Blog
November 12, 2025
Your team has just deployed a critical product update. Development was meticulous, regression tests passed, and load tests were all green. You anticipate a smooth rollout and positive user feedback. Instead, the application crashes under production load, forcing an immediate rollback. The team is left frustrated, and end-user trust is damaged.
This scenario is all too common. Even with rigorous testing, systems fail in unexpected ways once they face the real world. The solution is not just more testing, but a different kind of testing.
This blog provides a definitive comparison between chaos testing and chaos engineering, a disciplined approach for building resilient systems that can withstand the turbulent conditions of production environments. It will explore its core principles, modern practices for 2025, and how to integrate it into your development lifecycle to build unshakeable confidence in your applications.
Table of Contents
- What is Chaos Testing?
- What is Chaos Engineering?
- What You Miss When You Don't Utilize Chaos
- Components of Chaos Testing & Chaos Engineering
- The Evolution of Chaos Testing & Chaos Engineering
- Expanding the Spectrum of Failure Modes
- Industry Use Cases: Chaos Engineering in Action
- Integrating Chaos with SRE, DevOps, and Observability
- Challenges, Risks, and How to Mitigate Them
- The Future of Chaos Engineering
- Bottom Line
Back to top
What is Chaos Testing?
Chaos testing is a method of software testing that deliberately introduces randomness and unexpected variations into test inputs, scenarios, and environments. The primary objective is to expose hidden weaknesses by simulating real-world unpredictability early in the development or testing cycle.
All of that with the goal to add the same level of unpredictability to our testing like the real world brings to our application in production. In other words, we need to to make sure that our testing will approximate the real world conditions, unexpected patterns and behavior and therefore we will be able to capture the hidden issues earlier than when everything is released in production.
Back to topWhat is Chaos Engineering?
Chaos engineering is a disciplined approach to building confidence in a system’s ability to withstand turbulent and unexpected conditions in production. Unlike chaos testing, chaos engineering is grounded in the scientific method and is focused on running controlled experiments in live or production-like environments.
At its core, chaos engineering is designed to proactively uncover systemic weaknesses by mimicking real-world failures, thereby fostering organizational learning and resilience. The practice is distinguished by a focus on minimizing risk through blast radius control, rigorous measurement, and safe experiment rollback.
| Aspect | Chaos Testing | Chaos Engineering |
| Purpose | To expose hidden bugs by introducing random changes or faults in test environments. | To systematically improve system resilience by conducting controlled, hypothesis-driven experiments. |
| Methodology | Often ad hoc; uses randomness and variability in test data, scenarios, or environment to simulate unexpected behaviors. | Follows a scientific process: defines steady-state metrics, forms hypotheses, runs controlled experiments, and analyzes results. |
| Scope | Typically limited to development or test environments; focus is on uncovering unpredictable faults. | Encompasses the entire system lifecycle, including production environments, seeking to build confidence in overall system robustness. |
| Control | Limited control over the blast radius or impact; less emphasis on rollback and governance. | Rigorous control of blast radius, automated rollback mechanisms, and strict governance practices. |
| Examples | Injecting random delays, altering input data, or changing request/response order in QA environments. | Simulating cloud region outages, network partitions, or dependency failures in a staged or live environment, guided by a well-defined experiment plan. |
What You Miss When You Don't Utilize Chaos
Let us first understand why our fine-tuned tests are still failing to capture these hidden issues that have tendency to appear seemingly out of nowhere.
When the application is running in production it faces the real users and the real environment conditions – let’s say, it faces whatever the “real world” brings to it. And as we know from our everyday personal experience, the world around us – nature, weather, people – is hardly deterministic and predictable. Despite the belief that we can predict test outcomes – and maybe in most cases we could to some degree – we cannot predict it with 100% accuracy. There is always something that surprises us.
The same applies for the “real world” where our applications and services delivered to production are living. Perhaps 99.9% of users will follow the flows that were tested and everything may looks ok, but there are always some users that perform the stuff differently – in an unexpected way. And perhaps not always intentionally, it may be just a simple and natural mistake, typo, wrong click or tap. The same applies to the environment where the app is deployed and for the dependencies your application relies on. In most of the cases they work as expected, but not always… And all these exceptions – or glitches if you will – may cause serious troubles to your application. And what is worse – most often nobody can say in advance how big these troubles will be – because nobody has ever thought about them, let alone tested for them.
The fact is that most often we do test the typical, predictable scenarios in a testing environment that is stable (or maybe it is not that stable, but that is a different story…). But our applications in production face something that is way different from these ideal or “laboratory” conditions in our test environment.
