Azure Databricks Cost Optimization: A Practical Guide
Co-Authored by Sanjeev Nair This guide walks through a proven approach to Databricks cost optimization, structured in three phases: Discovery, Cluster/Data/Code Best Practices, and Team Alignment & Next Steps. Phase 1: Discovery Assessing Your Current State The following questions are designed to guide your initial assessment and help you identify areas for improvement. Documenting answers to each will provide a baseline for optimization and inform the next phases of your cost management strategy. Environment & Organization Cluster Management Cost Optimization Data Management Performance Monitoring Future Planning What is the current scale of your Databricks environment? How many workspaces do you have? How are your workspaces organized (e.g., by environment type, region, use case)? How many clusters are deployed? How many users are active? What are the primary use cases for Databricks in your organization? Data engineering Data science Machine learning Business intelligence How are clusters currently managed? Manual configuration Automated scripts Databricks REST API Cluster policies What is the average cluster uptime? Hours per day Days per week What is the average cluster utilization rate? CPU usage Memory usage What is the current monthly spend on Databricks? Total cost Breakdown by workspace Breakdown by cluster What cost management tools are currently in use? Azure Cost Management Third-party tools Are there any existing cost optimization strategies in place? Reserved instances Spot instances Cluster auto-scaling What is the current data storage strategy? Data lake Data warehouse Hybrid What is the average data ingestion rate? GB per day Number of files What is the average data processing time? ETL jobs Machine learning models What types of data formats are used in your environment? Delta Lake Parquet JSON CSV Other formats relevant to your workloads What performance monitoring tools are currently in use? Databricks Ganglia Azure Monitor Third-party tools What are the key performance metrics tracked? Job execution time Cluster performance Data processing speed Are there any planned expansions or changes to the Databricks environment? New use cases Increased data volume Additional users What are the long-term goals for Databricks cost optimization? Reducing overall spend Improving resource utilization & cost attribution Enhancing performance Understanding Databricks Cost Structure Total Cost = Cloud Cost + DBU Cost Cloud Cost: Compute (VMs, networking, IP addresses), storage (ADLS, MLflow artifacts), other services (firewalls), cluster type (serverless compute, classic compute) DBU Cost: Workload size, cluster/warehouse size, photon acceleration, compute runtime, workspace tier, SKU type (Jobs, Delta Live Tables, All Purpose Clusters, Serverless), model serving, queries per second, model execution time Diagnose Cost and Issues Effectively diagnosing cost and performance issues in Databricks requires a structured approach. Use the following steps and metrics to gain visibility into your environment and uncover actionable insights. Review Cluster Metrics CPU Utilization: Track guest, iowait, idle, irq, nice, softirq, steal, system, and user times to understand how compute resources are being used. Memory Utilization: Monitor used, free, buffer, and cached memory to identify over- or under-utilization. Key Question: Is your cluster over- or under-utilized? Are resources being wasted or stretched too thin? Review SQL Warehouse Metrics Live Statistics: Monitor warehouse status, running/queued queries, and current cluster count. Time Scale Filter: Analyze query and cluster activity over different time frames (8 hours, 24 hours, 7 days, 14 days). Peak Query Count Chart: Identify periods of high concurrency. Completed Query Count Chart: Track throughput and query success/failure rates. Running Clusters Chart: Observe cluster allocation and recycling events. Query History Table: Filter and analyze queries by user, duration, status, and statement type. 3. Review Spark UI Stages Tab: Look for skewed data, high input/output, and shuffle times. Uneven task durations may indicate data skew or inefficient data handling. Jobs Timeline: Identify long-running jobs or stages that consume excessive resources. Stage Analysis: Determine if stages are I/O bound or suffering from data skew/spill. Executor Metrics: Monitor memory usage, CPU utilization, and disk I/O. Frequent garbage collection or high memory usage may signal the need for better resource allocation. 