IP Co-Sell best practices: What high performing SDCs do to accelerate Microsoft Marketplace success
Barbara Treviño (BT) is Director of Strategic Partnerships & Alliances at Labra. She is a seasoned partnership leader with more than a decade of experience across sales, partner operations, alliances, enablement, programs, and cloud marketplace go-to-market. __________________________________________________________________________________________________________________________________________________________________ For Solution Development Companies (SDCs) building on Azure, Marketplace listing and IP Co-Sell eligibility are foundational milestones. But the SDCs who accelerate fastest—and generate meaningful traction with Microsoft—are the ones who understand that eligibility is only the beginning. Drawing from a decade working across the Microsoft ecosystem and leading Marketplace and Co-Sell readiness across AWS, Azure, and Google Cloud, I’ve seen a consistent pattern: high-performing SDCs prepare differently. They approach readiness as a strategic, architectural, and operational effort—not just a form submission. This article highlights what those SDCs do differently, why it matters, and the signals Microsoft looks for when evaluating partners beyond the checklist. Eligibility is the starting line—not the win SDCs often assume that once their offer is live and their IP Co-Sell submission is approved, Microsoft sellers will engage and pipeline will follow. In practice, Microsoft evaluates far more than the required fields. Seller confidence depends on deeper indicators of readiness, including: Technical alignment with Azure Architectural clarity in how the solution runs Customer outcomes that map to Azure value themes Consistent messaging across Marketplace assets SDC maturity in supporting joint customer conversations These factors influence whether a Partner Development Manager (PDM) or account executive sees a path to meaningful co-sell engagement. What high-performing SDCs do differently Across clouds and across maturity levels, top-performing SDCs consistently demonstrate five distinct behaviors: They lead with architectural clarity Azure‑aligned architecture is one of the strongest signals of technical readiness. High‑performing SDCs provide clear diagrams and narrative context that show exactly how their solution complements Azure services. They align their narrative to Microsoft’s sales motions Microsoft sellers need a replicable story. The strongest SDCs use language, outcomes, and framing that match how Microsoft positions value internally and externally. They present relevant customer evidence The best SDCs focus on customer outcomes that reinforce Azure consumption, modernization, or workload migration—not generic case studies. They sequence their readiness intentionally Rather than uploading every asset at once, high performers focus on what’s required now, save optional materials for later phases, and minimize rework cycles. They prepare for what happens after approval Eligibility is a threshold. Momentum requires internal readiness for co‑sell motions, customer engagement, and Marketplace operations. Why readiness staging accelerated IP Co-Sell approval Most delays during IP Co-Sell review come from misalignment—not missing assets. Common issues include: Architecture that contradicts the listing Evidence that doesn’t reinforce the solution’s value Positioning that isn’t Azure-aligned Assets uploaded “just in case” instead of intentionally Internal teams unprepared for post-listing motions High-performing SDCs move faster not because they rush, but because they prepare strategically. How Labra supports SDCs through SCAP-M Labra’s SCAP-M (SaaS Co-Sell Accelerator for Microsoft) program focuses on the deeper readiness drivers that influence Microsoft engagement, including: Azure-aligned reference architecture development Marketplace and solution-story coherence Customer evidence refinement Readiness sequencing to reduce review cycles Internal preparation for post-approval co-sell motions This is where structured support has the greatest impact—accelerating both eligibility and long-term field engagement. How can you learn more? Join me on February 25th for a live session where we will take a deeper look at: What Microsoft evaluates beyond the form How readiness staging reduces delays Where SDCs unintentionally create friction Why architecture, evidence, and narrative matter for seller adoption Practical insights drawn from Labra’s multi-cloud experience A live Q&A will follow for SDCs interested in accelerating their Marketplace and Co-Sell motion on Azure. Follow this link to add the session to your calendar: Inside Azure IP co-sell: What high-performing software developers do differently - Microsoft Marketplace Community If you miss the live session- don't worry, you can use the same link to view a recording of the session. High performing SDCs succeed in Azure’s IP Co-sell program because they treat readiness as a strategic initiative – not an administrative task. By aligning architecture, narrative, and customer evidence with Microsoft’s expectations, SDCs accelerate approvals and increase field engagement.
