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Optimizing scalability and cost for a global SaaS provider

Why are your hosting costs growing faster than your traffic?

About the company

A global SaaS provider offering cloud-based collaboration and productivity tools to enterprises, with millions of active users relying on a microservices architecture that must remain scalable, cost-effective, and high-performing.
Optimizing scalability and cost for a global SaaS provider

Industry

SaaS

Key challenge

Rising hosting costs from inefficient resource allocation; performance bottlenecks during peak usage caused by infrastructure configuration issues rather than application logic

Stack under test

HTTPS REST APIs (client-facing), Amazon SQS via JMS (inter-service messaging), SQL (persistent data)

QALIPSIS deployment

CI/CD pipeline execution with step-level monitoring and Slack notifications

Challenges

Where is the infrastructure waste hiding?

  • Hosting costs scaled faster than traffic with no data to pinpoint the waste.
  • Per-service monitoring could not trace end-to-end impact of configuration decisions.
  • An idle service could consume resources that a downstream service actually needed.

Solution: how QALIPSIS was used

How to test end-to-end across API, messaging, and database?

  • HTTP steps generated client-facing API traffic under progressively increasing load.
  • Messaging plugin consumed messages from Amazon SQS alongside API load injection.
  • Join operators matched each API request against its messaging and database outcomes.
  • Redundant service found: an intermediary performed trivial work that its neighbour could handle.
  • Connection shortage found: the main API service ran out of database connections at peak.

How to validate every infrastructure change automatically?

  • QALIPSIS scenarios ran in the CI/CD pipeline after every infrastructure change.
  • Step-level monitoring showed per-service response times and messaging lag.
  • Slack notifications on campaign failures caught configuration regressions before production.

Results

Hosting costs
Platform scalability
Platform responsiveness
System availability

Conclusion

Challenge

Hosting costs outpacing traffic growth due to infrastructure waste invisible to per-service monitoring, with configuration issues causing peak-load bottlenecks.

Solution

QALIPSIS tested the full service chain — from API through messaging to database — under progressive load, embedded in the infrastructure-change pipeline.

Gains

15% hosting cost reduction, 40% more concurrent users, 25% faster peak response times, and every infrastructure change now validated automatically.

More use cases to explore

Want to optimize your platform's scalability and reduce hosting costs?
Request a Demo of QALIPSIS Today