3.1. Storage & Jobs Tab Storage Level: Check if data is stored in memory, on disk, or both. Size: Assess the size of cached data. Job Analysis: Investigate jobs that dominate the timeline or have unusually long durations. Look for gaps caused by complex execution plans, non-Spark code, driver overload, or cluster malfunction. 3.2. Executor Tab Storage Memory: Compare used vs. available memory. Task Time (Garbage Collection): Review long tasks and garbage collection times. Shuffle Read/Write: Measure data transferred between stages. Phase 2: Cluster/Code/Data Best Practices Alignment Cluster UI Configuration and Cost Attribution Effectively configuring clusters/workloads in Databricks is essential for balancing performance, scalability, and cost. Tunning settings and features when used strategically can help organizations maximize resource efficiency and minimize unnecessary spending. Key Configuration Strategies 1. Reduce Idle Time: Clusters to incur costs even when not actively processing workloads. To avoid paying for unused resources: Enable Auto-Terminate: Set clusters automatically shut down after a period of inactivity. This simple setting can significantly reduce wasted spending. Enable Autoscaling: Workloads fluctuate in size and complexity. Autoscaling allows clusters to dynamically adjust the number of nodes based on demand: Automatic Resource Adjustment: Scale up for heavy jobs and scale down for lighter loads, ensuring you only pay for what you use. Use Spot Instances: For batch processing and non-critical workloads, spot instances offer substantial cost savings: Lower VM Costs: Spot instances are typically much cheaper than standard VMs. However, they are not recommended for jobs requiring constant uptime due to potential interruptions. Leverage Photon Engine: Photon is Databricks’ high-performance, vectorized query engine: Accelerate Large Workloads: Photon can dramatically reduce runtime for compute-intensive tasks, improving both speed and cost efficiency. Keep Runtimes Up to Date: Using the latest Databricks runtime ensures optimal performance and security: Benefit from Improvements: Regular updates include performance enhancements, bug fixes, and new features. Apply Cluster Policies: Cluster policies help standardize configurations and enforce cost controls across teams: Governance and Consistency: Policies can restrict certain settings, enforce tagging, and ensure clusters are created with cost-effective defaults. Optimize Storage: type impacts both performance and cost: Switch from HDDs to SSDs: SSDs provide faster caching and shuffle operations, which can improve job efficiency and reduce runtime. Tag Clusters for Cost Attribution: Tagging clusters enables granular tracking and reporting: Visibility and Accountability: Use tags to attribute costs to specific teams, projects, or environments, supporting better budgeting and chargeback processes. Select the Right Cluster Type: Different workloads require different cluster types: Job Clusters: Ideal for scheduled jobs and Delta Live Tables. All-Purpose Clusters: Suited for ad-hoc analysis and collaborative work. Single-Node Clusters: Efficient for simple exploratory data analysis or pure Python tasks. Serverless Compute: Scalable, managed workloads with automatic resource management. Monitor and Adjust Regularly: review cluster metrics and query history: Continuous Optimization: Use built-in dashboards to monitor usage, identify bottlenecks, and adjust cluster size or configuration as needed. Code Best Practices Avoid Reprocessing Large Tables Use a CDC (Change Data Capture) architecture with Delta Live Tables (DLT) to process only new or changed data, minimizing unnecessary computation. Ensure Code Parallelizes Well Write Spark code that leverages parallel processing. Avoid loops, deeply nested structures, and inefficient user-defined functions (UDFs) that can hinder scalability. Reduce Memory Consumption Tweak Spark configurations to minimize memory overhead. Clean out legacy or unnecessary settings that may have carried over from previous Spark versions. Prefer SQL Over Complex Python Use SQL (declarative language) for Spark jobs whenever possible. SQL queries are typically more efficient and easier to optimize than complex Python logic. Modularize Notebooks Use %run to split large notebooks into smaller, reusable modules. This improves maintainability. Use LIMIT in Exploratory Queries When exploring data, always use the LIMIT clause to avoid scanning large datasets unnecessarily. Monitor Job Performance Regularly review Spark UI to detect inefficiencies such as high shuffle, input, or output. Optimize join