Migrating your AWS offer to Microsoft Marketplace - Database services
For software development companies looking to expand or replicate their marketplace offerings from AWS to Microsoft Azure, one of the most critical steps in replicating your solution is selecting the right Azure database services. While both AWS and Azure provide robust managed database 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 databases work—and how they compare to AWS—before you replicate your app. 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 walks you through how Azure database services compare to those on AWS—spotlighting differences in architecture, scalability, and feature sets—so you can make confident choices when replicating your app’s data layer to Azure. This post is part of a series on replicating apps from AWS to Azure. View all posts in this series. AWS to Azure database mapping When replicating your app from AWS to Azure, start by mapping your existing database services to the closest Azure equivalents. Both clouds offer relational, NoSQL, and analytics databases, 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 Azure Marketplace requirements for an Azure-native deployment. AWS Service Azure Equivalent Recommended Use Cases & Key Differences Amazon RDS (MySQL/PostgreSQL) Azure Database for MySQL / PostgreSQL Fully managed relational DB with built-in HA, scaling, and security. Building Generative AI apps. Amazon RDS (SQL Server) Azure SQL Database or Azure SQL Managed Instance Use Azure SQL Database for modern apps; choose Managed Instance for near 100% compatibility with on-prem SQL Server. SQL Server on EC2 SQL Server on Azure VMs Best for lift-and-shift scenarios requiring full OS-level control. Amazon RDS (Oracle) Oracle Database@Azure Managed Oracle workloads with Azure integration. Amazon Aurora (PostgreSQL/MySQL) Azure Database for PostgreSQL (Flexible Server) or Azure Database for MySQL Similar managed experience for large workloads, consider Azure HorizonDB (public preview)—built on PostgreSQL to compete with Aurora & AlloyDB. Learn more. Amazon DynamoDB Azure Cosmos DB (NoSQL API) Global distribution, multi-model support, and guaranteed SLAs for latency and throughput. Amazon Keyspaces (Cassandra) Azure Managed Instance for Apache Cassandra Managed Cassandra with elastic scaling and Azure-native security. Cassandra on EC2 Azure Managed Instance for Apache Cassandra Same as above; ideal for lift-and-shift Cassandra clusters. Amazon DocumentDB MongoDB Atlas MongoDB on EC2 Azure DocumentDB Azure DocumentDB Azure DocumentDB Drop-in compatibility for MongoDB workloads with global replication and vCore-based pricing. Amazon Redshift Azure Synapse Analytics Enterprise analytics with integrated data lake and Power BI connectivity. Amazon ElastiCache (Redis) Azure Cache for Redis Low-latency caching with clustering and persistence options. 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 data model (relational, document, key-value), then factor in performance, consistency, and global reach. Building AI apps: Generative AI, vector search, advanced analytics. Relational workloads: Use Azure SQL Database, Azure SQL Managed Instance, or Azure Database for MySQL/PostgreSQL for transactional apps; enable zone redundancy for HA. Review schema compatibility, stored procedures, triggers, and extensions. Inventory all databases, tables, indexes, users, and dependencies before migration. Document any required refactoring for Azure. NoSQL workloads: Choose Azure Cosmos DB for globally distributed apps; select the API (No SQL, MongoDB, Cassandra) that matches your existing schema. Validate data: Model mapping and test migration in a sandbox environment to ensure data integrity and application connectivity. Analytics: For large-scale queries and BI integration, Azure Synapse Analytics offers MPP architecture and tight integration with Azure Data Lake. Inventory all analytics assets, ETL pipelines, and dependencies. Plan for migration using Azure Data Factory or Synapse pipelines. Test performance benchmarks and optimize query plans post-migration. Caching: Azure Cache for Redis accelerates app performance with in-memory data and clustering. Update application connection strings and drivers to use Azure endpoints. Implement retry logic and connection pooling for reliability. Validate cache warm-up and failover strategies. Hybrid scenarios: Combine Cosmos DB with Synapse Link (for Synapse as target) or Fabric Mirroring (for Fabric as target) for real-time analytics without ETL overhead. Assess network isolation, security, and compliance requirements. Deploy Private Endpoints and configure RBAC as needed. Document integration points and