, and allows customers to take full advantage of elastic cloud infrastructure. More innovation to come Overall, Azure Migrate and Azure Database for PostgreSQL, Azure Database for MySQL, and Azure SQL Database have redefined how we deliver database modernization, and our close collaboration with Microsoft has made it possible. By engaging early with Microsoft, we can validate migration architectures and gain insights into best practices for high-performance and secure cloud deployments. Access to Microsoft experts helps us fine-tune our designs, optimize performance, and resolve complex issues quickly. We’re also investing in AI-driven automation using Azure OpenAI in Foundry Models to analyze migration data, optimize queries, and predict performance outcomes. These innovations allow us to deliver more intelligent, adaptive solutions tailored to each customer’s unique environment.SubgenAI makes AI practical, scalable, and sustainable with Azure Database for PostgreSQL
Authors: Abe Omorogbe, Senior Program Manager at Microsoft and Julia Schröder Langhaeuser, VP of Product Serenity Star at SubgenAI AI agents are thriving in pilots and prototypes. However, scaling them across organizations is more difficult. A recent MIT report shows that 95 percent of projects fail to reach production. Long development cycles, lack of observability, and compliance hurdles leave enterprises struggling to deliver production-ready agents. SubgenAI, a European generative AI company that focuses on democratizing AI for businesses and governments, saw an opportunity to change this. Its flagship platform, Serenity Star, transforms AI agent development from a code-heavy, fragmented process into a streamlined, no-code experience. Built on Microsoft Azure Database for PostgreSQL, Semantic Kernel, and Microsoft Foundry, Serenity Star empowers organizations to deploy production-grade AI agents in minutes, not months. SubgenAI’s mission is to make generative AI accessible, scalable, and secure for every organization. Whether you're a startup or a multinational, Serenity Star offers the tools to build intelligent agents tailored to your business logic, with full control over data and deployment. “Many things must happen around it in the coming years. Serenity Star is designed to solve problems like data control, compliance, and decision ethics—so companies can unleash the full potential of generative AI without compromising trust or profitability” - Lorenzo Serratosa Simplifying complex AI agent development Technical and operational challenges are inherent in enterprise-wide AI agent deployments. Examples include time-consuming iteration cycles, lack of observability and cost control, security concerns, and data sovereignty requirements. Serenity Star addresses these pain points by handling the entire AI agent lifecycle while providing enterprise-grade security and compliance features. Users can focus on defining their agent's purpose and behavior rather than wrestling with technical implementation details. Its framework focuses on four essentials for AI agents: the brain (underlying model), knowledge (accessible information), behavior (programmed responses), and tools (external system integrations). This framework directly influenced the technology stack choices for Serenity Star, with Azure Database for PostgreSQL powering the knowledge retrieval and Semantic Kernel enabling flexible model orchestration. Real-world architecture in action When a user query comes in, Serenity Star uses the vector capabilities of Azure Database for PostgreSQL to retrieve the most relevant knowledge. That context, combined with the user’s input, forms a complete prompt. Semantic Kernel then routes the request to the right large language model, ensuring the agent delivers accurate and context-aware responses. Serenity Star’s native connectors to platforms such as Microsoft Teams, WhatsApp, and Google Tag Manager are also part of this architecture, delivering answers directly in the collaboration and communication tools enterprises already use every day. Figure 1: Serenity Star Architecture This routing and orchestration architecture applies to the multi-tenant SaaS deployments and dedicated customer instances offered by Serenity Star. Azure Database for PostgreSQL provides native Row-Level Security (RLS) capabilities, a key advantage for securely managing multi-tenant environments. Multi-tenant deployments allow organizations to get started quickly with lower overhead, while dedicated instances meet the needs of enterprises with strict compliance and data sovereignty requirements. Optimizing for scale The same architecture that powers retrieval, routing, and multi-channel delivery also provides a foundation for performance at scale. As adoption grows, the team continuously monitors query volume, response times, and resource efficiency across both multi-tenant and dedicated environments. To stay ahead of demand, SubgenAI actively experiments with new Azure Database for PostgreSQL features such as DiskANN for faster vector search. These optimizations keep latency low even as more users and connectors are added. The result is a platform that maintains sub-60-second response times for 99 percent of chart generations, regardless of deployment model or integration point. With this systematic approach to scaling, organizations can deploy fully functional AI agents that are connected to their preferred communication platforms in just 15 minutes instead of hours. For enterprises that have struggled with failed AI projects, Serenity Star offers not only a secure and compliant solution but also one proven to grow with their needs. Why Azure Database for PostgreSQL is a cornerstone The knowledge component of AI