strategies and data layout accordingly. Databricks Code Performance Enhancements & Data Engineering Best Practices By enabling the below features and applying best practices, you can significantly lower costs, accelerate job execution, and build Databricks pipelines that are both scalable and highly reliable. For more guidance review: Comprehensive Guide to Optimize Data Workloads | Databricks. Feature / Technique Purpose / Benefit How to Use / Enable / Key Notes Disk Caching Accelerates repeated reads of Parquet files Set spark.databricks.io.cache.enabled = true Dynamic File Pruning (DFP) Skips irrelevant data files during queries, improves query performance Enabled by default in Databricks Low Shuffle Merge Reduces data rewriting during MERGE operations, less need to recalculate ZORDER Use Databricks runtime with feature enabled Adaptive Query Execution (AQE) Dynamically optimizes query plans based on runtime statistics Available in Spark 3.0+, enabled by default Deletion Vectors Efficient row removal/change without rewriting entire Parquet file Enable in workspace settings, use with Delta Lake Materialized Views Faster BI queries, reduced compute for frequently accessed data Create in Databricks SQL Optimize Compacts Delta Lake files, improves query performance Run regularly, combine with ZORDER on high-cardinality columns ZORDER Physically sorts/co-locates data by chosen columns for faster queries Use with OPTIMIZE, select columns frequently used in filters/joins Auto Optimize Automatically compacts small files during writes Enable optimizeWrite and autoCompact table properties Liquid Clustering Simplifies data layout, replaces partitioning/ZORDER, flexible clustering keys Recommended for new Delta tables, enables easy redefinition of clustering keys File Size Tuning Achieve optimal file size for performance and cost Set delta.targetFileSize table property Broadcast Hash Join Optimizes joins by broadcasting smaller tables Adjust spark.sql.autoBroadcastJoinThreshold and spark.databricks.adaptive.autoBroadcastJoinThreshold Shuffle Hash Join Faster join alternative to sort-merge join Prefer over sort-merge join when broadcasting isn’t possible, Photon engine can help Cost-Based Optimizer (CBO) Improves query plans for complex joins Enabled by default, collect column/table statistics with ANALYZE TABLE Data Spilling & Skew Handles uneven data distribution and excessive shuffle Use AQE, set spark.sql.shuffle.partitions=auto, optimize partitioning Data Explosion Management Controls partition sizes after transformations (e.g., explode, join) Adjust spark.sql.files.maxPartitionBytes, use repartition() after reads Delta Merge Efficient upserts and CDC (Change Data Capture) Use MERGE operation in Delta Lake, combine with CDC architecture Data Purging (Vacuum) Removes stale data files, maintains storage efficiency Run VACUUM regularly based on transaction frequency Phase 3: Team Alignment and Next Steps Implementing Cost Observability and Taking Action Effective cost management in Databricks goes beyond configuration and code—it requires robust observability, granular tracking, and proactive measures. Below outlines how your teams can achieve this using system tables, tagging, dashboards, and actionable scripts. Cost Observability with System Tables Databricks Unity Catalog provides system tables that store operational data for your account. These tables enable historical cost observability and empower FinOps teams to analyze spend independently. System Tables Location: Found inside the Unity Catalog under the “system” schema. Key Benefits: Structured data for querying, historical analysis, and cost attribution. Action: Assign permissions to FinOps teams so they can access and analyze dedicated cost tables. Enable Tags for Granular Tracking Tagging is a powerful feature for tracking, reporting, and budgeting at a granular level. Classic Compute: Manually add key/value pairs when creating clusters, jobs, SQL Warehouses, or Model Serving endpoints. Use cluster policies to enforce custom tags. Serverless Compute: Create budget policies and assign permissions to teams or members for serverless workloads. Action: Tag all compute resources to enable detailed cost attribution and reporting. Track Costs with Dashboards and Alerts Databricks offers prebuilt dashboards and queries for cost forecasting and usage analysis. Dashboards: Visualize spend, usage trends, and forecast future costs. Prebuilt Queries: Use top queries with system tables to answer meaningful cost questions. Budget Alerts: Set up alerts in the Account Console (Usage > Budget) to receive