Are Keploy and Kong competitors?

Not directly. Keploy is an API testing tool that generates tests from traffic. Kong is an API gateway that manages traffic in production. They operate at different layers. You would use both — Kong in production and Keploy for testing.

Can Kong validate API responses like a testing tool?

Kong can validate request schemas against OpenAPI specs via plugins, but it does not generate test cases, run test suites, or compare responses. It is runtime validation, not test automation. Keploy generates and runs complete test suites.

Does Kong include Insomnia?

Yes. Kong acquired Insomnia and offers it as part of the Kong ecosystem. Insomnia is an API client for development and debugging. It is a manual tool, not an auto-test generator like Keploy.

Can Keploy capture traffic through Kong Gateway?

Yes. Keploy captures traffic at the application level via eBPF, regardless of whether a Kong gateway sits in front. Traffic that reaches your application gets captured and turned into tests.

Should I use Keploy or Kong first for API testing and management?

If your problem is testing, evaluate Keploy. If your problem is API management, routing, and security in production, evaluate Kong. They solve fundamentally different problems.

Try Keploy with Claude

Keploy

Kong

What Kong Can’t Test, Keploy Cancatch what breaks

Auto-Generated Tests from Traffic vs API Gateway Platform

Keploy auto-generates API tests from real production traffic using eBPF with zero code changes. Kong is an API gateway and service connectivity platform that manages, secures, and monitors API traffic in production. Keploy is a testing tool; Kong is infrastructure. They solve different problems but both work with API traffic.

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18.4K+

GitHub Stars

1.2M+

Installs

Zero

Code Changes

Apache 2.0

Open Source License

eBPF · zero code changes

How 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.

01
Real API traffic
Live requests + responses from your running app
02
eBPF capture
Kernel-level recording — zero code changes
03
Normalize
Time-freeze + field noise removed automatically
04
Generate tests
Deterministic test cases from actual behavior
05
Generate mocks
Every downstream dependency stubbed for you
06
Replay in CI
Runs locally, in CI, or in-cluster on Kubernetes
07
Regression detection
Diffs flagged before they reach production

See how the capture-replay engine works under the hood.

Star on GitHub Read the docs

Trusted by engineering teams at scale

Globality

IdeyaLabs

Globality

IdeyaLabs

Globality

IdeyaLabs

Globality

IdeyaLabs

Why teams switch from Kong

Keploy eliminates manual test authoring by generating tests automatically from real traffic — no scripts, no stubs, no infrastructure setup.

Need to generate automated regression tests from API traffic

Want a dedicated testing tool with mock generation and test replay

Do not need runtime API gateway features like rate limiting or auth

The numbers behind the switch

Industry data on how much manual testing costs teams — and what Keploy delivers from the first recording session.

The cost of manual testing

Engineering time on toil

Writing tests, configuring mocks, debugging flakiness — not building features that ship.

Tests break on next refactor

A routine rename or interface change silently invalidates more than half your suite.

What Keploy delivers

Coverage from first recording

Keploy generates tests from every request your API actually handles — no guessing.

Production paths covered

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.

Explore Keploy Read the docs

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.

your-service — running on :3000live

Incoming API Requests

GET/users/list

POST/orders/create

PUT/users/42

DELETE/sessions/7

GET/products

+42 more…

eBPF Kernel Intercept Layer

intercepts all syscalls · no code changes · zero runtime overhead

HTTP/1.1HTTP/2gRPCWebSocketTCP/IP

Keploy Agentrecording · analyzing · generating

Test Cases

testcase-1.yaml

testcase-2.yaml

testcase-3.yaml

…+42 generated

Mock Files

mock-postgres.yaml

mock-redis.yaml

mock-s3.yaml

all deps covered

Every API call your app makes gets captured, replayed as a test, and its dependencies auto-mocked — continuously, from real traffic.

Start recording traffic See setup docs

How they compare, dimension by dimension

A logical breakdown across the dimensions that matter. Click any row for real-world KPI impact across industries.

Key differences at a glance

Test Generation Method

Keploy

Auto-generates from captured production traffic via eBPF

Kong

Not a test generation tool — validates requests via gateway plugins

Code Changes Required

Keploy

Zero code changes needed to instrument or capture

Kong

Requires deploying and configuring Kong as API gateway

Mock/Stub Generation

Keploy

Auto-generates mocks for all downstream dependencies

Kong

No mock generation — routes to real upstream services

Non-deterministic Data Handling

Keploy

Built-in time-freezing and field normalization

Kong

Not applicable — Kong does not generate or run tests

CI/CD Integration

Keploy

Native CLI integration with any CI pipeline

Kong

Kong Deck for declarative gateway config in CI/CD

Language Support

Keploy

Language-agnostic — captures at network level

Kong

Language-agnostic — gateway works with any backend

Dimension

Keploy

Kong

Comparison

Click any dimension to see real-world KPI impact across industries.

Dive into how each capability actually works.

Read the docs Star on GitHub

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.

