Sold by: Antithesis
Deployed on AWS
Build faster on AWS with Antithesis, which verifies the code of your entire system. Antithesis autonomously explores realistic faults and unexpected execution paths before production, with perfectly reproducible failures for fast debugging.
4.2
Overview
Build Faster on AWS. Verify Your Complete System. Ship with Confidence.
AWS provides the cloud infrastructure and services to build and scale. The Antithesis adds a verification loop: it runs your complete system in a deterministic simulator, explores realistic faults and unexpected execution paths, and produces perfectly reproducible failures for fast debugging.
AI-assisted development accelerates code creation, but teams still need confidence in what to merge and ship. Unit tests, integration tests, AI-generated tests, code review, observability, and chaos exercises remain useful. Antithesis complements them by exploring system interactions and failure conditions before production.
The most expensive failures often emerge from rare interactions among concurrency, timing, state, dependencies, user behavior, and infrastructure faults. Customers need a way to test the complete system before production and turn complex failures into actionable, reproducible engineering work.
How It Works
Provide your system as container images, including dependencies, workloads, and correctness properties. Antithesis runs your services, databases, queues, and dependencies together in a deterministic simulator, combining:
- Property-Based Testing: Define how your system should behave. Antithesis explores inputs and execution paths to check those correctness properties across the complete system.
- Deterministic Simulation: Replay and inspect every failure Antithesis finds with perfect reproducibility, including race conditions, deadlocks, data corruption, and invariant violations.
- Realistic Fault Injection: Vary network conditions, storage behavior, process state, and infrastructure faults alongside inputs, timing, and thread scheduling to uncover failures your team did not anticipate.
Feedback-guided exploration focuses compute on new behaviors and code paths. Your team gets actionable debugging evidence for failures involving services, state, concurrency, and dependencies, including interactions it did not think to test.
Verification in Your Development Workflow
Trigger testing from CI, webhooks, or the Antithesis interface to verify changes during development. Antithesis autonomously explores your system and delivers reproducible findings your engineers can investigate and fix. Explore fully managed SaaS and customer-VPC deployment options with the Antithesis team to match your workload and operating requirements.
Who Uses Antithesis
Companies building complex distributed systems rely on Antithesis to secure their releases and safeguard reliability:
- MongoDB, Confluent, Ramp, Coinbase, Jane Street, Datadog, Supabase, Replit
These organizations trust Antithesis to find critical bugs in their distributed systems before those bugs reach production, protecting both their customers and their reputations.
Key Benefits
- Reduce outages: Find correctness and resilience issues across your complete system before customers encounter them, including race conditions, data corruption, and failures under network partitions.
- Shorten debugging cycles: Replace intermittent, hard-to-reproduce failures with deterministic replay and actionable evidence, so engineers can focus on fixing the underlying issue.
- Explore beyond known test cases: Use correctness properties and feedback-guided exploration to investigate behaviors and interactions your team did not explicitly model.
- Ship changes with confidence: Give engineers and AI-assisted development workflows a verification loop that checks system behavior before code reaches production.
Getting Started
Find the bugs before your customers do. Bring a high-risk workload to the Antithesis team to scope a technical evaluation and explore SaaS or customer-VPC deployment. Buy through AWS Marketplace, with private offers where applicable and subject to current account and program terms. See this listing's pricing tab for pricing details.
Highlights
- Verify Your Complete System: Run your services, databases, queues, and dependencies together in a deterministic simulator. Explore realistic faults and unexpected execution paths to find correctness and resilience issues before production.
- Reproduce Every Finding: Replay and inspect every failure Antithesis finds with perfect reproducibility. Give engineers actionable debugging evidence for race conditions, deadlocks, data corruption, and other system-level failures.
- Build and Ship Faster on AWS: Put verification into development with CI, webhooks, or the Antithesis interface. Explore SaaS and customer-VPC deployment options, with procurement through AWS Marketplace.
Details
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Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
Base Annual Pricing | Annualized base pricing commit for single core hour. Reach out for a customized private offer | $17,520.00 |
Dimensions summary
This listing uses a single contract dimension: an annualized base commitment priced per single core hour. Your cost scales with core-hours because the platform runs your system on CPU cores and parallelizes work across them. There are no separate tiers or instance sizes to choose from. Pricing is set through a customized private offer, so you reach out to arrange terms rather than selecting from preset packages. Purchases often run through AWS Marketplace Private Offers, which can draw down your organization's committed AWS spend depending on your private pricing agreement.
Top-of-mind questions for buyers
A core hour is one CPU core running for one hour. The platform runs your system on CPU cores and parallelizes work across many of them. More cores used, or longer run time, means more core hours. Your commitment is priced per single core hour.
Cost is tied to core hours consumed while your system runs in the simulation environment. When no tests are running, no core hours accrue. You draw down against your annualized commitment as you run tests, so idle periods do not add core-hour usage.
Your software must support x86 hardware and run in containers orchestrated by Docker Compose or Kubernetes. Containerized dependencies like databases and message queues work as well. Systems in other languages can use the low-level fallback API. These requirements shape the workloads that consume your core hours.