notifications when spend approaches defined thresholds. Review Bottlenecks and Optimization Opportunities With observability in place, regularly review system tables, dashboards, and tagged resources to identify: Cost Bottlenecks: Clusters or jobs with unusually high spend. Optimization Opportunities: Underutilized resources, inefficient jobs, or misconfigured clusters. Team Alignment: Share insights with engineering and FinOps teams to drive collaborative optimization. Summary Table: Cost Observability & Action Steps Area Best Practice / Action System Tables Use for historical cost analysis and attribution Tagging Apply to all compute resources for granular tracking Dashboards Visualize spend, usage, and forecasts Alerts Set budget alerts for proactive cost management Scripts/Queries Build custom analysis tools for deep insights Cluster/Data/Code Review & Align Regularly review best practices, share findings, and align teams on optimizationUnlock bigger deals with flexible billing and five-year terms in Microsoft Marketplace
Many partners are already leveraging flexible billing and multi-year terms to close larger, more strategic deals in the Microsoft Marketplace. These features have been available for some time, but they’re worth revisiting — especially as they enable more personalized, enterprise-grade transactions for both SaaS and Virtual Machine (VM) offers. Why it matters Flexible billing and extended terms aren’t just enhancements — they’re strategic tools that help partners: Align with customer budgeting cycles Support custom payment preferences Simplify complex procurement processes Enable predictable revenue streams If you haven’t revisited your Marketplace offer recently, now is the time. Flexible billing: More control, more opportunity Traditionally, SaaS and VM offerings have been limited to monthly or annual upfront billing. Now, with flexible billing, you can set custom billing schedules with up to 70 installments over the duration of the contract. Key Capabilities Set specific charge dates and amounts Add notes for context (visible to both customer and partner) Support flat-rate SaaS and Professional Services plans Available globally in all Marketplace-supported currencies 💡 Note: Seat-based SaaS pricing is not supported. Metered billing dimensions are still charged monthly. Example Billing Schedule Charge Date Amount (USD) Notes Immediate $0.00 No upfront fee Jan 10, 2026 $5,000.00 First charge Jul 05, 2026 $2,500.00 Mid-year Mar 15, 2027 $8,000.00 Q1 Year 2 Sep 20, 2027 $4,000.00 Final charge Total $19,500.00 Longer terms: Up to five years Previously, term lengths were restrictive: SaaS: Max 3 years VM: 1- or 3-year terms, upfront only Now, both SaaS and VM offers support 1- to 5-year terms, combined with flexible billing. This opens the door to: Multi-year contracts with predictable revenue Enterprise-grade commitments Simplified renewals and fewer procurement cycles Real-world impact: Siemens “Siemens was recently able to transact a 5-year contract with a quarterly payment schedule on the Microsoft Marketplace thanks to the new multiyear contract durations. The process to enable this 5-year contract within one offer with a very large customer was extremely easy, and it simplifies the complex billing schedule. Siemens is excited and will continue to transact complex deals through the Microsoft Marketplace.” — Clare Dennis, Senior Manager, 3rd Party Marketplace Operations, Siemens Best practices to keep in mind Contract Duration: Must be 1, 2, 3, 4, or 5 years Charge Dates: Must fall within the contract duration and are executed in UTC Immediate Charges: Can be set to $0 to delay the first payment Currency Support: Pricing can be defined in local currencies using export/import tools Multiparty Offers: Channel partners can apply a uniform customer adjustment percentage Modifications: Once accepted, flexible schedules are locked Renewals: SaaS plans renew at public pricing unless auto-renew is disabled Resources to Help You Get Started Flexible billing schedule documentation Video tutorials: Flexible billing for private offers - This video explains what flexible billing is, how the feature works, and covers details of capabilities and restrictions. Flexible billing for private offers - This demo video shows software development companies how to create a direct customer private offers with a flexible billing schedule. Flexible billing for private offers - This demo video shows how to create multi-party private offers with flexible billing. The flexible billing customer experience - This demo video shows the customer purchase experience for a Marketplace private offer which has a flexible billing setup. This video is valuable to software development companies and customers. Take action Review your current offers in Partner Center and consider updating them to include flexible billing and longer terms. These features can help you: Support custom billing schedules Create multi-year partnerships Compete with tailored deal structures Simplify procurement for enterprise customers Need help? Reach out to your Microsoft partner manager or consult the Marketplace documentation for more details.Azure Governance Tools Policies, Blueprints, and Role-Based Access Control (RBAC)
