Nasdaq builds thoughtfully designed AI for board governance with PostgreSQL on Azure
Authored by: Charles Federssen, Partner Director of Product Management for PostgreSQL at Microsoft and Mohsin Shafqat, Senior Manager, Software Engineering at Nasdaq When people think of Nasdaq, they usually think of markets, trading floors, and financial data moving at extraordinary speed. But behind the scenes, Nasdaq also plays an equally critical role in how boards of directors govern, deliberate, and make decisions. Nasdaq Boardvantage® is the company’s governance platform, used by more than 4,400 organizations worldwide—including nearly half of the Fortune 100. It’s where directors review board books, collaborate in an environment designed with robust security, and prepare for meetings that often involve some of the most sensitive information a company has. In recent years, Nasdaq set out to modernize Nasdaq Boardvantage with AI, without compromising security and reliability. That journey was featured in a Microsoft Ignite session, “Nasdaq Boardvantage: AI-Driven Governance on PostgreSQL and Foundry.” It offers a practical look at how Azure Database for PostgreSQL can support AI-driven applications where precision, isolation, and data control are non-negotiable. Introducing AI where trust is everything Board governance isn’t a typical productivity workload. Board packets can run 400 to 600 pages, meeting minutes are legal records, and any AI-generated insight must be confined to a customer’s own data. “Our customers trust us with some of their most strategic, sensitive data,” said Mohsin Shafqat, Senior Manager of Software Development at Nasdaq. That trust meant tackling several core challenges upfront, including: How do you minimize AI hallucinations in a governance context? How do you guarantee tenant isolation at scale? How do you keep data regional across a global customer base? A cloud foundation built for governance Before adding intelligence, Nasdaq decided to re-architect Nasdaq Boardvantage on Microsoft Azure, using Azure Kubernetes Service (AKS) to run containerized, multi-tenant workloads with strong isolation boundaries. Microsoft Foundry provides the managed foundation for deploying, governing, and operating AI models across this architecture, adding consistency, security, and control as intelligence is introduced. At the data layer, Azure Database for PostgreSQL and Azure Database for MySQL became the backbone for governance data. PostgreSQL, in particular, plays a central role in managing structured governance information alongside vector embeddings that support AI-driven features. Together, these services give Nasdaq the performance, security, and operational control required for a highly regulated, multi-tenant environment, while still moving quickly. Key architectural choices included: Tenant isolation by design, with separate databases and storage Regional deployments to align with data residency requirements High availability and managed operations, so teams could focus on product innovation instead of infrastructure maintenance PostgreSQL and pgvector: Powering context-aware AI With that foundation in place, Nasdaq was ready to carefully introduce AI. One of the first AI capabilities was intelligent document summarization. Board materials that once took hours to review could now be condensed into concise, contextually accurate summaries. Under the hood, this required more than just calling an LLM. Nasdaq uses pgvector, natively supported in Azure Database for PostgreSQL, to store and query embeddings generated from board documents. This allows the platform to perform hybrid searches that combine traditional SQL queries with vector similarity to retrieve the most relevant context before sending anything to a language model. Instead of treating AI as a black box, the team built a pipeline where: Documents are processed with Azure Document Intelligence to preserve structure and meaning Content is chunked and embedded Embeddings are stored in PostgreSQL with pgvector Vector similarity searches retrieve precise context for each AI task Because this runs inside PostgreSQL, the same database benefits from Azure’s built-in high availability, security controls, and operational tooling – delivering tangible results, including a 25% reduction in overall board preparation time and internal testing shows 91–97% accuracy for AI-generated summaries and meeting minutes. From summaries to an AI Board Assistant With summarization working in production, Nasdaq expanded further. The team is now building an AI-powered Board Assistant that will help directors prepare for upcoming meetings by surfacing trends, risks, and insights from prior discussions. This introduces a new level of scale. Years of board data across thousands of customers translate into millions of embeddings. PostgreSQL continues to anchor this architecture, storing vectors for semantic retrieval while MySQL supports complementary non-vector workloads. Across Nasdaq Boardvantage, users are advised to always review AI outputs, and no customer data is shared or used to train external models. “We designed AI for governance, not the other way around,” Shafqat said. More importantly, customers trust the system because security, isolation, and data control were engineered in from day one. Looking ahead Nasdaq’s work shows how Azure Database for PostgreSQL can support AI workloads that demand both intelligence and integrity. With PostgreSQL at the core, Nasdaq has built a governance platform that scales globally, respects regulatory boundaries, and introduces AI in a way that feels dependable and not experimental. What started as a modernization of Nasdaq Boardvantage is now influencing how Nasdaq approaches AI across the enterprise. To dive deeper into the architecture and hear directly from the engineers behind it, watch the Ignite session and check out these resources: Watch the Ignite breakout session for a technical walkthrough of how Nasdaq Boardvantage is built, including PostgreSQL on Azure, pgvector, and Microsoft Foundry in production. Read the case study to see how Nasdaq introduced AI into board governance and what changed for directors, administrators, and decision-making. Watch the Ignite broadcast for a candid discussion on Azure Database for PostgreSQL, Azure HorizonDB, and what it takes to scale AI-driven governance.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.AlphaLife Sciences powers regulatory-compliant AI workflows with PostgreSQL on Azure
