Verify every Pull Request

Traditional tests confirm.
Antithesis discovers.

Mockup of Antithesis' web application for debugging and investigating software bugs

Trusted by Engineering teams building mission-critical systems

why antithesis

Unleash your team.

Antithesis catches unforeseen bugs in minutes, so humans and agents can ship quickly without risking outages.

Prevent outages

By injecting realistic faults and leveraging our simulator’s unique replay capabilities, we explore the execution paths that even years of traditional testing miss.

Skip debugging

Our simulator is 100% deterministic and automatically root causes every bug. Rather than wasting time and tokens chasing flaky heisenbugs, your team can jump straight to a fix.

Ship faster

When every pull request has already survived the Antithesis gauntlet, your team can merge big changes confidently — even when they’re authored by agents.

how to use

4 steps to get started with Antithesis

Outline your invariants

Decide how your system should behave – what it should do or never do. Our agent skills can help with this, or check out our property catalogs for different system types.

Check out our skills

the antithesis platform

Optimized for code verification

Antithesis home dashboard charting findings over time

deterministic hypervisor

Deterministic down to the instruction stream

Our platform’s foundation is a custom deterministic hypervisor that runs your whole system on a single CPU core. We massively parallelize runs and fully utilize every core to get you results faster.

Never fight another flaky test.
Find rare, multi-factorial bugs in minutes.
Instruction-level determinism works with any x86 binary.

Antithesis run report showing properties discovered

intelligent exploration

Years of production experience in minutes

Our feedback-guided exploration algorithm synthesizes unusual input data and injects faults to actively increase test coverage, so no time is wasted blindly repeating the same scenarios. If your system survives our tailored torture regime, it will stay up in production.

Simulate clock, scheduling, network, and storage faults with zero configuration.
Exercise hard-to-reach code paths like exception handling and leader election.
Prioritize exploration of particularly critical code.

Antithesis causality analysis showing a bug's probability over time

debugging superpowers

Perfect reproductions are just the start

Especially in complex systems, identifying bugs is only half the battle: root causing them is often equally hard. Deterministic replay lets Antithesis replace hours of spelunking through logs with automated root cause analysis and interactive debugging.

Get unified, perfectly ordered traces of distributed execution.
Pull custom artifacts from your system, like data files or core dumps.
Edges surfaced in minutes, not months
Suspend execution, rewind time, and analyze your system as bugs occur.

Snouty mock-using the antithesis-launch skill in a terminal

Skills, CLIS, & APIS

Verification in the agentic loop

Our skills let you integrate Antithesis into your harness’s feedback loop, so your agent only declares victory once our platform has verified its output. While you’re just getting started, our skills also help with every step of onboarding. Have agents:

Always produce battle-tested, correct code.
Write Docker Compose or Kubernetes manifests for your system.
Build a precise specification of your system’s expected behavior.
Triage and debug for you.

Book a demo

customer testimonials

Accelerating reliability for elite teams

The way Antithesis tests whole-system scenarios that we either wouldn’t have thought to test is its own kind of magic… we feel like we can tackle more ambitious projects by relying on Antithesis to fill in any gaps along the way.

Doug Patti

Software Engineer, Jane Street

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Engineering output

What would otherwise take six months of testing in production, running the software -- we can get that in an hour.

Clément Salaün

Co-Founder & CTO, Formance

Antithesis helped us find three key bugs in Tigris…These are the bugs that CI can't cover because no one thinks to write them.

Adil Ansari

Founding Engineer, Tigris Data

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6 hr

Better coverage than 100 engineers in one year

Antithesis’s deterministic simulation and active exploration removes the blindfold, enabling a systematic and reproducible search for bugs.

Marek Siarkowicz

Kubernetes & etcd Maintainer

There’s been a transformation in the engineering group. With basically the same group of people, output is now significantly better. Antithesis has given us more confidence in what we’re delivering, lower stress development, more predictability… I look at Antithesis as a way of enabling us to scale, and that is just an enormous deal.

