API Testing comparison
Keploy logo
Keploy
vs
Kong logo
Kong

Keploy vs Kong

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.

17K+
GitHub Stars
1M+
Installs
Zero
Code Changes
Apache 2.0
Open Source License

Why teams switch from Kong

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

01

Need to generate automated regression tests from API traffic

02

Want a dedicated testing tool with mock generation and test replay

03

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
0%
Engineering time on toil

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

0%
Tests break on next refactor

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

What Keploy delivers
0%
Coverage from first recording

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

0%
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.

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
KeployKeploy Agent
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.

How They Compare

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

KeployOpen Source · 17K+ Stars

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.

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

How they work differently

Architectural differences that affect workflow, cost, and velocity.

Keploy logoKeployeBPF-based
Keploy auto-generating tests from real trafficLive Demo

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
Kong logo
Kong
Kong manual setup
$ npm install --save-dev jest supertest
$ touch __tests__/users.test.js
# write describe / it / expect blocks...
# configure mock for postgres, redis, s3...
# handle timestamps, UUIDs manually...
$ npx jest --coverage
FAIL __tests__/users.test.js
● Cannot connect to mock DB
● Timestamp mismatch on line 47
# debug, fix, repeat...

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.

When to use each tool

Specific scenarios where each tool delivers the most value.

Keploy

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 logo

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

The workflow you're escaping

Every step you write manually is a step Keploy can eliminate. The difference isn't just time — it's the feedback loop that determines how fast your team ships.

Manual testing (Kong)
Write your API
business logic only
~2 hrs
Write test scaffolding
per endpoint, by hand
~3 hrs
Configure mock servers
for db, cache, queues…
~4 hrs
Handle non-determinism
timestamps, UUIDs, tokens
~2 hrs
Debug flaky tests
race conditions, env drift
~1 day
Maintain on every change
refactor = rewrite tests
ongoing
Ship… eventually
after manual QA sign-off
2–3 days
Time to first regression suite2–3 days
With Keploy
Write your API
business logic only
~2 hrs
keploy record
run your app once
5 min
Tests generated
from real traffic
instant
Mocks generated
every dependency covered
instant
Ship to CI/CD
full regression coverage
today
Time to first regression suite~2 hrs
10×
Faster path from API to full regression coverage
Teams that switch from Kong to Keploy reclaim 2–3 engineering days per sprint that used to go to test maintenance.

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+

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
3
4# 2. Record your traffic
5keploy record -c "your-start-command"
6
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
Most teams are generating tests within minutes
No code changes. No framework migration. No staging dependencies.
17K+
GitHub Stars
1M+
Installs
0
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.

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-written
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';
6 
7let db;
8 
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});
17 
18afterEach(async () => {
19 await db.cleanup();
20 await mockRedis.disconnect();
21 await mockS3.teardown();
22 await mockMailer.stop();
23 jest.useRealTimers();
24});
25 
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-generated
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

Frequently asked questions

Common questions about choosing between Keploy and Kong.

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.