How does Keploy's eBPF capture compare to Speedscale's sidecar approach?

Keploy uses eBPF to capture traffic at the kernel level, requiring no sidecars, proxies, or code changes. Speedscale uses Kubernetes sidecars to intercept traffic. Keploy's approach works in any environment while Speedscale is optimized for Kubernetes specifically.

Can Keploy do load testing like Speedscale?

Keploy focuses on functional API testing and regression detection rather than load testing. Speedscale can replay captured traffic at higher volumes for performance testing. If you need both, you can use Keploy for functional tests and a dedicated load testing tool.

Which tool is better for non-Kubernetes environments?

Keploy works in any environment — bare metal, VMs, containers, or Kubernetes — thanks to eBPF-based capture. Speedscale is designed specifically for Kubernetes. If your services run outside K8s, Keploy is the clear choice.

Is Keploy really free while Speedscale is paid?

Yes. Keploy is fully open source under Apache 2.0 and can be self-hosted at no cost. Speedscale is a commercial SaaS product with usage-based pricing. Keploy also offers enterprise support options for organizations that need them.

Which tool handles non-deterministic data better?

Keploy has built-in time-freezing and normalization to handle timestamps, UUIDs, and other non-deterministic fields automatically. Speedscale provides assertion customization and field-level ignoring. Keploy's approach is more automated with less manual configuration needed.

Try Keploy with Claude

Keploy

Speedscale

What Speedscale Can’t Test, Keploy Cancatch what breaks

Kubernetes Traffic Replay Testing Platform

Keploy and Speedscale both capture real traffic for test generation, but differ significantly in approach. Keploy uses eBPF for zero-code capture and is open source under Apache 2.0. Speedscale focuses on Kubernetes-native traffic replay with built-in load testing capabilities and is a commercial SaaS platform.

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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 Speedscale

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

You want open-source, self-hosted testing under Apache 2.0 licensing

You need eBPF-based capture that works beyond Kubernetes environments

Your team wants zero-code-change setup without sidecars or proxies

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

eBPF-based traffic capture, zero instrumentation

Speedscale

Kubernetes sidecar and service mesh capture

Code Changes Required

Keploy

Zero code changes, no sidecars needed

Speedscale

Requires sidecar injection in Kubernetes pods

Mock/Stub Generation

Keploy

Auto-generates mocks for all dependencies

Speedscale

Simulates backend dependencies from recorded traffic

CI/CD Integration

Keploy

Native CLI, Docker, GitHub Actions support

Speedscale

Kubernetes-native CI/CD with operator pattern

Language Support

Keploy

Go, Java, Node.js, Python and more

Speedscale

Language-agnostic via traffic capture layer

Open Source

Keploy

Apache 2.0, fully open source, 20K+ stars

Speedscale

Proprietary commercial SaaS platform

Dimension

Keploy

Speedscale

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 Speedscale

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 Speedscale

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 capture API traffic at the kernel level without any code changes or sidecars. It generates integration tests with auto-mocked dependencies and handles non-deterministic data through time-freezing and normalization. Fully open source and self-hostable.

eBPF CaptureZero Code ChangesAuto MocksAI Noise DetectionCI/CD Native

Speedscaleits approach

Speedscale captures traffic in Kubernetes environments using sidecars and service mesh integration. It replays traffic for both functional testing and load testing scenarios. The platform is Kubernetes-native with a managed SaaS control plane and focuses on microservice environments.

Load / performanceContainers / infraManaged / SaaS

When to use each tool

Specific scenarios where each tool delivers the most value.

Keploy is the better fit when…

You want open-source, self-hosted testing under Apache 2.0 licensing
You need eBPF-based capture that works beyond Kubernetes environments
Your team wants zero-code-change setup without sidecars or proxies
You need automatic handling of non-deterministic data like timestamps
You prefer a lightweight CLI-based tool with broad language support

Speedscale is the better fit when…

Your infrastructure is exclusively Kubernetes and you want native integration
You need combined functional testing and load testing from captured traffic
Your team wants a managed SaaS platform with enterprise support included
You need traffic replay at scale with built-in performance benchmarking
You want detailed Kubernetes-specific observability during test execution

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

With Speedscale

— 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 Speedscale 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.

Microservice Regression Testing

With Keploy

Keploy captures inter-service API calls via eBPF and replays them with auto-mocked dependencies. No sidecars or Kubernetes operators are required, and it works in any deployment environment.

Without Keploy

Speedscale captures traffic between Kubernetes services using sidecars and replays it in isolated test environments. It provides Kubernetes-native workflows but requires a K8s deployment for both capture and replay.

Pre-Production Validation

With Keploy

Keploy replays recorded traffic against new builds in CI pipelines, comparing responses to detect regressions. Time-freezing ensures non-deterministic fields do not cause false failures.

Without Keploy

Speedscale replays captured traffic at configurable load levels in Kubernetes staging environments. It combines functional correctness checks with performance baseline validation in a single replay.

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 Speedscale~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 Speedscale.

Still have questions? The docs and community can help.

Read the docs Star on GitHub

Looking for a Speedscale alternative?

Engineering teams evaluating Speedscale 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).

Speedscale alternativeSpeedscale vs Keploybest alternative to Speedscalecompare Speedscale and KeploySpeedscale open source alternativeswitch from Speedscale

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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