Use cases
Performance workflows for real release pressure.
Choose the workflow that matches your goal: validating an API under load, gating a release, monitoring production continuously, or proving your platform is ready for peak traffic. Every use case pairs a concrete objective with the MaxoPerf features that deliver it.
- AI inference load testing
Validate LLM inference APIs under concurrent request load. Measure time-to-first-token, token throughput, and cost per request to right-size GPU capacity.
For: ML infrastructure, AI platform, and backend teams operating LLM APIs
- Frontend browser performance testing
Load-test real browser journeys (login flows, SPAs, and media-heavy pages), measuring user-perceived latency and render performance under concurrency.
For: Frontend engineers, web performance engineers, and SRE teams owning user-facing surfaces
- Microservices SLO validation
Confirm each service meets its latency and error-budget SLOs under realistic request concurrency before changes are promoted to production environments.
For: SRE, platform reliability, and microservices engineering teams
- Peak traffic readiness
Validate that your platform survives Black Friday, product launches, and seasonal spikes using breakpoint and scalability tests weeks before the event.
For: SRE, platform, and e-commerce engineering teams
- Regression and baseline testing
Establish performance baselines before changes ship and catch regressions automatically by comparing every run against your stored reference baseline.
For: Backend engineers, SRE teams, and engineering leads managing release quality
- Results analysis and reporting
Turn raw run data into release decisions: compare runs across releases, drill into per-label breakdowns, and share self-contained reports with stakeholders.
For: SRE teams, engineering managers, and release owners
- Streaming and media load testing
Validate video delivery, live-stream concurrency, and media API throughput under realistic viewer concurrency before a broadcast or major product launch.
For: Media engineering, streaming platform, and video infrastructure teams
- Synthetic monitoring
Run scheduled test journeys from managed regions, alert on p95 latency or error-rate regressions, and catch service degradation before customers report it.
For: SRE and platform reliability teams
- API performance testing
Validate API latency, throughput, and correctness under realistic load using labeled scenarios, p95/p99 thresholds, and assertion signals your team can act on.
For: Backend engineers and API platform teams
- CI/CD performance gates
Block release promotions when latency, error rate, or runner health breach agreed thresholds. Catch performance regressions before they reach production users.
For: Release engineering, SRE, and backend platform teams
- Cloud load testing
Run repeatable load tests from managed runner regions without provisioning infrastructure. Scale from 10 to 10,000 virtual users in minutes per region.
For: SREs, platform teams, and backend engineering leads
- Private load generation
Generate load from inside your network perimeter when targets are private, regulated, or must never be exposed to external testing infrastructure during a run.
For: Platform, security, and regulated-industry engineering teams
