AWS Lambda Development Workshop

Accelerate Your Serverless Journey with AWS Lambda

AWS Lambda enables you to run code without provisioning or managing servers, but mastering its nuances takes time. Our workshop gives you a solid foundation with real-world code, deployment guidance, and expert insights that would take months to learn on your own.

What You'll Learn

• Starter Code Base – Production-ready Lambda functions with best practices built in
• Deployment to Your Account – We deploy directly to your AWS environment and walk you through the setup
• Detailed Walkthrough – Understand every component and how to extend it for your needs
• Pros/Cons Analysis – Learn about Lambda's limitations with large payloads, long processing times, and high concurrency
• Best Practices – Cold starts, memory optimization, error handling, and monitoring strategies

AWS Lambda vs Traditional Servers

Lambda offers automatic scaling, pay-per-use pricing, and zero server maintenance. However, it comes with execution time limits (15 minutes max), payload size restrictions, and cold start latency considerations. Our workshop covers when Lambda is the right choice and when traditional servers or containers might be better suited for your workload.

The Limits That Actually Shape Your Architecture

Most serverless projects that go wrong do so because a hard platform limit was discovered late, after the design was already committed. These are the constraints worth knowing before you write the first handler, and they are the ones the workshop walks through against your own workload:

• 15-minute execution ceiling – A single invocation cannot run longer. Anything slower has to be decomposed into steps, handed to Step Functions, or moved to Fargate or ECS.
• 6 MB synchronous payloads – Request and response bodies are capped for synchronous invokes, and asynchronous events are capped far lower. Larger data moves through S3 with the event carrying only a pointer.
• Memory buys CPU – Lambda allocates vCPU in proportion to configured memory, so a function is often cheaper at higher memory because it finishes proportionally faster. This is measurable, and worth measuring rather than guessing.
• Ephemeral /tmp storage – Writable scratch space defaults to 512 MB and is configurable well beyond that, but it persists only for the life of the execution environment and must never be treated as durable.
• Regional concurrency – Accounts start with a shared per-region concurrency pool. One busy function can starve every other function in the account unless reserved concurrency is set deliberately.
• Deployment package size – Zip uploads are limited enough that heavyweight dependencies push you toward layers or container images, which changes your build and release process.

Serverless Pitfalls Worth Designing Around

Lambda removes server management, not engineering judgment. The failure modes simply move somewhere less familiar. The recurring ones:

• Database connections – Traditional connection pooling assumes long-lived processes. Hundreds of concurrent Lambdas each opening a connection will exhaust a relational database. RDS Proxy, or a data API, exists for this reason.
• Retries and idempotency – Asynchronous invocations retry automatically and event sources deliver at least once. Any handler with side effects needs a deduplication key, or it will double-charge, double-send, or double-write.
• Silent failures – Without a dead-letter queue or failure destination, a poison message is retried and then discarded. Configuring these is a five-minute job that is routinely skipped.
• Cold starts – Real, but usually mis-attributed. Init-phase work, VPC attachment, and dependency loading dominate; runtime choice matters less than what happens before the handler runs. Provisioned concurrency is a cost decision, not a default.
• Debugging without a server – You cannot attach to a box. Structured logging, correlation IDs, and distributed tracing have to be in place from the first deploy, because retrofitting them mid-incident is painful.

What You Take Away

The workshop is built around your codebase and your AWS account rather than a sandbox, so what exists at the end is running infrastructure rather than notes. The starter code base is production-shaped: infrastructure as code, environment separation, least-privilege IAM roles, structured logging, and error handling already wired in. We deploy it to your target account together and walk through every component so your team can extend it without reverse-engineering anything.

The pros and cons analysis is the part teams tend to value most in hindsight. It is an honest assessment of where Lambda fits your specific workload and where it does not — including the cases where a container or a plain server is the better answer. Recommending serverless for something that should not be serverless costs far more than the workshop.

Is This Workshop Right for You?

It fits teams with solid software engineering practice who are new to serverless specifically, and teams already running Lambda who have hit cost, latency, or reliability problems they cannot place. If you have never deployed to AWS at all, you will get more out of foundational cloud training first. If you already run serverless at scale and have opinions about cold starts, you likely do not need this.

Frequently Asked Questions

Do we need an existing AWS account?

Yes. We deploy into your account so that everything built during the workshop stays under your control and continues running afterwards. You keep the code, the infrastructure definitions, and the deployment pipeline.

Which language and runtime do you use?

We work in the language your team already maintains. The architectural material — limits, idempotency, observability, cost modelling — applies across runtimes, and the starter code is written to match your stack rather than ours.

Will this reduce our AWS bill?

Often, though it depends entirely on your current workload and how it is deployed. Consistently busy services can be cheaper on reserved capacity than on Lambda, while spiky or low-volume workloads are usually much cheaper serverless. We model your actual traffic rather than assert a number, and if serverless is the more expensive option for you, we will say so.

What happens after the workshop?

You own everything delivered. Support is available for follow-up questions as your team extends the code, and larger pieces of work can be scoped separately.

Choose Your Plan

Workshops run from $3,000 to $10,000 depending on scope, the number of engineers involved, and how much of the starter code base is tailored to your stack.