What JMeter Can’t Test, Keploy Cancatch what breaks
Auto-Generated Functional Regression Tests vs Load & Performance Testing
Keploy auto-generates functional API and integration tests by capturing real traffic with eBPF, requiring zero code changes. Apache JMeter is a Java-based open-source tool for load and performance testing that simulates many concurrent users to measure throughput, latency, and stability under stress. They answer different questions: Keploy asks whether behavior is still correct, while JMeter asks whether the system holds up under load. Teams often use both.
eBPF · zero code changesHow Keploy turns real traffic into a test suite
One pipeline, running the moment your app receives a request. No SDKs, no sidecars, no test scripts — just kernel-level capture that becomes deterministic regression coverage.
- 01Real API trafficLive requests + responses from your running app
- 02eBPF captureKernel-level recording — zero code changes
- 03NormalizeTime-freeze + field noise removed automatically
- 04Generate testsDeterministic test cases from actual behavior
- 05Generate mocksEvery downstream dependency stubbed for you
- 06Replay in CIRuns locally, in CI, or in-cluster on Kubernetes
- 07Regression detectionDiffs flagged before they reach production
See how the capture-replay engine works under the hood.
Trusted by engineering teams at scale
Why teams switch from JMeter
Keploy eliminates manual test authoring by generating tests automatically from real traffic — no scripts, no stubs, no infrastructure setup.
Want functional API regression tests generated from real traffic without writing code
Need automatic mock generation for databases and downstream services
Your primary question is whether responses are still correct after a change
The numbers behind the switch
Industry data on how much manual testing costs teams — and what Keploy delivers from the first recording session.
Writing tests, configuring mocks, debugging flakiness — not building features that ship.
A routine rename or interface change silently invalidates more than half your suite.
Keploy generates tests from every request your API actually handles — no guessing.
Traffic capture reaches edge cases, error paths, and concurrent requests no dev would write.
Pain stats sourced from developer productivity surveys. Coverage stats from Keploy production recording sessions across 50+ engineering teams.
Every number here comes from teams running Keploy in CI.
Zero code. Real tests. Automatically.
Keploy's eBPF agent intercepts every API call at the kernel level and turns live traffic into test cases with dependency mocks — no SDK, no sidecars, no annotations.
Incoming API Requests
Keploy Agentrecording · analyzing · generatingEvery API call your app makes gets captured, replayed as a test, and its dependencies auto-mocked — continuously, from real traffic.
How they compare, dimension by dimension
A logical breakdown across the dimensions that matter. Click any row for real-world KPI impact across industries.
Functional correctness and regression coverage
Load, performance, and scalability under concurrent users
Auto-generates from captured production traffic via eBPF
Manual test plans built from thread groups and samplers
Zero code changes needed to instrument or capture
No app changes but requires building and tuning test plans
Auto-generates mocks for all downstream dependencies
No mocking; drives real load against real endpoints
Built-in time-freezing and field normalization
Response assertions and correlation configured manually
Replays captured requests for correctness, not load
Thread-based virtual users simulate heavy concurrent load
KeployClick any dimension to see real-world KPI impact across industries.
Dive into how each capability actually works.
Your tests miss more than you think
Manual tests cover paths developers remember to write — usually just the happy path. Keploy captures every pattern production traffic actually generates.
Coverage grid shows 8 common endpoints × 10 production scenario types. Manual tests cover only what developers remember to write. Keploy captures every pattern your API actually serves in production.
Keploy is open source — read the code that captures this coverage.
The infrastructure you're maintaining
Traditional testing stacks require a shadow infrastructure to exist alongside your real app. Keploy eliminates all of it — tests and mocks come from actual traffic, not from services you run and maintain.
How they work differently
Architectural differences that affect workflow, cost, and velocity.
KeployeBPF-basedKeploy uses eBPF to record real API calls and responses from your running application, then replays them as functional regression tests. It auto-generates mocks for downstream dependencies and handles non-deterministic fields like timestamps through time-freezing. The goal is correctness — verifying that responses match expected behavior release over release, not measuring performance under load.