monitor hybrid data flows. Factor in security and compliance Encryption: Confirm default encryption meets compliance requirements; enable customer-managed keys (CMK) if needed. Enable Transparent Data Encryption (TDE) and review encryption for backups and in-transit data. Access control: Apply Azure RBAC and database-level roles for granular permissions. Audit user roles and permissions regularly to ensure least privilege. Network isolation: Use Private Endpoints within a virtual network to keep traffic off the public internet. Configure Network Security Groups (NSGs) and firewalls for additional protection. Identity integration: Prefer Managed Identities for secure access to databases. Integrate with Azure Active Directory for centralized identity management. Compliance checks: Verify certifications like GDPR, HIPAA, or industry-specific standards. Use Azure Policy and Compliance Manager to automate compliance validation Audit logging and threat detection: Enable audit logging and advanced threat detection with Microsoft Defender for all database services. Review logs and alerts regularly. Optimize for cost Compute tiers: Choose General Purpose for balanced workloads; Business Critical for low-latency and high IOPS. Review workload sizing and adjust tiers as needed for cost efficiency. Autoscaling: Enable autoscale for Cosmos DB and flexible servers to avoid overprovisioning. Monitor scaling events and set thresholds to control spend. Reserved capacity: Commit to 1–3 years for predictable workloads to unlock discounts. Evaluate usage patterns before committing to reservations. Serverless: Use serverless compute for workloads with completely ad hoc usage and low frequency of access. This eliminates the need for pre-provisioned resources and reduces costs for unpredictable workloads. Monitoring: Use Azure Cost Management and query performance insights to optimize spend. Set up budget alerts and analyze cost trends monthly. Include basic resource monitoring to detect adverse usage patterns early. Storage and backup costs: Review storage costs, backup retention policies, and configure lifecycle management for backups and archives. Data migration from AWS to Azure Migrating your data from AWS to Azure is a key step in replicating your app’s database layer for Azure Marketplace. The goal is a one-time transfer—after migration, your app runs fully on Azure. Azure Database Migration Service (DMS): Automates migration from RDS, Aurora, or on-prem to Azure Database, Azure SQL Managed Instance, Azure Database for MySQL/PostgreSQL, and SQL Server on Azure VM (for MySQL/PostgreSQL/SQL Server). Supports online and offline migrations; run pre-migration assessments and schema validation. Azure Data Factory: Orchestrates data movement from DynamoDB, Redshift, or S3 to Azure Cosmos DB or Synapse. Use mapping data flows for transformations and data cleansing. MongoDB migrations: Use the online migration utility designed for medium to large-scale migrations to Azure DocumentDB. Ensure schema compatibility and validate performance benchmarks before cutover. Cassandra migrations: Use Cassandra hybrid cluster or dual write proxy for Azure Managed Instance for Apache Cassandra. Validate schema compatibility and test migration in a sandbox environment. Offline transfers: For very large datasets, use Azure Data Box for secure physical migration. Plan logistics and security for device handling. Migration best practices: Schedule migration during a maintenance window, validate data integrity post-migration, and perform cutover only after successful data validation & verifications. Final readiness before marketplace listing Validate performance: Benchmark with real data and confirm chosen SKUs deliver required throughput and latency. Test application functionality under expected load and validate query performance for all critical scenarios. Lock down security: Ensure RBAC roles, Private Endpoints, and encryption meet compliance requirements. Review audit logs, enable threat detection, and verify access controls for all database and storage resources. Control costs: Verify autoscaling, reserved capacity, and cost alerts are active. Review storage and backup policies, and set up budget alerts for ongoing cost control. Enable monitoring: Set up dashboards for query performance, latency, and capacity. Configure alerts for failures, anomalies, and capacity thresholds. Monitor with Azure Monitor and Log Analytics for real-time operational insights. Documentation and support: Update migration runbooks, operational guides, troubleshooting documentation, and escalation contacts for post-migration support. 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 Advisor