In today’s cloud-driven world, organizations shifting workloads to Microsoft Azure need more than just virtual machines and databases—they need governance. Governance provides the framework of rules, standards, and controls that keeps your Azure environment secure, compliant, and cost-efficient. In this post, we’ll explore three essential Azure governance tools—Azure Policy, Azure Blueprints, and Role-Based Access Control (RBAC)—how they differ, how they work together, and how you can use them to create a well-governed Azure environment. https://dellenny.com/azure-governance-tools-policies-blueprints-and-role-based-access-control-rbac/Azure DevOps for Container Apps: End-to-End CI/CD with Self-Hosted Agents
Join this hands-on session to learn how to build a complete CI/CD pipeline for containerized applications on Azure Container Apps using Azure DevOps. You'll discover how to leverage self-hosted agents running as event-driven Container Apps jobs to deploy a full-stack web application with frontend and backend components. In this practical demonstration, you'll see how to create an automated deployment pipeline that builds, tests, and deploys containerized applications to Azure Container Apps. You'll learn how self-hosted agents in Container Apps jobs provide a serverless, cost-effective solution that scales automatically with your pipeline demands—you only pay for the time your agents are running. Don't miss your spot !GPT‑5.1 in Foundry: A Workhorse for Reasoning, Coding, and Chat
Azure AI Foundry is unveiling OpenAI’s GPT-5.1 series, the next generation of reasoning, analytics, and conversational intelligence. The following models will be rolling out in Foundry today: GPT-5.1: adaptive, more efficient reasoning GPT-5.1-chat: chat with new chain-of-thought for end-users GPT-5.1-codex: optimized for long-running conversations with enhanced tools and agentic workflows GPT-5.1-codex-mini: a compact variant for resource-constrained environments Learn more here!Discover how flexible billing and multi-year terms drive bigger deals
Partners are increasingly using custom billing schedules and extended contract terms to secure larger, more strategic agreements in Microsoft Marketplace. These capabilities—available for both SaaS and VM offers—make transactions more personalized and enterprise-ready. Read the full article to learn how these features can help you close high-value deals.Building brighter futures: How YES tackles youth unemployment with Azure Database for MySQL
YES leverages Azure Database for MySQL to power South Africa’s largest youth employment initiative, delivering scalable, reliable systems that connect thousands of young people to jobs and learning opportunities.Accelerating the multi-cloud advantage: Storage migration paths into Azure storage
Broaden your customer base and enhance your app’s exposure by bringing your AWS-based solution to Azure and listing it on Microsoft Marketplace. This guide will walk you through how Azure storage services compare to those on AWS—spotlighting important differences in architecture, scalability, and feature sets—so you can make confident choices when replicating your app’s storage layer to Azure. This post is part of a series on replicating apps from AWS to Azure. View all posts in this series For software development companies looking to expand or replicate their marketplace offerings from AWS to Microsoft Azure, one of the most critical steps is selecting the right Azure storage services. While both AWS and Azure provide robust cloud storage options, their architecture, service availability, and design approaches vary. To deliver reliable performance, scale globally, and meet operational requirements, it’s essential to understand how Azure storage works—and how it compares to AWS—before you replicate your app. AWS to Azure storage mapping When replicating your app from AWS to Azure, start by mapping your existing storage services to the closest Azure