Operations | Monitoring | ITSM | DevOps | Cloud

Serverless computing requires a significant shift in how organizations think about deploying and managing applications. No longer do Ops teams need to think about provisioning servers, installing operating system patches, and writing shell scripts to manage deployments. While serverless takes away much of this responsibility, one aspect still needs to be handled thoughtfully: reliability. In this blog, we’ll look at three important facts about serverless reliability that teams often overlook.

The amount of traffic handled by AI systems can’t be overstated. Over half of all organizations in India, the UAE, Singapore, and China use AI, and traffic from generative AI sources jumped by 1,200% since July 2024. While demand for AI-powered workloads is steadily increasing overall, traffic to individual AI providers is much more unpredictable. User demand spikes and wanes unexpectedly, but like any service, users expect you to always be available and responsive.

It’s been a year and a half since Failure Flags was released. Since then, customers have used Failure Flags to run thousands of tests for applications running on serverless, container, and service meshes.

Not too long ago, a customer came to us with a high-value use case. The customer, a major apparel company with retail and e-commerce applications, needed to prove that a critical service of their payment applications could failover correctly between regions in case of an outage. But there was one snag: the service was built using AWS Lambda. This meant infrastructure-focused tests would have trouble replicating the failure conditions necessary to test the failover due to Lambda’s serverless model.

The AI (artificial intelligence) landscape wouldn’t be where it is today without AI-as-a-service (AIaaS) providers like OpenAI, AWS, and Google Cloud. These companies have made running AI models as easy as clicking a button. As a result, more applications have been able to use AI services for data analysis, content generation, media production, and much more.

One of the biggest causes of outages and incidents is good old-fashioned human error. Despite all of our best intentions, we can still make mistakes, like forgetting to change defaults, making small typos, or leaving conflicting timeouts in the code. It’s why 27.8% of unplanned outages are caused by someone making a change to the environment. Fortunately, reliability testing can help you catch these errors before they cause outages.

When you think about “AI reliability,” what comes to mind? If you’re like most people, you’re probably thinking of generative AI model accuracy, like responses from ChatGPT, Stable Diffusion, and Sora. While this is certainly important, there’s an even more fundamental type of reliability: the reliability of the infrastructure that your AI models and applications are running on. AI infrastructure is complex, distributed, and automated, making it highly susceptible to failure.

Today, we’re excited to announce Gremlin Private Edition, a version of Gremlin that you can run entirely within your own private network.

Testing shouldn’t feel risky. While it might sound counterintuitive, certain types of testing can actually increase risks to your systems. Load testing, for example, is a great way to see how your systems behave under pressure, but it can also cause those same systems to fail if they aren’t equipped to handle the load. For some types of testing, this is necessary, as is the case with reliability testing and Chaos Engineering.

You’re well on your way to becoming more reliable. You’ve added your services, found and fixed some Detected Risks, and run your first set of reliability tests. However, some of your tests returned as “Failed.” Not to worry: this isn’t a reflection of you or your engineering skills but rather an opportunity to learn more about how your systems work and, more importantly, how to make them more resilient.