AWS Lambda: Deploy Your First Serverless Function

By the end of this post you'll have a Python function deployed to AWS Lambda, reachable from the public internet via a Function URL, and you'll understand the basic cost model. About 25 minutes. Stays in the AWS free tier.

You'll need an AWS account and the AWS CLI installed and configured (aws configure).

What "serverless" actually means #

Serverless does not mean "no servers." There are servers; AWS just doesn't make you provision, patch, or scale them. You upload code, AWS runs it on demand, and you pay per invocation + per millisecond of execution.

The trade: you give up control over the runtime environment (max execution time, memory limits, no persistent disk between invocations) in exchange for never thinking about capacity again. Good fit for event-driven workloads (S3 triggers, scheduled jobs, webhooks, light APIs). Bad fit for sustained high-throughput services or anything with persistent state.

The Lambda mental model #

Three things to know:

Function code lives in a .zip (or container image). AWS unpacks it onto an "execution environment" — a tiny container — and runs it.
A handler is the entrypoint function AWS calls when an event fires. For Python: lambda_function.lambda_handler(event, context).
An event is a JSON object describing why the function was invoked. Could be an HTTP request, an S3 upload notification, a scheduled time, etc. The function returns a response (also JSON).

That's the whole programming model. Everything else is wiring.

Step 1: Write the function #

bash.bash

mkdir lambda-tutorial && cd lambda-tutorial

Create lambda_function.py:

python.python

import json
import datetime

def lambda_handler(event, context):
    name = event.get("queryStringParameters", {}).get("name", "world") if event else "world"
    body = {
        "message": f"Hello, {name}!",
        "time_utc": datetime.datetime.utcnow().isoformat() + "Z",
        "request_id": context.aws_request_id if context else "local",
    }
    return {
        "statusCode": 200,
        "headers": {"Content-Type": "application/json"},
        "body": json.dumps(body),
    }


if __name__ == "__main__":
    print(lambda_handler({}, None))

The if __name__ == "__main__" block lets you test locally:

bash.bash

python lambda_function.py

You should see a dict with Hello, world!, current time, and request_id: local. Locally context is None; on Lambda it'll be a real object with the actual request ID.

Step 2: Package it #

Lambda wants a .zip containing your code:

bash.bash

zip function.zip lambda_function.py

For a function with no extra dependencies, that's the whole package. With dependencies (e.g. requests), you'd pip install requests --target . first, then zip the directory.

Step 3: Create an execution role #

Lambda runs as an IAM role. The simplest viable role lets the function write logs to CloudWatch:

bash.bash

cat > trust-policy.json <<EOF
{
  "Version": "2012-10-17",
  "Statement": [{
    "Effect": "Allow",
    "Principal": {"Service": "lambda.amazonaws.com"},
    "Action": "sts:AssumeRole"
  }]
}
EOF

aws iam create-role --role-name lambda-tutorial-role \
  --assume-role-policy-document file://trust-policy.json

aws iam attach-role-policy --role-name lambda-tutorial-role \
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole

The first command creates a role Lambda is allowed to assume. The second attaches a managed policy that allows writing CloudWatch logs. Wait ~10 seconds before the next step — IAM is eventually consistent and a freshly-created role isn't immediately usable.

Get your AWS account ID for the next step:

bash.bash

ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
echo $ACCOUNT

Step 4: Create the Lambda function #

bash.bash

aws lambda create-function \
  --function-name hello-tutorial \
  --runtime python3.12 \
  --role arn:aws:iam::${ACCOUNT}:role/lambda-tutorial-role \
  --handler lambda_function.lambda_handler \
  --zip-file fileb://function.zip

You should get back JSON describing the function. The important field is FunctionArn.

Test it:

bash.bash

aws lambda invoke --function-name hello-tutorial --payload '{}' /tmp/out.json
cat /tmp/out.json

You'll see your response. Lambda is running.

Step 5: Expose it as a URL #

bash.bash

aws lambda create-function-url-config \
  --function-name hello-tutorial \
  --auth-type NONE

This gives you a public URL — no API Gateway needed for this simple case. Copy the FunctionUrl from the output.

Lambda also needs an explicit permission to be invoked anonymously:

bash.bash

aws lambda add-permission \
  --function-name hello-tutorial \
  --statement-id public-invoke \
  --action lambda:InvokeFunctionUrl \
  --principal "*" \
  --function-url-auth-type NONE

Now hit it:

bash.bash

curl "<your-function-url>?name=ada"

You should see:

json.json

{"message": "Hello, ada!", "time_utc": "...", "request_id": "..."}

That's a serverless function reachable from the public internet, in five steps.

Step 6: Update the function #

Change something in lambda_function.py (e.g. add "version": 2 to the body), then:

bash.bash

zip function.zip lambda_function.py
aws lambda update-function-code --function-name hello-tutorial --zip-file fileb://function.zip

The next request hits the new version. No restart, no provisioning. That's the part teams fall in love with.

Step 7: Cleanup #

bash.bash

aws lambda delete-function-url-config --function-name hello-tutorial
aws lambda delete-function --function-name hello-tutorial
aws iam detach-role-policy --role-name lambda-tutorial-role \
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
aws iam delete-role --role-name lambda-tutorial-role

Common mistakes #

Cold starts ignored. The first request after the function has been idle takes longer because Lambda is spinning up a new execution environment. For a tiny Python function it's ~200ms; for a large Java function with heavy startup it's seconds. If user-facing latency matters, use Provisioned Concurrency or a different platform.

Storing state on disk. /tmp exists in Lambda but is per-execution-environment and ephemeral. State that needs to survive across invocations goes to S3, DynamoDB, or another store.

Long-running tasks. Lambda has a 15-minute hard limit. Workloads near that should split into smaller invocations or move to ECS / EKS.

Loose IAM permissions. It's tempting to grant the function * permissions to skip the IAM dance. Don't — the role is your blast radius if the function is ever compromised. Grant exactly what it needs.

What to read next #

You've shipped your first function. The next levels:

AWS Lambda optimization: cost and performance — the patterns that move the bill once you're running ~200 functions
Serverless architecture patterns — when Lambda is the right tool, when it isn't
AWS ECS vs EKS: choosing the right container platform — what to reach for when Lambda's constraints get in the way

Lambda is one of AWS's best products when used for event-driven work and short-lived tasks. The hardest part of getting started is the IAM dance — once your first function is running, every subsequent one is faster.

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