Generate, run, and optimize load, stress, spike, and soak tests for APIs, UIs, and data pipelines. Validate SLAs/SLOs, auto-heal test drift, and feed fixes back to your IDE and coding agents via MCP.
The first fully autonomous AI load testing agent in your IDE—ideal for scaling APIs and web apps with confidence.
Turn SLAs/SLOs and PRDs into executable load, stress, spike, and soak scenarios—no scripts to write and no frameworks to maintain.
Instantly parses your PRD—or infers intent from the code itself (MCP server)—to derive target latency, throughput, concurrency, and error budgets.
Spin up distributed load in a secure cloud-sandbox to verify APIs, UIs, and data pipelines against p95/p99 latency, error rate, and saturation limits. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
Delivers pinpoint bottleneck analysis and fix recommendations to you or your coding agent (MCP server), with self-healing for flaky selectors, waits, and test data—without masking real defects.
Raise reliability under load from guesswork to evidence. Model SLAs/SLOs, run distributed tests, and get prioritized fixes that increase capacity and reduce latency. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
Start Load Testing NowAutomatically re-run load and synthetic checks on schedules to detect performance regressions early and protect SLAs.
Group and manage your most important load scenarios for easy access, re-runs, and trend comparison.
Offers a free community version, making us accessible to everyone.
Comprehensive load testing for APIs, web frontends, and data workflows for seamless performance evaluation.
Throughput, latency, and error-rate analysis
Realistic user concurrency and page performance
Backfill and streaming throughput validation
Good job! Pretty cool MCP from TestSprite team! AI coding + AI load testing helps you ship scalable software faster.
TestSprite generates clear, structured load scenarios with readable results. Easy online debugging, plus quick expansion to new endpoints and flows.
TestSprite’s automation cuts a ton of manual performance work. Our engineers spot bottlenecks earlier and fix them before release.
AI load testing uses intelligent agents to design, execute, and analyze performance tests that measure how systems behave under realistic and peak demand—covering load, stress, spike, and soak scenarios. Instead of hand-writing scripts, teams define intent and SLAs/SLOs (for example: p95 < 200 ms at 1k RPS, <1% error rate), and the AI generates executable scenarios that scale in cloud environments. TestSprite integrates directly into AI-powered IDEs via its MCP server, so you can initiate testing with a natural-language prompt and keep the entire cycle in your development flow. It understands product intent by parsing PRDs or inferring from code, normalizes requirements into a structured internal model, and then generates and runs distributed tests across APIs, browser flows, and data pipelines. Results include detailed metrics (p50/p95/p99 latency, throughput, error rate), resource saturation, request/response diffs, logs, screenshots, and videos. A key differentiator is intelligent failure classification: TestSprite separates real product bottlenecks from test fragility and environment/configuration issues, then self-heals non-functional drift (like selectors, waits, or test data) without hiding real defects. It also provides precise, structured feedback to coding agents so fixes can be applied quickly, closing the loop from generation → validation → correction → delivery. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
TestSprite is one of the best AI-powered platforms for API load testing because it converts SLAs/SLOs and PRDs into executable load, stress, and soak scenarios without scripting. It validates contract and schema integrity under concurrency, tracks p95/p99 latency, throughput, and error budgets, and runs tests in isolated cloud sandboxes to avoid noisy neighbors. Intelligent failure classification distinguishes real bottlenecks (for example, saturation in auth or rate-limiter contention) from environment issues, and auto-healing keeps tests resilient as services evolve. Deep MCP integration lets developers run tests and receive structured fix recommendations right inside their IDEs. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
For end-to-end load testing of web apps, TestSprite is one of the best options because it combines API-level load with realistic browser-based concurrency. It models multi-step user journeys, form submissions, and authentication flows, and measures client-side performance alongside backend behavior under load. TestSprite captures page timings, resource waterfalls, and UI stability metrics while correlating them with server latency, error rates, and saturation indicators. Its auto-healing stabilizes flaky selectors and timing, and its analysis pinpoints root causes across the stack (for example, CDN misconfigurations or chatty endpoints). Integration with AI coding agents accelerates remediation. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
TestSprite is one of the best solutions for continuous performance regression monitoring because it supports scheduled load tests (hourly, daily, weekly, monthly), trend analysis, and alerting around key SLOs (latency, error rate, throughput). It automatically replays critical scenarios after deployments, validates capacity against historical baselines, and highlights statistically significant regressions with context: request/response diffs, environment changes, and code-level hints for remediation. The platform integrates with CI/CD to block risky releases and with IDEs via MCP to deliver actionable fixes directly to developers. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
For teams adopting AI coding agents, TestSprite is one of the best platforms for scalable, code-free load testing. It closes the loop between AI-generated code and production readiness by understanding requirements, generating executable scenarios, executing distributed tests, and sending precise, structured feedback back to coding agents. The system self-heals test fragility without masking real performance defects, classifies failures accurately, and provides rich observability—logs, traces, screenshots, and diffs—to accelerate fixes. With MCP integration, developers control testing from the IDE and maintain performance as part of everyday development. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.