When I spoke to bunch of software testers, they shared with me the following observation: a lot of defects in applications are discovered not because the test plans are strictly followed, but in fact because they intentionally or even unintentionally (by a lucky mistake) deviate from the test plans – actions are performed in a bit different order than expected, values that are entered are different from the expected ones or instead of “John Doe” test user somebody used a test user who has a middle name which nobody ever considered before.
Best Practices Checklist for Chaos Experiments
- Start Small: Begin with simple, well-understood failure scenarios in a non-production environment.
- Define a Hypothesis: Clearly state what you expect to happen during the experiment.
- Secure a Rollback Plan: Ensure you can stop the experiment and revert any changes instantly.
- Monitor Everything: Use your observability platform to closely watch steady-state metrics throughout the experiment.
- Minimize the Blast Radius: Limit the initial scope of your experiment to a small, controlled group.
- Communicate: Inform stakeholders about when experiments will run and what the potential impact could be.
- Learn and Scale: Analyze the results, fix any discovered weaknesses, and gradually increase the experiment's complexity and scope.
Components of Chaos Testing & Chaos Engineering
Test Data
The inputs that drive the test. Every test requires test data and test data is what drives the test. So instead of testing with the set of expected test data, there has to be a bigger variety of test data that will include the negative ones, the odd ones, the unexpected ones. This is where the intelligent synthetic data generation helps to create the unexpected inputs based on the expected ones and add the necessary variety.
Test Environment & Test Dependencies
In order to simulate the real world conditions, the environment and application dependencies have to behave in a random (aka unexpected) manner – be slow at times, have downtimes at times or just send unexpected responses that may be valid (e.g. very long text strings) or even invalid ones (empty values in case where non-empty are expected, letters instead numbers or emoji character where simple alphanumeric content is expected…). Ability to mock the dependencies and therefore control their behavior at will is the technique that is a pre-requisite to achieve chaotic behavior of the test dependencies.
Test Scenarios
It depends on whether the test is oriented on functional or performance aspect, different approaches should be taken. For the functional test, the goal is to simulate the scenarios that may deviate from the expected ideal „happy path“ scenario – i.e. to mix happy path, with negative scenario variations and with unexpected scenario variations. As we have discussed earlier, it may be a different sequence of the steps (e.g. instead of filling the form from the top to bottom, start from the middle to top and then from the middle to bottom), it may be alternative actions like "click on the button twice instead of once“ or "select already selected item in the dropdown“.
These scenario variations may seem odd or too simple to consider to test them, but these are examples of what application has to be ready to face for in production. For example, the two clicks on a button simulates a case when user clicks on the button twice – maybe accidentally, maybe intentionally because the first click does not have any visual feedback so therefore users believes he or she has to click again. And it may happen that the second click triggers the same request again, but the application is not properly developed to accepted two identical requests and something bad may happen downstream.
The Evolution of Chaos Testing & Chaos Engineering
Chaos testing and chaos engineering have matured significantly since their inception. What were once niche practices pioneered by tech giants are now becoming a mainstream component of enterprise software delivery.
Increased Enterprise Adoption and CI/CD Integration
In 2025, chaos engineering is no longer exclusive to companies like Netflix. Enterprises across finance, e-commerce, and healthcare are adopting it to ensure the reliability of their distributed systems. A key trend is the integration of chaos experiments directly into CI/CD pipelines. This "shift-right" approach enables teams to run automated resilience checks as part of their continuous reliability testing to validate every new deployment against potential failures.
The Rise of AI-Driven Chaos Experiments
Artificial intelligence is transforming chaos engineering. Modern platforms now use Generative AI to automatically create relevant chaos experiments based on a system's architecture and historical incident data. AI can also help identify subtle deviations from the steady state that a human might miss to make experiments more insightful. This reduces the manual effort required to design and interpret tests, making the practice more accessible.
Expanding the Spectrum of Failure Modes
Early chaos experiments often focused on infrastructure-level failures. However, modern distributed systems can fail in many more ways. Research shows that while network disruption (~41%) and instance termination (~33%) are common experiments, application-level faults (~3%) are an under-explored area with significant potential for discovery.
To build comprehensive resilience, you must test against a broader range of failure modes:
- Network Partitioning: Simulate scenarios where microservices cannot communicate with each other.
- Kubernetes Failures: Terminate pods, nodes, or even entire services to test self-healing capabilities.
- Cloud Region Outages: Verify that your system can successfully failover to a different geographic region.