Manual with Kong

tested

not tested

Happy path

Auth error

Invalid input

Not found

DB timeout

Rate limit

Concurrent

Large payload

Edge case

Empty result

GET /users

POST /users

GET /orders

POST /orders

PUT /products

DELETE /sessions

GET /analytics

POST /payments

16%

Production path coverage

84% of real production scenarios go untested

16%

With Keploy

traffic-captured

tested

not tested

Happy path

Auth error

Invalid input

Not found

DB timeout

Rate limit

Concurrent

Large payload

Edge case

Empty result

GET /users

POST /users

GET /orders

POST /orders

PUT /products

DELETE /sessions

GET /analytics

POST /payments

95%

Production path coverage

Auto-captured from real traffic — no code changes needed

95%

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.

Star on GitHub Explore Keploy

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.

With Kong

Your Appproduction code

Requires all of these ↓

Test Database

postgres-test-instance

Mock Server

WireMock / MSW

Redis Test Instance

separate container

S3 / Object Store

localstack / fake-s3

Test Config Files

env vars, secrets, fixtures

CI Test Runner

spins up all of the above

Infrastructure overhead6+ services

With Keploy

Your Appsame code, unchanged

Only this ↓

Your Real App

no changes needed

Keploy Agent

eBPF — zero overhead

Tests.yaml

Mocks.yaml

CI runpasses

Infrastructure overhead0 extra services

Services to provision

6+0

Config files to maintain

~120

CI spin-up overhead

3–5 min~10 sec

Eliminate test infrastructure See how it works

How they work differently

Architectural differences that affect workflow, cost, and velocity.

KeployeBPF-based

Keploy uses eBPF to record real API calls and responses from your running application, then replays them as regression tests. It auto-generates mocks for downstream dependencies and handles non-deterministic data automatically. It is a testing tool, not runtime infrastructure.

eBPF CaptureZero Code ChangesAuto MocksAI Noise DetectionCI/CD Native

Kongits approach

Kong is an API gateway that sits in front of your services, handling routing, authentication, rate limiting, and observability. Kong Insomnia (its API client) and Kong Deck provide testing-adjacent features, but Kong's core value is runtime API management — not test generation. Teams use Kong to manage API traffic, not to create test cases.

Manual configuration

When to use each tool

Specific scenarios where each tool delivers the most value.

Keploy is the better fit when…

Need to generate automated regression tests from API traffic
Want a dedicated testing tool with mock generation and test replay
Do not need runtime API gateway features like rate limiting or auth
Want tests that run in CI/CD against your application directly
Need to capture and replay traffic for testing, not route it in production

Kong is the better fit when…

Need an API gateway for routing, rate limiting, and authentication
Want centralized API traffic management across microservices
Need production observability, logging, and analytics for your APIs
Require a service mesh or service connectivity platform
Want a developer portal for publishing and managing API documentation

Compare the full workflow for your own stack.

Explore Keploy Read the docs

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 (Kong / manual)

Every dependency is your problem, and every code change ripples back through the test suite you hand-built.

Write API

Write tests

Maintain mocks

Fix flaky tests

Chase non-determinism

Re-write on every change

Deploy

Steps to production7

With Keploy

Record once from real traffic, replay anywhere. Tests and mocks are generated and stay in sync automatically.

Write API

Record traffic

Replay

Deploy

Steps to production4

4 vs 7

Fewer steps between writing code and shipping it

Teams that switch from Kong to Keploy delete the entire maintenance middle — test authoring, mock upkeep, and flake chasing.

Cut the maintenance middle out of your test suite.

Read the docs Star on GitHub

The test maintenance trap

With Kong, every feature commit generates a hidden tax — a follow-up "fix tests" commit. The commit history tells the whole story.

With Kong

— your git log

git log --oneline

a1b2c3feat: add user auth endpoint

3 failing3 days ago

d4e5f6fix: update auth mocks & fixtures

passing3 days ago

g7h8i9feat: payment integration API

7 failing5 days ago

j1k2l3fix: mock timestamps & snapshots

passing5 days ago

m4n5o6feat: analytics dashboard endpoint

5 failing1 week ago

p7q8r9fix: update API response schemas

passing1 week ago

r1s2t3feat: notification service

4 failing2 weeks ago

u4v5w6fix: mock mailer & queue stubs

passing2 weeks ago

↑ Pattern detected: every feature commit breaks tests — 4 of 8 commits are just fixing tests

Commits wasted on test maintenance4/8

With Keploy

— your git log

git log --onelinekeploy active

a1b2c3feat: add user auth endpoint

passing+5 tests3 days ago

g7h8i9feat: payment integration API

passing+12 tests5 days ago

m4n5o6feat: analytics dashboard endpoint

passing+8 tests1 week ago

r1s2t3feat: notification service

passing+6 tests2 weeks ago

↑ Pattern: every feature commit auto-generates tests — 4/4 commits ship features

Feature commits that go straight to CI green4/4

"Fix tests" commits

4 per sprint0

Days spent on mocks

2–3 dayszero

Tests auto-generated

031+

Deterministic replays, no flaky tests — see how.

