Cosmos DB
We build Cosmos DB solutions that scale predictably: correct partition keys, data model decisions, throughput planning, and monitoring so RU/s cost stays under control.
- NoSQL
- Partitioning
- Global Scale
Most Cosmos issues come from data modeling and partition strategy — we design those first.
Cosmos DB Foundations We Deliver
Cosmos DB is powerful, but only when the model and throughput strategy match access patterns.
Data Model & APIs
Choose the right API and modeling approach aligned to your app’s queries.
Partition Strategy
Partition keys designed to avoid hot partitions and support scale.
Throughput & Cost Planning
RU/s planning, autoscale strategy, and cost guardrails.
Observability
Dashboards and alerts for throttling, latency, and workload regressions.
How We Implement Cosmos DB
- 1
Access Pattern Review
Understand reads/writes, query patterns, and scale expectations.
- 2
Design
Model entities, pick partition keys, and define throughput and indexing.
- 3
Implement & Validate
Build collections, indexes, and app integration; validate with load tests.
- 4
Operate
Monitoring, cost guardrails, and runbooks for throttling and failures.
What You Get
Partition key and data model aligned to queries.
Throughput plan with autoscale strategy and cost controls.
Monitoring for throttling and latency regressions.
Guidance to keep RU/s usage predictable.
Common Cosmos DB Issues
RU/s spikes
Throughput costs explode due to inefficient queries or indexing.
Hot partitions
A poor partition key causes throttling and uneven performance.
Indexing mismatch
Default indexing wastes RU/s or blocks key query patterns.
Cosmos DB Questions
Build Cosmos DB Without Cost Surprises
Tell us your data and access patterns. We’ll propose a Cosmos DB design with predictable performance and throughput.
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