Sean Murphy

Co-Founder & CEO, PingThings

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1 day

Compressed 3 weeks of debugging into one day

With Antithesis, you can make these pretty bold changes that ordinarily you would have to approach so carefully in order to do safely, and you can just kind of write the thing.

Mark Logan

Tech Lead for Sui Core at Mysten

How is Antithesis different from chaos testing?

Like chaos testing, Antithesis uncovers problems by subjecting your system to real-world faults. But unlike chaos testing’s blind fault injection, Antithesis intelligently guides fault injection and input data generation to maximize coverage — so even complex, multi-factorial bugs are uncovered in minutes rather than days or weeks. And because every bug found with Antithesis is perfectly reproducible, debugging is a cinch.

How is Antithesis different from AI code review?

AI code review can be helpful, but it operates on the same information that agents used to write the code in the first place — the code itself, plus some best practices scraped from the internet. Like traditional static analysis, it’s high noise and often misses serious bugs. Antithesis actually runs your whole distributed system, injects faults, and actively searches for buggy behavior. This is a fundamentally different source of information, and it’s high enough precision to serve as an automated quality gate in your agentic coding loop.

How is Antithesis different from the tests I already have?

Traditional tests check that a single code path works as expected. Property-based tests are more powerful, but still limited to checking a handful of happy-path cases on each run. Antithesis actively searches your distributed system’s entire state space for bugs, finding the unknown unknowns that you’d never think to write tests for. And unlike even the most thorough suite of hand-written tests, every run in Antithesis is fully deterministic, so you can effortlessly reproduce every bug perfectly.

How long does onboarding take?

As little as a day, especially when starting with a single critical sub-system. Onboarding comes in two steps: first getting your system running in Antithesis, and then gradually adopting property-based testing. Getting up and running often takes just a few hours, especially when using our agent skills and starting with a working Docker Compose or Kubernetes manifest. Property-based testing can also start very simply, perhaps with an existing integration or end-to-end test. From there, your setup can gradually become more powerful — by expanding to include more related systems, by adding more sophisticated properties, or both.

What languages and stacks does Antithesis support?

Your software must support x86 hardware and run in containers orchestrated by Docker Compose or Kubernetes. Including containerized dependencies like Postgres or Kafka is fine! Our SDKs offer ergonomic ways to define test properties and instrument your system, and we offer SDKs for C, C++, Go, Java, JavaScript, .NET, Python, and Rust. Systems written in other languages can use our low-level fallback API.

What if my system uses Postgres, Kafka, or other dependencies?

Antithesis supports any containerized x86 software, including many common open source dependencies. Postgres, Redis, Kafka, NATS, Ceph, and a long list of other software works well — just reference the image in your Docker Compose or Kubernetes manifest. If your system depends on software that’s not available as a container, you’ll need to use a mock. Our documentation includes a list of ready-to-use mocks for many cloud and SaaS services.

Does Antithesis support multicast networking?

Yes — many of our capital markets customers use Antithesis to test systems that rely on multicast networking. Multicast works out of the box when using Docker Compose manifests with Antithesis. Because Antithesis runs your whole system on a single physical machine, all the complexity of multi-node production multicast setups (such as CNI networking) disappears.

Can I buy Antithesis through the AWS Marketplace?

Yes! We often transact via AWS Marketplace Private Offers, so purchasing Antithesis usually draws down your organization’s committed spend. As always, the details depend on the terms of your particular AWS Private Pricing Agreement.

Does Antithesis offer an on-premise / VPC option?

You can run Antithesis in your own AWS VPC, keeping proprietary binaries and source code fully under your control. In this architecture, Antithesis’s ephemeral execution environments and container image registry run exclusively in your VPC, while the control plane remains in our VPC with customer-isolated, fully single-tenant compute and databases. We do not offer a fully on-premise option at this time.

Is there a free or open-source version of Antithesis?

Sadly, no. Antithesis is compute-intensive by nature, and the infrastructure has to get paid for somehow. However, we do have two open-source tools for property-based testing – the Hegel family of PBT libraries, and Bombadil, a PBT tool for web and terminal UIs. Give them a try! You might also be interested in dhyve, an open-source deterministic hypervisor built on bhyve and inspired by Antithesis.

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