AWS Lambda MicroVMs: I Tested the New Stateful Serverless Primitive

What just happened

On June 22, 2026, AWS quietly launched AWS Lambda MicroVMs. Not a Lambda feature update. A new compute primitive sitting between AWS Lambda Functions (stateless, 15-min max) and EC2 (full VM, you manage everything).

Each MicroVM is an isolated Firecracker VM with its own HTTPS endpoint, running your code from a pre-built snapshot. Stateful. Up to 8 hours. Suspend when idle, resume on demand.

I tested it the same week. Here's what I found.

The test setup

A minimal Python HTTP server packaged as a Dockerfile:

from http.server import HTTPServer, BaseHTTPRequestHandler
import json, time, os

class Handler(BaseHTTPRequestHandler):
    start_time = time.time()
    request_count = 0

    def do_GET(self):
        Handler.request_count += 1
        body = json.dumps({
            "message": "Hello from Lambda MicroVM!",
            "uptime_seconds": round(time.time() - Handler.start_time, 2),
            "requests_served": Handler.request_count,
            "pid": os.getpid()
        })
        self.send_response(200)
        self.send_header("Content-Type", "application/json")
        self.end_headers()
        self.wfile.write(body.encode())

HTTPServer(("0.0.0.0", 8080), Handler).serve_forever()

The Dockerfile:

FROM public.ecr.aws/lambda/microvms:al2023-minimal
RUN dnf install -y python3 && dnf clean all
WORKDIR /app
COPY app.py .
EXPOSE 8080
CMD ["python3", "app.py"]

How it works

Three steps:

1. Zip code + Dockerfile → upload to Amazon Simple Storage Service (Amazon S3)
2. create-microvm-image builds the container, starts the app, takes a Firecracker snapshot of memory and disk
3. run-microvm launches from that snapshot

Every launch resumes from the pre-initialized state. No cold boot. Your app is already running the moment the MicroVM starts.

aws lambda-microvms create-microvm-image \
  --name hello-microvm-test \
  --code-artifact "uri=s3://my-bucket/artifact.zip" \
  --base-image-arn arn:aws:lambda:us-east-1:aws:microvm-image:al2023-1 \
  --build-role-arn arn:aws:iam::123456789:role/MicroVMBuildRole

Image build took about 3 minutes. Once done:

aws lambda-microvms run-microvm \
  --image-identifier arn:aws:lambda:us-east-1:123456789:microvm-image:hello-microvm-test \
  --execution-role-arn arn:aws:iam::123456789:role/MicroVMExecutionRole \
  --idle-policy '{"maxIdleDurationSeconds":300,"suspendedDurationSeconds":60,"autoResumeEnabled":true}'

Response:

{
  "microvmId": "microvm-489fbc1b-1c73-3b37-a9f2-266d0173cb94",
  "state": "RUNNING",
  "endpoint": "34cf7dac-bb5c.lambda-microvm.us-east-1.on.aws"
}

The numbers

The 340ms warm latency includes my network round-trip from Hamburg to us-east-1. The actual compute latency is lower.

Statefulness proof

This is the part that matters. After three requests:

{"requests_served": 3, "uptime_seconds": 434.76, "pid": 1}

Suspend the MicroVM. Resume it. Send another request:

{"requests_served": 5, "uptime_seconds": 454.1, "pid": 1}

Same PID. Counter continued from where it left off. Uptime kept ticking (includes suspended time). Full memory and disk state preserved across suspend/resume.

Authentication

Each request needs a JWE token generated via the API:

aws lambda-microvms create-microvm-auth-token \
  --microvm-id microvm-489fbc1b \
  --expiration-in-minutes 15 \
  --allowed-ports '[{"port":8080}]'

The token goes in the X-aws-proxy-auth header. Short-lived, scoped to specific ports. No way to hit someone else's MicroVM.

What this replaces

Before Lambda MicroVMs, running untrusted code (AI-generated, user-submitted) meant:

• Containers with custom hardening — shared kernel, escape risk, significant engineering to harden
• EC2 per user — minutes to start, expensive, you manage everything
• Lambda Functions — 15-min max, stateless, no interactive sessions

Lambda MicroVMs fills the gap: VM-level isolation with serverless operational model. No capacity planning. No kernel to patch. Suspend when idle, pay only for snapshot storage.

Specs and limits

• Compute: 0.5–8 GB RAM baseline, burst to 32 GB. 0.25–4 vCPU baseline, burst to 16.
• Disk: up to 32 GB
• Runtime: max 8 hours
• Architecture: ARM64 only (for now)
• Protocols: HTTP/1.1, HTTP/2, gRPC, WebSocket, SSE
• Regions: us-east-1, us-east-2, us-west-2, eu-west-1, ap-northeast-1

Pricing model

Three dimensions:

• Compute: per-second, based on your chosen baseline + peak usage above it
• Snapshot operations: read/write when launching or suspending
• Snapshot storage + data transfer

Suspended MicroVMs cost only storage. No compute charges while idle.

Who should care

If you're building any of these, Lambda MicroVMs changes your architecture:

• AI agent sandboxes (execute generated code safely)
• Browser-based IDEs (each user gets their own env)
• CI/CD runners (isolated per job, no shared state)
• Jupyter/analytics (state persists across sessions)
• Vulnerability scanning (disposable, isolated)

What I'd watch

• ARM64 only is a constraint for workloads compiled for x86
• 5 regions at launch means some customers wait
• The snapshot-based model means your app's initialization needs to be snapshot-friendly (no stale connections, no clock-sensitive state at init)
• Pricing details not fully public yet at time of writing

Getting started

You need AWS CLI v2.35.10+. The lambda-microvms service is a separate command namespace:

aws lambda-microvms list-managed-microvm-images --region us-east-1
aws lambda-microvms create-microvm-image --help
aws lambda-microvms run-microvm --help

The base image (al2023-1) is Amazon Linux 2023 minimal. Your Dockerfile adds what you need on top.

Tested June 24, 2026. Lambda MicroVMs launched June 22 in preview.

Sources

• Blog: https://aws.amazon.com/blogs/aws/run-isolated-sandboxes-with-full-lifecycle-control-aws-lambda-introduces-microvms/
• Product page: https://aws.amazon.com/lambda/lambda-microvms/
• CLI: aws-cli v2.35.10+ (aws lambda-microvms)