Cosmos DB API for MongoDB

Security for Cosmos DB API for MongoDB should cover account keys, connection strings, firewall rules, private endpoints, approved client networks, diagnostic logs, and administrator roles. MongoDB applications often carry secrets in configuration files, containers, or CI variables, so migration must include secret cleanup and rotation. Data classification matters because document collections may contain nested personal or financial fields that are easy to expose through broad queries. Review whether support tools, BI exports, or migration utilities have excessive access. Security checks should prove that public network access is controlled, credentials are rotated, least-privilege operations are documented, and failed connection attempts are visible.

Cost for Cosmos DB API for MongoDB is influenced by request units, storage, regions, backups, monitoring, and query efficiency. Existing MongoDB teams may think in clusters or server tiers, but Cosmos DB cost is tied closely to throughput and data volume. Large documents, unselective queries, missing or mismatched indexes, high autoscale ceilings, and replicated regions can raise spend quickly. Cost reviews should examine RU per operation, collection growth, peak traffic, analytical exports, and migration validation environments. Budget owners need alerts that connect spend to applications, not just account names, so teams can tune queries before cost becomes an incident. Tie every cost decision to measured workload behavior and named budget ownership.

Reliability for Cosmos DB API for MongoDB depends on account regions, consistency settings, backup policy, partitioning, throughput headroom, index design, and driver retry behavior. A migration that passes small tests can still fail when production traffic creates hot partitions, expensive aggregations, or retry storms after throttling. Teams should define recovery objectives, restore steps, failover expectations, and acceptable error handling for application services. Monitor 429 responses, request charge, latency percentiles, normalized RU, storage growth, and regional health. Reliability drills should include bad data writes, accidental collection deletion, driver version changes, and regional outage scenarios. Include realistic failure drills, owner escalation, and recovery evidence in the runbook.

Performance for Cosmos DB API for MongoDB is shaped by partition keys, index definitions, document size, aggregation patterns, throughput, consistency, region distance, and driver configuration. Applications should prefer targeted reads and writes that align with partitioning rather than broad cross-partition queries. Test the exact MongoDB driver version, indexes, and aggregation pipelines used in production because compatibility does not guarantee identical performance. Monitor latency percentiles, request charge, throttling, and query diagnostics during load tests. Performance work often means changing indexes, query filters, document shape, or autoscale settings before adding more throughput. Validate tuning with representative traffic, documented baselines, and user-facing service targets.

Operationally, Cosmos DB API for MongoDB needs a clear map of accounts, databases, collections, partition keys, indexes, applications, drivers, and owners. Support teams should know which collections are business critical, which queries are approved, and which migrations or analytics jobs can be paused when RU pressure appears. Runbooks should include CLI discovery, connection string rotation, throughput updates, backup verification, index review, and troubleshooting for driver errors. Migration projects should retain compatibility test results and known limitations. Good operations prevent a familiar MongoDB interface from hiding Azure-specific responsibilities around scaling, monitoring, cost, and recovery. Keep ownership, dashboard links, deployment history, and support escalation steps current.