Partition Keys That Scale
We analyze cardinality and access patterns to choose partition keys, or hierarchical partition keys, that spread storage and throughput evenly and keep hot partitions from throttling requests.
Database Technologies
Azure Cosmos DB Consulting
Global Scale on Azure With Costs You Can Predict
Azure Cosmos DB can serve users worldwide with low latency, but its core design choices are hard to reverse. Our architects help Azure teams choose the right API, partition key, and throughput model so applications scale without runaway request unit bills.
What We Deliver
Where We Make the Difference
Cosmos DB bills by request units, so a poor partition key or an index-everything policy shows up on the invoice as well as in latency. We model your access patterns and costs before building, then validate the design under realistic load.
Request Unit Cost Control
We compare provisioned, autoscale, and serverless throughput, trim indexing policies to what queries need, and tune queries so each operation consumes as few request units as possible.
Consistency and Multi-Region
We pick a consistency level from strong through bounded staleness and session to eventual, and configure multi-region reads or writes with conflict resolution that fits your data.
Choosing the Right API
We recommend the API for NoSQL, MongoDB, Apache Cassandra, Apache Gremlin, or Table based on your source system, query needs, and the skills your developers already have.
Our Approach
How We Deliver Azure Cosmos DB Consulting
Step 1: Model Access and Cost
We document every read and write pattern, estimate request unit consumption, and compare throughput options so the cost of the design is known before it is built.
Step 2: Design Containers and Keys
We design containers, document shapes, partition keys, and indexing policies, then test them against a sample of your real data distribution.
Step 3: Load Test and Tune
We run production-like traffic, watch for throttled requests and hot partitions, and refine queries, indexes, and throughput settings until your targets are met.
Step 4: Govern Spend Over Time
We set up Azure Monitor dashboards and budget alerts on request unit consumption so cost and performance stay visible as usage grows.
Why is our Cosmos DB bill higher than expected?
The usual causes are a partition key that concentrates traffic, cross-partition queries, default indexing of every property, or throughput provisioned for peak around the clock. We review each one and show what a change would save before we make it.
Which consistency level should we use?
Session consistency suits most user-facing applications because each user sees their own writes. Strong or bounded staleness make sense only where every reader must see the latest data, and they cost more in latency and request units.
Can we change the partition key later?
Not in place. Changing it means copying data to a new container with a container copy job or the change feed, which takes planning. That is why we spend time on the key up front.
Can we move a MongoDB application to Cosmos DB?
Often, using Azure Cosmos DB for MongoDB. We test your drivers and queries against it first, since feature support differs between the request unit and vCore options.
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