by: Maxim Lukiyanov, PhD, Principal PM Manager and Sharon Chen, CEO and Founder at AlphaLife Sciences In life sciences, every document is deeply interconnected and highly regulated. Each clinical trial, regulatory submission, safety report, or protocol amendment is expected to stand up to rigorous audit. For AlphaLife Sciences, that challenge became an opportunity to rethink how AI could support expert human judgment. At Microsoft Ignite, AlphaLife Sciences CEO and Founder Sharon Chen shared how her team is building an AI-powered content authoring platform on top of Azure Database for PostgreSQL, designed specifically for the demands of regulated life sciences workflows. She also explained why the team is excited about Azure HorizonDB as a new PostgreSQL service that is built to meet the needs of modern enterprise workloads. This post explores how AlphaLife Sciences uses PostgreSQL as more than a data store. It’s a semantic foundation for compliant, auditable AI agents. Bringing AI into regulated workflows Life sciences organizations are under constant pressure. R&D pipelines are growing and patent windows are shrinking. A single clinical study report can take six months or more to complete, involving multiple teams and hundreds of source documents. Building efficiency into these processes is critical, but only if it doesn’t compromise accuracy, traceability, or compliance. That’s where many AI solutions fall short. Generating text is one thing, but generating verifiable, version-controlled, regulation-aware content is another. AlphaLife Sciences needed agents that could: Work across massive volumes of structured and unstructured data (Word, PDF, Excel, PowerPoint) Maintain full traceability from generated content back to source documents Support audits, amendments, and regulatory review Minimize hallucinations in a zero-tolerance environment Integrate naturally into the tools writers already use Bringing data, search, and AI together in one system At the core of AlphaLife Sciences’ platform is Azure Database for PostgreSQL. The team chose it for flexibility, extensibility, and for how well it supports modern AI workloads. Instead of stitching together separate systems for SQL queries, vector search, text indexing, and metadata tracking, AlphaLife Sciences consolidated everything into PostgreSQL. One of its flagship use cases is clinical trial protocol authoring, a process that typically involves: Designing trial objectives and endpoints Pulling references from previous studies Writing and revising hundreds of pages of structured content Managing multiple rounds of amendments and regulatory feedback With AI agents backed by PostgreSQL, that workflow changes dramatically. When a writer generates a protocol section, the system can automatically retrieve relevant references from a centralized document pool, using semantic search rather than manual lookup. Writers select the sources they want, apply rules or prompts, and let AI draft the section - complete with citations tied back to the original documents. Reviewers can inspect the source, adjust the output, or insert it directly into the document. For protocol amendments, the platform allows teams to upload inputs (Word or Excel), analyze which sections are affected, and generate structured suggestions. Changes are clearly highlighted, compared against previous versions, and summarized in amendment tables. AI agents that respect the rules A recurring theme in Chen’s talk was restraint. “We don’t just need AI that can write,” she said. “We need intelligent agents that understand data structures, follow regulatory laws, and manage version control.” This is where PostgreSQL-backed AI agents shine. By grounding AI behavior in structured schemas, controlled access, and auditable records, automation works hand-in-hand with human experts. AI accelerates first drafts, consistency checks, discrepancy detection, and cross-document analysis, but final accountability stays firmly with professionals. In some cases, the time to complete processes has been reduced by more than 50%. Azure Database for PostgreSQL has become more than a database for AlphaLife Sciences. It’s a semantic knowledge base that supports: Structured and unstructured data Vector similarity search Metadata-driven traceability Compliance, security, and auditability AI agents operating safely inside enterprise constraints By grounding AI agents directly in the database, reasoning, retrieval, and generation all operate against the same governed source of truth. “AI agents are not here to replace human beings,” said Chen. “They extend structured, compliant, and auditable thinking.” What’s next for AlphaLife Sciences with PostgreSQL on Azure Looking ahead, Chen shared her excitement about Azure HorizonDB and the capabilities it brings to PostgreSQL on Azure. Features like in-database AI model management, semantic operators for classification and summarization, and faster vector search with DiskANN align closely with AlphaLife Sciences’ needs as their platform continues to scale. “We’re extremely happy to see the launch of Azure HorizonDB and the more powerful tools coming with it,” Chen said. “By putting everything together in PostgreSQL, we don’t have to rely on different systems for vector search, text indexing, or SQL queries. Everything happens in one streamlined system. The code becomes cleaner, efficiency improves, and the AI agents perform much more elegantly.” Learn more AlphaLife Sciences’ journey was featured during the Microsoft Ignite session “The Blueprint for Intelligent AI Agents Backed by PostgreSQL.” Watch the session to learn more and see a demo of how Azure Database for PostgreSQL transforms the protocol and protocol amendment process. When AI is anchored in a strong PostgreSQL foundation, innovation and compliance don’t have to compete - they can reinforce each other.January 2026 Recap: Azure Database for PostgreSQL
We just dropped the 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟲 𝗿𝗲𝗰𝗮𝗽 for Azure Database for PostgreSQL and this one’s all about developer velocity, resiliency, and production-ready upgrades. January 2026 Recap: Azure Database for PostgreSQL • PostgreSQL 18 support via Terraform (create + upgrade) • Premium SSD v2 (Preview) with HA, replicas, Geo-DR & MVU • Latest PostgreSQL minor version releases • Ansible module GA with latest REST API features • Zone-redundant HA now configurable via Azure CLI • SDKs GA (Go, Java, JS, .NET, Python) on stable APIs Read the full January 2026 recap here and see what’s new (and what’s coming) - January 2026 Recap: Azure Database for PostgreSQLSupporting 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.