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Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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Customers can reach our Support Team via email at support@antithesis.com .
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Customer reviews
Mohit N.
Eliminates Flaky Incidents with Repeatable Chaos Testing
Reviewed on Nov 07, 2026
Review provided by G2
What do you like best about the product?
What stand out most is the confidence it provide prior to production releases. Running workload inside there deterministic hypervisor catch es deep state machine
What do you dislike about the product?
The onboarding and initial configuration can take substantial engineering effort, especially containerizing multi-service setups and instrumenting custom assertions. It also requires careful handling or mocking for workloads that have external third-party network dependencies
What problems is the product solving and how is that benefiting you?
It solves the problem of subtle, non-deterministic bugs and race conditions slipping into production. By providing a deterministic simulation environment with fault injection, it eliminates the guesswork and engineering toil typically spent attempting to reproduce flaky incidents. This directly benefits our team by protecting production reliability, accelerating release velocity, and significantly lowering mean-time-to-resolution (MTTR).
Suman S.
Autonomous Continuous Testing That Keeps Trying to Break Our System
Reviewed on Oct 30, 2026
Review provided by G2
What do you like best about the product?
No doubt, one of the best features of this application is its autonomous, continuous testing. Instead of engineers manually writing endless unit tests or integration test scripts, Antithesis works as an autonomous testing engine. I can deploy it across our entire system, including dependencies like the database or message queues, in our environment, and the platform continuously tries to break it. That’s the part I like most.
What do you dislike about the product?
Generally, I don’t have any major issues, but there is one problem that really bothers a lot of our engineers: the setup complexity and the steep learning curve. To get the full value from this particular application, developers have to package their entire stack—including micro servers, the database, network layers, and background workers—into a deterministic runtime environment. On top of that, writing global invariants is difficult as well.
What problems is the product solving and how is that benefiting you?
Generally, it helps us in a very structured way. It breaks things down step by step, and it solves the problem of finding deep bugs in distributed software, for example. First, it packages our system through containerization. It packages our software components such as micro servers and the database. Step two is that it defines invariants. For example, two nodes can never believe they are the primary leader at the same time, or no committed financial transaction can ever disappear. After that, it generally puts its resources for deployment into a deterministic version, which focuses on controlling non-determinism and eliminating flakiness. In step four, it automates exploration and chaos injection. In step five, it detects an invariant violation. Finally, in step six, it identifies the flaws, reports them, and supports debugging.
Ranganath C.
Antithesis Makes Testing Easier and Boosts Confidence in App Quality
Reviewed on Oct 29, 2026
Review provided by G2
What do you like best about the product?
I have had a good experience with Antithesis so far. It helps make testing easier by identifying issues that might be difficult to catch through regular testing. I like the way it helps find problems without needing to manually check every possible scenario. It can save time and give more confidence in the overall quality of the application.
What do you dislike about the product?
Initially, it can take some time to understand how everything works and to set up the testing environment. The interface and reports could also be a bit easier for new users to follow and interpret. Aside from that initial learning curve, my overall experience has been good.
What problems is the product solving and how is that benefiting you?
Antithesis helps solve the challenge of finding bugs that are hard to catch with regular testing. It tests different scenarios automatically and helps identify issues before they affect users. This saves time on manual debugging and makes it easier for the team to improve application reliability and release changes with more confidence.
Rahul J.
Reliable Automated Testing with Powerful Bug Detection
Reviewed on Oct 29, 2026
Review provided by G2
What do you like best about the product?
I like Antithesis because it helps identify bugs and improve software reliability through automated testing. It reduces manual testing effort, makes it easier to reproduce issues, and helps teams deliver more reliable software with greater confidence.
What do you dislike about the product?
Antithesis can have a learning curve for new users, and the initial setup may take some time, especially for teams unfamiliar with deterministic simulation testing. The interface and documentation could be more beginner-friendly, with more practical examples and tutorials to help users get started quickly.
What problems is the product solving and how is that benefiting you?
Antithesis helps solve the challenge of finding difficult-to-reproduce bugs and unexpected issues in complex software systems. Its automated testing helps identify failures earlier, reduces manual debugging time, and improves software reliability. This benefits me by making testing more efficient, saving development time, and increasing confidence that the software will behave correctly under different conditions.
Ritesh K.
Reliable and Effective Testing
Reviewed on Oct 29, 2026
Review provided by G2
What do you like best about the product?
I like that Antithesis is focused on finding bugs and making software more reliable. I find its approach to testing genuinely interesting, and I appreciate the idea of using technology to tackle real problems and improve the testing process overall.
What do you dislike about the product?
I don’t have any major dislikes about Antithesis. Because it uses advanced testing technology, it may take new users a little time to understand everything at first, but I see that as a good opportunity to learn, get comfortable with the platform, and improve over time.
What problems is the product solving and how is that benefiting you?
Antithesis helps me find bugs and hidden issues in software before they affect users. I like that it makes testing more reliable and saves time by uncovering problems that are hard to catch with normal testing. It’s useful because it also helps me see how stronger testing practices can improve overall software quality and reliability.