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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.

Results

Hosting costs
Platform scalability
Platform responsiveness
System availability

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.

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