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Cosmos DB APIs & Capacity

APIs · Provisioned · Autoscale · Serverless · Multi-region

Practice questions for this topic →

APIs

APIPick when…
NoSQLNew apps; richest features; native JSON
MongoDBExisting MongoDB apps and drivers
Apache CassandraExisting Cassandra/CQL apps
Apache GremlinGraph relationships
TableTable Storage apps needing global distribution and indexing

Capacity modes

ModeBillingRegionsLatency targetPick when…
Provisioned (manual)RU/s per hourUnlimited<10 ms point reads and writes (SLA)Steady, predictable traffic
AutoscaleHighest RU/s reached in each hour (scales 10%–100% of the maximum)UnlimitedAs provisionedVariable traffic with an SLA
ServerlessRUs consumedSingle region only<10 ms point reads, <30 ms writes (SLO)Intermittent, low average traffic, dev/test

Facts to know

Exam Tips

Tick each tip once you can answer it without looking.

“Existing MongoDB app moved to Azure with no code changes” → Cosmos DB for MongoDB.

“Model social network relationships” → Cosmos DB for Apache Gremlin.

“Sporadic traffic, lowest cost, single region” → Serverless.

“Unpredictable spikes but must keep SLA and multiple regions” → Autoscale provisioned throughput. Trap: Serverless is single-region only.

“Writes must be accepted in every region with lowest latency” → Multi-region writes (multi-master).

“Steady 10,000 RU/s around the clock” → Manual provisioned throughput (+ reserved capacity).

“Replicate Cosmos data to analytics without impacting transactions (new project)” → Cosmos DB mirroring in Microsoft Fabric. Trap: Synapse Link isn't supported for new projects.

“Convert a serverless account to provisioned throughput” → Each container gets partitions x 5,000 RU/s as manual throughput, and the change is irreversible. Trap: You can move to autoscale afterwards.

“Latency guarantees by capacity mode” → Provisioned: under 10 ms point reads and writes (SLA). Serverless: under 10 ms reads and under 30 ms writes (SLO). Trap: Serverless accounts run in one region only.

Sources

← Database SecurityCosmos DB Consistency & Partitioning →