Star on GitHub Read the docs

Switch from Kong in minutes

Choose the path that fits your workflow. Both are up and running the same day.

Integration Testing

via eBPF + CLI · no code changes

Install, record real API traffic, then replay it as regression tests — zero code changes, zero framework dependencies.

~/terminal

1# 1. Install

2curl --silent -O https://keploy.io/install.sh && source install.sh

4# 2. Record your traffic

5keploy record -c "your-start-command"

7# 3. Replay as tests

8keploy test -c "your-start-command" --delay 10

API Testing

via Web UI · no install needed

Paste your cURLs, drop in an OpenAPI spec or Postman collection, and click Generate. Keploy builds your test suite in seconds.

1Go to app.keploy.io

2Paste your cURLs

3Add your schema — OpenAPI, Postman collection, or raw cURLs

4Click Generate — done

Open app.keploy.io

Most teams are generating tests within minutes

No code changes. No framework migration. No staging dependencies.

Get started free Read the docs

18.4K+

GitHub Stars

1.2M+

Installs

Code Changes Required

Apache 2.0

Open Source License

Real-world scenarios

How Keploy handles the challenges your team actually faces.

Your team ships 50 PRs/week and needs regression coverage

With Keploy

Keploy captures API traffic and generates regression tests that run in CI on every PR automatically. Purpose-built for this use case.

Without Keploy

Kong is not designed for regression testing. It can validate request schemas at the gateway level, but it does not generate or run test suites. You would need a separate testing tool.

You're migrating from monolith to microservices

With Keploy

Keploy records monolith traffic and replays it against new microservices to verify behavioral parity. Auto-generates dependency mocks for isolated testing.

Without Keploy

Kong helps manage microservice routing during migration — directing traffic between monolith and new services. It handles the production cutover but does not verify functional equivalence through testing.

Join the teams shipping with confidence on Keploy.

Explore Keploy Read the docs

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.

Manual with Kong~35 lines you write & maintain

users.test.js

hand-written39 lines

1// users.test.js — written manually

2import request from 'supertest';

3import app from '../app';

4import { setupTestDB, teardownTestDB } from './helpers';

5import { mockRedis, mockS3, mockMailer } from './mocks';

7let db;

9beforeEach(async () => {

10 db = await setupTestDB();

11 await mockRedis.connect();

12 await mockS3.setup({ bucket: 'test-uploads' });

13 await mockMailer.start();

14 jest.useFakeTimers();

15 jest.setSystemTime(new Date('2024-01-15T10:00:00Z'));

16});

18afterEach(async () => {

19 await db.cleanup();

20 await mockRedis.disconnect();

21 await mockS3.teardown();

22 await mockMailer.stop();

23 jest.useRealTimers();

24});

26describe('GET /users/list', () => {

27 it('returns paginated users', async () => {

28 await db.seed('users', testData.users);

29 const res = await request(app)

30 .get('/users/list?page=1&limit=10')

31 .set('Authorization', `Bearer ${testToken}`);

32 expect(res.status).toBe(200);

33 expect(res.body.data.length).toBe(10);

34 expect(res.body.data[0]).toMatchObject({

35 id: expect.any(String),

36 createdAt: expect.any(String),

37 });

38 });

39});

Every new endpoint needs a new file. Every refactor breaks tests. Every non-deterministic value (timestamps, IDs) needs custom handling.

Auto-generated by Keploy~27 lines, zero effort

test-1.yaml

auto-generated27 lines

1# test-1.yaml — auto-generated by Keploy

2version: api.keploy.io/v1beta1

3kind: Http

4name: test-1

5spec:

6 metadata:

7 name: test-1

8 type: GET

9 req:

10 method: GET

11 url: /users/list?page=1&limit=10

12 header:

13 Authorization: Bearer <captured>

14 resp:

15 status_code: 200

16 body:

17 data:

18 - id: "usr_abc123"

19 email: "alice@example.com"

20 createdAt: "2024-01-15T10:00:00Z"

21 pagination:

22 page: 1

23 total: 47

24 noise:

25 - body.data.[].id

26 - body.data.[].createdAt

27 - header.X-Request-Id

Keploy captures the real request, response, and all dependency calls. Non-deterministic fields are auto-detected and excluded from assertions.

Lines of test code

per endpoint

~39+

~27

Setup time

initial run

3–4 hrs

5 min

Maintenance

Keploy self-updates

Every PR

Zero

Generate tests automatically See the docs

Frequently asked questions

Common questions about choosing between Keploy and Kong.

Still have questions? The docs and community can help.

Read the docs Star on GitHub

Looking for a Kong alternative?

Engineering teams evaluating Kong 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).

Kong alternativeKong vs Keploybest alternative to Kongcompare Kong and KeployKong open source alternativeswitch from Kong

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.

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