equivalents. Both clouds offer robust object, file, and block storage, but they differ in architecture, features, and integration points. Choosing the right Azure service helps keep your app performant, secure, and manageable—and aligns with Microsoft Marketplace requirements for an Azure‑native deployment. AWS Service Azure Equivalent Recommended use cases & key differences Amazon S3 Azure Blob Storage (enable ADLS Gen2 for hierarchical namespace + POSIX ACLs) Object storage with strong consistency and tiering (Hot/Cool/Archive). Blob is part of an Azure Storage account; ADLS Gen2 unlocks data‑lake/analytics features. Amazon EFS Azure Files (SMB/NFS) General‑purpose shared file systems and lift‑and‑shift app shares. Azure Files supports full-featured SMB and fully POSIX compatible NFS shared filesystems on Linux. Amazon FSx for Windows File Server Azure Files (SMB) Windows workloads that need full NTFS semantics, ACLs, and directory integration. Use Premium for low‑latency shares. Amazon FSx for NetApp ONTAP Azure NetApp Files Enterprise file storage with predictable throughput/latency, multiprotocol (SMB/NFS), and advanced data management. Amazon EBS Azure Managed Disks (Premium SSD v2 or Ultra Disk for top performance) Low‑latency block storage for VMs/DBs with provisioned IOPS/MBps; choose Premium SSD v2/Ultra for tighter SLOs. Local NVMe on EKS Azure Container Storage Extreme performance for Kubernetes workloads with a familiar cloud-native developer experience Many EBS volumes (fleet scale) Azure Elastic SAN (VMs & AKS only) Pooled, large‑scale block for Azure VMs via iSCSI or AKS via Azure Container Storage; simplifies fleet provisioning and management. Tip: Some AWS services map to multiple Azure options. For example, EFS → Azure Files for straightforward SMB/NFS shares, or → Azure NetApp Files when you need stricter latency SLOs and multiprotocol at scale. Match your use case After mapping AWS services to Azure equivalents, the next step is selecting the right service for your workload. Start by considering the access pattern, object, file, or block, and then factor in performance, protocol, and scale. Object storage & analytics: Use Azure Blob Storage for unstructured data like images, logs, and backups. If you need hierarchical namespace and POSIX ACLs, enable Azure Data Lake Storage Gen2 on top of Blob. General file sharing / SMB apps: Choose Azure Files (SMB) for lift‑and‑shift scenarios and Windows workloads. Integrate with Entra ID for NTFS ACL parity, and select the Premium tier for low‑latency performance. NFS or multiprotocol file workloads: Start with Azure Files (NFS) for basic needs, or move to Azure NetApp Files for predictable throughput, multiprotocol support, and enterprise‑grade SLAs. High‑performance POSIX workloads: For HPC or analytics pipelines requiring massive throughput, use Azure Managed Lustre. Persistent storage for containers: Azure’s CSI drivers brings Kubernetes support for most Azure disk, files, and blob offerings. Azure Container Storage brings Kubernetes support for unique disk backends that are unsupported by the Azure Disks CSI driver, such as local NVMe. Block storage for VMs and databases: Use Azure Managed Disks for most scenarios, with Premium SSD v2 or Ultra Disk for provisioned IOPS and sub‑millisecond latency. For large fleets or shared performance pools, choose Azure Elastic SAN (VMs & AKS only). Quick tip: Start simple—Blob for object, Azure Files for SMB and NFS, Managed Disks for block—and scale up to NetApp Files, Elastic SAN, or Managed Lustre when performance or compliance demands it. Factor in security and compliance Encryption: Confirm default encryption meets your compliance requirements; enable customer‑managed keys (CMK) if needed. Access control: Apply Azure RBAC for role‑based permissions and ACLs for granular control at the container or file share level. Network isolation: Use Private Endpoints to keep traffic off the public internet and connect storage to your VNet. Identity integration: Prefer Managed Identities or SAS tokens over account keys for secure access. Compliance checks: Verify your chosen service meets certifications like GDPR, HIPAA, or industry‑specific standards. Optimize for cost Tiering: Use Hot, Cool, and Archive tiers in Blob Storage based on access frequency; apply Premium tiers only where low latency is critical. Lifecycle management: Automate data movement and deletion with lifecycle policies to avoid paying for stale data. Reserved capacity: Commit to 1–3 years of capacity for predictable workloads to unlock discounts. Right‑sizing: Choose the smallest