JMeter builds test plans of thread groups, samplers, and listeners that generate concurrent virtual-user load against a server, API, or database. Teams configure ramp-up, think times, and assertions, then analyze throughput, response times, and error rates under stress. It can record scenarios from a browser or app and run headless via CLI for CI. The focus is performance and scalability, not functional correctness.
When to use each tool
Specific scenarios where each tool delivers the most value.
Keploy is the better fit when…
- Want functional API regression tests generated from real traffic without writing code
- Need automatic mock generation for databases and downstream services
- Your primary question is whether responses are still correct after a change
- Need to handle non-deterministic data like timestamps and UUIDs automatically
- Want tests that evolve with real traffic rather than hand-built test plans
JMeter is the better fit when…
- Need to measure throughput, latency, and error rates under concurrent load
- Want to find the breaking point and capacity limits of your system
- Require stress, soak, or spike testing before a high-traffic launch
- Need to load test protocols beyond HTTP such as JDBC, JMS, or FTP
- Your goal is performance and scalability validation, not correctness checks
Compare the full workflow for your own stack.
The workflow you're escaping
Same starting point, same finish line. One path is short because Keploy does the tedious middle for you — the other is where teams lose days every sprint.
Without Keploy (JMeter / manual)
Every dependency is your problem, and every code change ripples back through the test suite you hand-built.
With Keploy
Record once from real traffic, replay anywhere. Tests and mocks are generated and stay in sync automatically.
Cut the maintenance middle out of your test suite.
The test maintenance trap
With JMeter, every feature commit generates a hidden tax — a follow-up "fix tests" commit. The commit history tells the whole story.
Deterministic replays, no flaky tests — see how.
Switch from JMeter in minutes
Choose the path that fits your workflow. Both are up and running the same day.
Install, record real API traffic, then replay it as regression tests — zero code changes, zero framework dependencies.
# 1. Installcurl --silent -O https://keploy.io/install.sh && source install.sh# 2. Record your traffickeploy record -c "your-start-command"# 3. Replay as testskeploy test -c "your-start-command" --delay 10Paste your cURLs, drop in an OpenAPI spec or Postman collection, and click Generate. Keploy builds your test suite in seconds.
Real-world scenarios
How Keploy handles the challenges your team actually faces.
You changed an API and need to confirm behavior is unchanged
Keploy replays captured requests against the new build and diffs responses, flagging any behavioral change automatically. This is exactly the correctness check it is designed for, with mocks isolating downstream dependencies.
JMeter can hit the endpoint but is built to measure performance, not diff response bodies. You would have to add manual response assertions, and it still would not tell you whether overall behavior stayed the same.
You're preparing for a Black Friday traffic spike
Keploy verifies functional correctness of the code paths involved but does not generate load, so it cannot tell you how the system behaves under thousands of concurrent users.
JMeter is purpose-built for this: ramp up thousands of virtual users, run spike and soak tests, and measure where latency degrades or errors appear so you can size capacity before the event.
Join the teams shipping with confidence on Keploy.
What you write vs what Keploy writes
The same test coverage — one approach takes hours of setup and ongoing maintenance, the other takes five minutes and zero boilerplate.
Every new endpoint needs a new file. Every refactor breaks tests. Every non-deterministic value (timestamps, IDs) needs custom handling.
Keploy captures the real request, response, and all dependency calls. Non-deterministic fields are auto-detected and excluded from assertions.
Frequently asked questions
Common questions about choosing between Keploy and JMeter.
Still have questions? The docs and community can help.
Looking for a JMeter alternative?
Engineering teams evaluating JMeter alternatives often compare it with Keploy for API testing and regression coverage. Keploy captures real production traffic via eBPF and auto-generates tests with dependency mocks — requiring zero code changes. The key differences come down to how tests are generated (traffic-based vs manual), how dependencies are mocked (automatic vs configured), and what infrastructure changes are needed (none vs SDK/sidecar/containers).
Ready to stop writing tests manually?
Keploy captures your real API traffic and turns it into a regression suite automatically. Zero code changes. Full coverage from day one.