Written by Albert Friedman
on 

Deploying your first AWS Lambda: a practical path with AWS SAM and SnapStart

Serverless starts simple: small pieces of code that run on demand, with no servers to manage. For many beginners, the first milestone is deploying a Hello World-style AWS Lambda. Today, a practical and future-ready approach is to use the AWS Serverless Application Model (SAM) to package and deploy your function, and—when latency matters—enable Lambda SnapStart to reduce cold starts for supported runtimes. This article walks through the what, why, and a compact how, with concrete examples and notes on trade-offs.

Why pick AWS SAM for your first Lambda?

AWS SAM is a lightweight framework that wraps CloudFormation with serverless-friendly shortcuts. It generates a project scaffold, builds dependencies, and turns a SAM template into CloudFormation during deployment—so your infrastructure is versionable and repeatable. SAM also makes local testing easier (local containers) and integrates with CI/CD patterns out of the box. The official SAM tutorial demonstrates an initialize → build → deploy workflow that’s ideal for first-time deployments. (docs.aws.amazon.com)

Key practical benefits:

A short, hands-on overview (what you’ll deploy)

This example uses:

Minimal Python handler (hello_world/app.py)

def lambda_handler(event, context):
    return {
        "statusCode": 200,
        "body": "Hello from Lambda via SAM!"
    }

A minimal SAM function resource that publishes versions automatically and enables SnapStart:

Resources:
  HelloFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: hello_world.lambda_handler
      Runtime: python3.12
      CodeUri: hello_world/
      AutoPublishAlias: live
      SnapStart:
        ApplyOn: PublishedVersions
      Events:
        HelloApi:
          Type: Api
          Properties:
            Path: /hello
            Method: get

Notes:

Quick command flow (what SAM runs for you)

These commands remove much of the manual packaging and IAM wiring you’d otherwise do in the console or via direct CloudFormation.

Why consider SnapStart right away?

Cold starts—when Lambda has to initialize runtime and your code—are a frequent source of latency, especially for JVM-based functions or any function that loads heavy libraries during initialization. SnapStart addresses this by initializing a function version once, taking a snapshot of the initialized execution environment, and then restoring new execution environments from that snapshot. The result: initialization can drop from multiple seconds to sub-second latency in many cases. SnapStart works best for latency-sensitive APIs or data-processing paths that are invoked at scale. (docs.aws.amazon.com)

Important constraints and behavior:

Container images and Lambda (an alternative packaging option)

Lambda also supports packaging functions as container images, which can be convenient if your build system already outputs Docker images or if you need larger artifacts or custom base images. Container images are supported as a deployment format and can be built using multi-stage Docker builds; Lambda base images are provided to simplify runtime compatibility. Note that Lambda supports large images (whitepapers and docs mention image size up to 10 GB), but large images increase cold-start surface and deployment complexity—measure the trade-offs. (docs.aws.amazon.com)

If using container images with SnapStart, be aware there are additional implementation details (SnapStart hooks for container images) that you should validate in the docs. (docs.aws.amazon.com)

A few practical tips and pitfalls to watch for

Short recap

This combination—SAM to handle the scaffolding and deployment, and SnapStart to tame cold starts when the runtime supports it—gives a practical, modern path for a first Lambda that is repeatable, testable, and tuned for latency-sensitive endpoints.