Running a Load Test within a Chaos Experiment

With Azure Chaos Studio and Azure Load Testing, you can simulate both — run a controlled load test while injecting faults into your application or infrastructure to understand how it behaves under stress.

Together, they help you find those resiliency blind spots — the cascading failures, retry storms, and degraded dependencies that only appear when your system is both busy and broken.

For example:

• What if your database becomes read-only during peak user traffic?
• How does your API react if a downstream service starts returning 500s?
• Can your autoscaling rules recover fast enough?

Let’s explore how you can run load tests from Azure Load Testing as part of a chaos experiment.

Azure Chaos Studio + Azure Load Testing Integration

Azure Chaos Studio has load test actions that let you integrate load testing directly into your chaos experiment flow.

From the Chaos Studio fault library, you can find:

• Start load test (Azure Load Testing)
• Stop load test (Azure Load Testing)

Triggers a load test from your Azure Load Testing resource as part of an experiment step.

This means you can now orchestrate a sequence like:

1. Start load test
2. Inject a fault (e.g., shut down VM, throttle network, restart App Service)
3. Observe and measure resiliency
4. Stop the test and analyze metrics

Chaos Experiment with Load Test Action

Here’s how a typical experiment might look conceptually:

Step 1. Define the experiment in Chaos Studio

Create a new experiment that targets your application or infrastructure components — for example, an App Service or a SQL Database.

Add the Start Load Test (Azure Load Testing) action: This tells Chaos Studio to kick off a load test from Azure Load Testing.

Snapshot of a Chaos experiement with load test action in Azure portal

Step 2. Add faults to simulate real-world failures

You can follow up the load test action with a fault like:

• CPU pressure on your VM or container
• Network latency or packet loss injection
• Service shutdown of a dependent component

Step 3. Observe and analyze

Once the experiment runs, you can:

• View load test metrics (like response times, error rates, throughput) in Azure Load Testing
• View fault outcomes in Chaos Studio
• Correlate both using Application Insights or Log Analytics

This gives a holistic view of performance and resiliency under controlled failure.

By combining load and chaos, you can answer:

• How does latency or failure in one microservice affect end-to-end response times?
• Do retry policies or circuit breakers behave as expected under load?
• Does the system self-heal once the fault is removed?
• What’s the performance impact of failover mechanisms?

Conclusion

Chaos testing under load helps teams move from confidence to certainty. Azure’s native integration between Chaos Studio and Load Testing makes it easier than ever to build resiliency testing into your CI/CD pipeline — using only Azure-native services.

 Learn More

• Azure Chaos Studio documentation
• Azure Load Testing documentation