Serverless at scale: resilience and performance in the cloud
Serverless architecture can dramatically reduce overhead, but it requires a different mental model for resilience and observability.
Embracing ephemeral compute
Serverless is the ultimate abstraction for integration - you write code, the cloud provider runs it, and you only pay for what you use. But serverless components are ephemeral. They can be killed and restarted at any moment. This requires your code to be stateless and your integrations to be robust.
We help organizations navigate 'cold starts' and execution limits by choosing the right granularity for their functions. Sometimes a single monolithic function is better for warm-start performance; other times, a highly decomposed set of functions allows for better scaling. The key is matching the compute pattern to the workload.
Designing for 'The Error' path
In serverless environments, errors are first-class citizens. Whether it's a timeout, a downstream dependency failure, or a platform-level issue, your functions must handle failure gracefully. We advocate for 'idempotent design' (ensuring a function can run multiple times with the same input without side effects) and the heavy use of asynchronous queues to handle retries automatically.
When combined with distributed tracing, serverless becomes a powerful way to build self-healing integrations that scale automatically to meet demand without requiring manual server management.