Cloud cost
Cosmos DB cost and performance review
Profiled provisioned throughput against real consumption to separate true bottlenecks from over-provisioned containers.
Proof block
What this proves
A compact hiring view of the work before the deeper project narrative.
A database account mixed expensive low-use containers with workloads that had real throttle risk.
Profiled throughput, consumption, storage, and throttle signals to separate cost savings from performance fixes.
Turned a vague cost concern into specific right-sizing and performance recommendations.
Can use telemetry to make cloud cost decisions without creating hidden reliability risk.
Situation
A multi-container account mixed under-provisioned workloads with low-utilization workloads, making a blanket change risky.
Role
Analyzed usage patterns, identified the actual throttling source, and produced rightsizing recommendations.
Actions
- Compared provisioned RU/s, consumed RU/s, storage size, and throttle signals.
- Separated daily burst behavior from sustained pressure.
- Recommended downsize candidates only where usage and throttling data supported the decision.
- Kept recommendations explicit enough for leadership to evaluate cost and performance tradeoffs.
Outcomes
- Identified the true performance constraint instead of cutting blindly.
- Created a specific savings path for low-utilization workloads.
- Improved the team conversation around cost with evidence instead of intuition.
Public safety
What is preserved
The project details are intentionally sanitized for a public repository while keeping the operating logic and technical tradeoffs visible.
Architecture thinking
Resource categories, dependency order, validation habits, and operational tradeoffs remain visible.
Impact
The outcomes focus on risk reduction, repeatability, cost awareness, and stakeholder alignment.
Protected details
Internal hostnames, ticket identifiers, raw IPs, client names, and sensitive names are excluded.