Azure PostgreSQL Lesson Learned #14: Hitting the Max Storage Limits Blocking Further Scale‑Up
Co‑authored with HaiderZ-MSFT Case Overview A customer attempted to increase storage for their Azure Database for PostgreSQL Flexible Server but was unable to exceed 32 TiB. Investigation confirmed that the server was deployed in a region where the maximum supported storage is capped at 32 TiB, meaning no additional scale‑up was possible. This limitation required exploring alternative storage options and potential redeployment strategies to support growing workload demands. Symptoms: How the Problem Appears Storage size maxes out at 32 TiB Portal does not allow selecting any higher value This behavior is expected because the platform enforces the maximum storage supported by the region and tier. Root Cause: Regional Storage Limit of 32 TiB Reached Azure Database for PostgreSQL Flexible Server supports up to 32 TiB of storage on Premium SSD within the customer’s region. Once this limit is reached: Further storage growth is not possible Storage cannot be downgraded or changed in-place The customer must migrate to a tier that supports larger disks (e.g., Premium SSD v2, where available) Premium SSD v2 supports: Up to 64 TiB 1 GiB granular sizing Higher throughput and IOPS However, its availability and supported capabilities can vary by region. Step‑By‑Step Troubleshooting & Migration Guidance STEP 1 — Validate Current Storage Azure Portal → Server → Compute + Storage Confirms storage is already at the maximum the region can offer STEP 2 — Confirm Tier Limitations Check documentation for storage caps by tier and region from Storage options | Microsoft Learn STEP 3 — Attempt a PITR‑Based Redeployment Migration from Premium SSD → Premium SSD v2 is possible via following these steps: https://learn.microsoft.com/en-us/azure/postgresql/compute-storage/concepts-storage-migrate-ssd-to-ssd-v2?tabs=portal-restore-custom-point ⚠ Important: The PITR wizard always deploys the restored server in the same region as the source server. There is no option to change regions during PITR. Therefore, customers must check if Premium SSD v2 becomes selectable during PITR within that same region. If SSD v2 is not listed in the dropdown, it means: The region does not support SSD v2 for PostgreSQL Flexible Server The customer cannot exceed 32 TiB on Flexible Server in that region Final Outcome The server reached the maximum supported storage (32 TiB) for its region and storage could not be increased further. To exceed this limit, the customer needs to move to Premium SSD v2 (if supported) Migration must be done via following these steps: https://learn.microsoft.com/en-us/azure/postgresql/compute-storage/concepts-storage-migrate-ssd-to-ssd-v2?tabs=portal-restore-custom-point Tip: Alternative Path — Use Dump & Restore to a New Server With Larger Storage If Premium SSD v2 does not appear as an available storage option during the Migration from Premium SSD → Premium SSD v2 workflow, our customer still has another viable path to exceed the 32 TiB limit: ➡ Option: Perform a pg_dump / pg_restore to a New Server with Higher Storage Capacity Data can be migrated by creating a brand‑new Flexible Server in a region that supports higher storage tiers (including Premium SSD v2), then migrating the data using standard PostgreSQL backup tools Best Practices Add proactive storage alerts (70%, 80%, 90%). Validate regional storage limits before provisioning servers. Architect for growth by selecting a region/tier that aligns with future capacity needs. Request Premium SSD v2 quota increases early when planning large workloads. Helpful References Premium SSD v2 for Azure PostgreSQL Flexible Server https://learn.microsoft.com/azure/postgresql/compute-storage/concepts-storage-premium-ssd-v2 How to Migrate from Premium SSD → Premium SSD v2 (PITR) https://learn.microsoft.com/en-us/azure/postgresql/compute-storage/concepts-storage-migrate-ssd-to-ssd-v2?tabs=portal-restore-custom-point