disk, volume, or file share that meets your needs; scale up only when required. Monitoring: Set up cost alerts and review usage regularly to catch anomalies early; use Azure Cost Management for insights. Avoid hidden costs: Co‑locate compute and storage to prevent cross‑region egress charges. Data migration from AWS to Azure Migrating your data from AWS to Azure is a key step in replicating your app’s storage layer for Marketplace. The goal is a one‑time transfer—after migration, your app runs fully on Azure. Azure Storage Mover: A managed service that automates and orchestrates large‑scale data transfers from AWS S3, EFS, or on‑premises sources to Azure Blob Storage, Azure Files, or Azure NetApp Files. Ideal for bulk migrations with minimal downtime. AzCopy: A command‑line tool for fast, reliable copying of data from AWS S3 to Azure Blob Storage. Great for smaller datasets or scripted migrations. Azure Data Factory: Built‑in connectors to move data from AWS storage services to Azure, with options for scheduling and transformation. Azure Data Box: For very large datasets, provides a physical device to securely transfer data from AWS to Azure offline. Final readiness before marketplace listing Validate performance under load: Benchmark with real data and confirm your chosen SKUs deliver the IOPS and latency your app needs. Lock down security: Ensure RBAC roles are applied correctly, Private Endpoints are in place, and encryption meets compliance requirements. Control costs: Verify lifecycle policies, reserved capacity, and cost alerts are active to prevent surprises. Enable monitoring: Set up dashboards and alerts for throughput, latency, and capacity so you can catch issues before customers do. Key Resources SaaS Workloads - Microsoft Azure Well-Architected Framework | Microsoft Learn Metered billing for SaaS offers in Partner Center Create plans for a SaaS offer in Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success Maximize your momentum with step-by-step guidance to publish and grow your app with App AdvisorPartner Blog | Azure updates for partners: November 2025
We’ve moved beyond the era of experimentation. Across our partner ecosystem, I’m seeing a decisive shift from exploring what’s possible with AI and cloud to executing with focus, scale, and sustainable impact. Azure Accelerate and momentum in the Microsoft Marketplace give partners like you a strong foundation to align investments and deliver outcomes that matter. Microsoft Ignite is where those insights become action. It is where ideas become practice and where partners shape what is possible for customers. From November 18–21 in San Francisco and online, you will hear directly from our product leaders, preview the latest in Azure and AI innovation, and connect with peers who are pushing the boundaries of what’s possible. Make time for ancillary events, interactive roundtables, and the Partner Hub to build new skills, expand your network, and explore hands-on opportunities to advance your work. I’m especially looking forward to the sessions on AI-powered innovation in Azure, my session on how partners can accelerate growth with Azure, and the hands-on labs. There is always a moment that sparks new ideas that I can take back to my team. If you have not registered, do so today. Plan your agenda, pick sessions that stretch your thinking, and come ready to take fresh ideas back to your customers. Here’s a look at other recent updates designed to help partners move faster, innovate more, and deliver greater value with Azure. Continue reading here Be sure to follow our Azure discussion board for updates and new conversations!Time tracking woven into Microsoft 365, simple, secure, and proven
From Web to Teams, Outlook, and now M365 Copilot, Klynke time tracking is fully woven into the Microsoft 365 fabric. For partners, this isn’t just another feature. It’s a value-added service you can recommend, bundle, or build into your existing offerings. The payoff: clients stay inside the tools they already know and trust, while you strengthen your Microsoft 365 practice with a solution that’s proven, secure, and seamless. With a tried and tested approach, partners can confidently deliver integrated experiences that feel natural to end users. No extra training. No steep adoption curve. Microsoft even spotlighted this in the Tech Community interview Building Secure SaaS on Microsoft Cloud Let’s explore how integrated solutions like time tracking can open new doors for collaboration. If you’re building on Microsoft 365, we’d love to connect and share ways to create more value together.