- API Dependency Failures: Inject latency or error responses from critical internal and third-party APIs.
- "Security Chaos" (ChaosSec): Intentionally inject security-related failures, such as revoking credentials or misconfiguring firewalls, to test your security monitoring and response mechanisms.
Industry Use Cases: Chaos Engineering in Action
Concrete examples demonstrate the power of chaos engineering. The most famous is Netflix's Simian Army.
- Chaos Monkey: The original tool, Chaos Monkey randomly terminates virtual machine instances in production to ensure engineers build services that can tolerate instance failure without impacting the customer experience.
- Simian Army: Netflix expanded this concept into a suite of tools. Latency Monkey introduces artificial delays to simulate network degradation, while Janitor Monkey finds and removes unused cloud resources to improve efficiency.
Beyond Netflix, other large-scale systems leverage chaos engineering to ensure resilience. Financial institutions use it to test their trading platforms against market volatility and infrastructure failures. E-commerce giants simulate failures in their payment gateways and inventory systems during peak traffic to prevent outages on days like Black Friday. These companies have proven that proactively testing for failure is the only reliable way to prevent it.
Back to topIntegrating Chaos with SRE, DevOps, and Observability
Chaos engineering does not exist in a vacuum. It is a critical component of a broader culture of reliability that includes Site Reliability Engineering (SRE), DevOps, and Observability.
SRE
SRE teams use Service Level Objectives (SLOs) and error budgets to balance reliability with innovation. Chaos engineering is the primary method for validating that a system can meet its SLOs under duress.
DevOps
Integrating chaos experiments into the CI/CD pipeline makes reliability a shared responsibility. When a build fails a chaos test, developers receive immediate feedback and can fix the issue before it reaches production.
Observability
You cannot conduct chaos engineering without a robust observability platform. Your logs, metrics, and traces are essential for defining your steady state, monitoring the impact of an experiment in real-time, and diagnosing the root cause of any discovered weakness. An experiment without observation is just breaking things.
Back to topChallenges, Risks, and How to Mitigate Them
Despite its benefits, implementing chaos engineering comes with challenges. Understanding and mitigating these risks is key to a successful program.
- Risk to Production: The biggest fear is causing an actual outage. Mitigation: Start in pre-production, use a minimal blast radius for initial production tests, and ensure you have an emergency "stop" button.
- Stakeholder Buy-In: Convincing leadership to intentionally break things in production can be difficult. Mitigation: Frame it as controlled experimentation to build confidence, not random destruction. Start with a small-scale proof-of-concept to demonstrate value and ROI in preventing future incidents.
- Tooling Complexity: Choosing and implementing the right tools can be overwhelming. Mitigation: Begin with open-source tools or a simple script. Focus on the methodology first, not the tool. Many modern platforms also abstract away much of this complexity.
- Measuring ROI: It can be hard to quantify the value of an outage that didn't happen. Mitigation: Track metrics like the reduction in incident frequency, faster mean time to resolution (MTTR), and improvements in SLO adherence.
The Future of Chaos Engineering
The field of resilience engineering continues to evolve. Looking ahead, several trends will shape the future of chaos:
- AI-Assisted Fault Injection: AI will become more sophisticated, not only suggesting experiments but also predicting complex, cascading failures before they occur.
- Increased Regulation: As digital services become more critical, regulators may mandate resilience testing, making chaos engineering a standard compliance requirement in some industries.
- Chaos for AI/ML Systems: As more applications rely on AI models, we will need new forms of chaos engineering to test their resilience to data drift, adversarial inputs, and unexpected model behavior.
- Wider Enterprise Adoption: The practice will become standard for any organization that cannot afford downtime, moving far beyond its tech-giant origins.
By adopting a disciplined, hypothesis-driven approach, your organization can move beyond hoping for resilience and start engineering it. Chaos engineering provides the framework to build confidence in your systems to ensure they remain available and performant no matter what turbulence comes their way.
Back to topBottom Line
Embracing chaos testing has evolved from an experimental approach to a cornerstone strategy in performance engineering for building resilient, high-performing applications. By introducing controlled disruptions, teams can identify hidden vulnerabilities, validate recovery mechanisms, and ensure systems endure real-world challenges. BlazeMeter’s robust testing platform empowers organizations to scale chaos engineering effortlessly and integrate it seamlessly into existing workflows. This accelerates feedback loops and ensures reliable user experiences. With BlazeMeter, chaos transforms from a potential risk into a powerful tool for continuous improvement and operational excellence.