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Cloud Native Microservices Architecture Total Cost of Ownership Optimization

A high growth fintech enterprise operating 120 Kubernetes microservices across multiple AWS and Google Cloud regions, processing 80 million daily financial API calls.

Runs onSahay

cloud infrastructure spend reduced: 36%cloud infrastructure spend reduced
cost transparency achieved across all 120 production: 100%cost transparency achieved across all 120 production

How the work runs

The pressure that made this worth automating, the steps the system runs, and what came out the other side.

Pressure & Trigger Points

Cloud infrastructure spend grew by 45% quarter over quarter, driven by over provisioned container resources, idle compute nodes, and unindexed database queries.
Engineering teams had zero visibility into the true Total Cost of Ownership (TCO) per microservice or per customer transaction.
FinOps cost reduction initiatives stalled because developers could not pinpoint which specific code routines were generating excessive cloud resource consumption.

The run · 5 operational steps

Click any step to inspect telemetry signals, model reasoning, and governance gates.

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Microservice Resource Telemetry Mapping

Sahay correlates Kubernetes pod resource utilization, database IOPS, and network egress directly to individual microservices and code repositories.

Input Signal:

Real-time operational telemetry & queue

Reasoning Pattern:

MCP grounded vector inference

Governance Gate:

Policy constrained with audit write-back

Verified Business Outcomes

Cloud infrastructure spend reduced by 36%, saving $1.4M annually without degrading system latency or reliability.
100% cost transparency achieved across all 120 production microservices.
Engineering teams reduced average compute cost per transaction by 42% within 60 days.

Capabilities this relies on

system connectors
anomaly detection
root cause reasoning
forecasting
workflow orchestration

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