agents relies heavily on retrieval-augmented generation (RAG) systems that perform similarity searches against embedded content. This requires a database capable of handling efficient vector search while maintaining enterprise-grade reliability and security. SubgenAI evaluated multiple vector database options. However, Azure Database for PostgreSQL with PGVector emerged as the clear winner. There were several compelling reasons for this. One is its mature technology, which provides immediate credibility with enterprise customers. Two, the ability to scale GenAI use cases with features like DiskANN for accurate and scalable vector search. There, the flexibility and appeal of using an open-source database with a vibrant and fast-moving community. As CPO Leandro Harillo explains: “When we tell them their data runs on Azure Database for PostgreSQL, it’s a relief. It's a well-known technology versus other options that were born with this new AI revolution.” As an open-source relational database management system, Azure Database for PostgreSQL offers extensibility and seamless integration with Microsoft’s enterprise ecosystem. It has a trusted reputation that appeals to organizations with strict data sovereignty and compliance requirements such as those in healthcare and insurance where reliability and governance are non-negotiable. The integration with Azure's broader ecosystem also simplified implementation. With Serenity Star built entirely on Azure infrastructure, Azure Database for PostgreSQL provided seamless connectivity and consistent performance characteristics. The fast response times necessary for real-time agent interactions are the result, along with maintaining the reliability demanded by enterprise customers. Semantic Kernel: Enabling model flexibility at scale Enterprise AI success requires the ability to experiment with different models and adapt quickly as technology evolves. Semantic Kernel makes this possible, supporting over 300 LLMs and embedding models through a unified interface. With Serenity Star, organizations can make genuine choices about their AI implementations without vendor lock-in. Companies can use embedding models from OpenAI through Azure deployments, ensuring their information remains in their own infrastructure while accessing cutting-edge capabilities. If business requirements change or new models emerge, switching becomes a configuration change rather than a development project. Semantic Kernel's comprehensive connector ecosystem also accelerated SubgenAI's own development process. Interfaces for different vector databases enabled rapid prototyping and comparison during the evaluation phase. “Semantic Kernel helped us to be able to try the different ones and choose the one that fit better for us,” notes Julia Schroder, VP of Product. The SubgenAI team has also extended Semantic Kernel to support more features in Azure Database for PostgreSQL, which is easier because of how well-known and popular PostgreSQL is. SubgenAI has also contributed improvements back to the community. This collaborative approach ensures the platform benefits from the latest developments while helping advance the broader ecosystem. Proven impact of Azure Database for PostgreSQL across industries Because organizations struggle to deliver production-ready agents because of long development cycles, lack of observability, and compliance, the effectiveness of Azure Database for PostgreSQL and other Azure services is reflected in deployment metrics and customer feedback. Production-ready agents typically require around 30 iterations for basic implementations. Complex use cases demand significantly more refinement. One GenAI customer in medical education required over 200 iterations to perfect an agent that evaluates medical students through complex case analysis. Azure PostgreSQL and other Azure services support hour-long iteration cycles rather than week-long sprints, which made this level of refinement economically feasible. Cost efficiency is another significant advantage. SubgenAI provisions and configures models in Microsoft Foundry, which eliminates idling GPU resources while providing detailed cost breakdowns. Users can see exactly how tokens are consumed across prompt text, RAG context, and tool usage, enabling data-driven optimization decisions. Consulting partnerships validate the platform's market position. One consulting firm with 50,000 employees is delighted with the easier implementation, faster deployment, and reliable production performance. Conclusion The combination of Azure Database for PostgreSQL and Semantic Kernel has enabled SubgenAI to address the fundamental challenges that cause 95 percent of enterprise AI projects to fail. Organizations using Serenity Star bypass the traditional barriers of lengthy development cycles, limited observability, and compliance hurdles that typically derail AI initiatives. The platform's architecture delivers measurable results, including a 50 percent reduction in coding time, support for complex agents requiring 200+ iterations, and deployment capabilities that compress months-long projects into 15-minute implementations. Azure Database for PostgreSQL provides the enterprise-grade foundation that customers in regulated industries require, while Semantic Kernel ensures organizations retain flexibility as AI technology evolves. This technological partnership creates a reliable pathway for companies to deploy production-ready AI agents without sacrificing data sovereignty or operational control. Through the reliability of Azure Database for PostgreSQL and the flexibility of Semantic Kernel, Serenity Star delivers an enterprise-ready foundation that makes AI practical, scalable, and sustainable.
