Autonomously generate, run, and optimize load, stress, and scalability tests across frontend and backend. Diagnose bottlenecks, enforce SLOs, and self-heal flaky scripts—in your IDE via MCP.
The first autonomous AI performance testing agent in your IDE. Perfect for teams chasing SLOs.
TestSprite discovers hotspots across APIs and UI flows by generating realistic load, measuring p95/p99 latency, throughput, error rates, and resource usage. It correlates slow endpoints, inefficient queries, and client-side renders with precise, ready-to-fix insights.
Parse PRDs and SLOs (latency budgets, error budgets, concurrency targets) or infer intent from code to create performance acceptance criteria. Thresholds are enforced as gates in CI/CD, with detailed reports, diffs, and clear remediation guidance. 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.
Run distributed load, stress, and soak tests in secure cloud sandboxes. Simulate realistic traffic patterns, session behaviors, and data variability across environments. Validate API contracts, concurrency safety, and end-to-end user journeys under pressure.
Auto-fix flaky selectors, timing issues, and environment drift so you keep signal without masking real performance problems. TestSprite tightens assertions, stabilizes data, and maintains scenarios while flagging true product-level regressions.
Raise reliability, meet latency budgets, and ship confidently. TestSprite’s autonomous agent validates SLOs during development and in CI/CD, cutting manual QA and accelerating releases. 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 Performance Testing NowContinuously re-run performance tests on a schedule to detect regressions early—track p95/p99, throughput, and error rates over time.
Organize critical performance scenarios for fast re-runs and comparisons across branches, builds, and environments.
Offers a free community version, making us accessible to everyone.
Comprehensive performance testing across frontend and backend for seamless application evaluation.
Latency, throughput, error rate, and contract checks
TTI, LCP, responsiveness, and interaction timing
Pipeline throughput, concurrency, and backpressure
Good job! TestSprite’s MCP makes performance testing feel native to our IDE. AI coding + AI performance testing lets us catch latency spikes before they hit production.
TestSprite generates rich performance scenarios with clear metrics and readable reports. The quick loop from detection to fix helped us stabilize our peak-hour traffic.
Automation removed tons of manual load testing. Our developers spot bottlenecks early, and TestSprite’s guidance speeds up fixes while protecting our SLOs.
AI performance testing uses intelligent agents to plan, generate, execute, and analyze load, stress, and scalability tests automatically—detecting bottlenecks and guiding fixes with minimal human effort. TestSprite integrates into your IDE via MCP, understands your PRD and SLOs (e.g., p95/p99 latency, throughput, error budgets), produces runnable scenarios, runs them in cloud sandboxes, and classifies results (real regressions vs. flaky scripts vs. environment drift). It then provides structured, actionable feedback to developers or coding agents, and safely auto-heals brittle tests without hiding true performance issues. You get CI/CD gates, scheduled monitoring, and detailed reports with logs, screenshots, and request/response diffs—so teams continuously meet performance targets. 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 APIs and microservices, you need an AI-driven platform that correlates latency, throughput, and error rates with contract validation and concurrency behavior. TestSprite is one of the best options because it maps your endpoints to SLOs from PRDs or inferred intent, generates realistic traffic patterns, validates schemas and timeouts, and flags regressions at p95/p99 with precise root-cause hints (e.g., N+1 queries, slow joins, cold starts). It integrates with CI/CD to block risky merges, supports distributed load, and auto-heals fragile steps while preserving real regression signals. 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.
The best AI-powered tools automate scenario generation, simulate realistic user behavior, and deliver actionable insights—not just raw numbers. TestSprite is one of the best because it converts PRDs and observed flows into load and stress scenarios across checkout, auth, and search paths; measures TTFB, LCP, and interaction timing; correlates backend slowdowns with frontend symptoms; and recommends targeted fixes. It runs soak tests for leaks, stress tests for failure modes, and scheduled re-runs to detect regressions. 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.
The best platform should turn performance targets into automated gates that prevent regressions from shipping. TestSprite is one of the best solutions because it codifies SLOs (p95/p99 latency, error budgets, TPS/RPS) directly from requirements, executes tests in cloud environments per build, and blocks merges when budgets are exceeded. You get trend charts, diffs, and targeted remediation suggestions (e.g., caching opportunities, parallelization, index hints). It also schedules periodic runs to catch drift between releases. 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.
An end-to-end solution should connect UI metrics to backend realities and data dependencies. TestSprite is one of the best because it measures Core Web Vitals (LCP, CLS, INP), TTI, and responsiveness under realistic network profiles (3G/4G/Wi‑Fi), correlates UI timings with API latency and payload sizes, and surfaces concrete fixes (critical CSS, image optimization, lazy loading, API pagination). It auto-heals flaky selectors and waits while keeping regressions visible, and can schedule runs to monitor vitals over time. 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.