To run AI-generated code more safely, give each session or job its own constrained execution environment instead of running untrusted code inside the agent controller or an ordinary function invocation. A microVM can provide a VM-level isolation boundary while retaining its own filesystem and state across work. With AWS Lambda MicroVMs, the documented pattern is to initialize an application, capture a snapshot, launch a separate microVM from it, and then suspend or terminate that instance as the workload requires.
A microVM is not a complete security policy. What it can access still depends on what the platform mounts, proxies, injects, or exposes across the boundary. The controller must decide which files, network destinations, credentials, and tools are allowed.
What changes when code moves from a function invocation to a microVM?
An ordinary stateless function invocation is designed to handle a discrete call; it is not the same thing as a long-lived, per-user workspace. A microVM gives a session or job a separate execution environment with its own lifecycle. The application can run in it, retain state while it is active, suspend it while idle, resume it later, and terminate it when finished.
| Question | Ordinary stateless function invocation | Dedicated microVM session or job |
|---|---|---|
| What is the execution unit? | A function invocation. | A separately launched VM environment, such as an AWS Lambda MicroVM instance. |
| What happens to state? | Not intended to be a durable per-session workspace. | The instance can retain memory and disk when suspended, then resume. |
| How is an application prepared? | Function deployment and invocation model. | A documented AWS pattern builds an initialized application snapshot, then launches instances from it. |
| Which is faster or cheaper? | Not stated in the cited AWS product material as a controlled comparison. | Not stated in the cited AWS product material as a controlled comparison. |
The trade is not simply “microVMs are better.” They fit when code is untrusted or user-supplied, needs OS-level capabilities or existing tools, and benefits from a separately managed filesystem and lifecycle. Whether they are the right choice depends on workload behavior, policy requirements, operational design, and measured cost.
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How the AWS Lambda MicroVM lifecycle works
AWS describes Lambda MicroVMs as a managed serverless execution environment built on Firecracker, with VM-level isolation and full OS capabilities. AWS identifies user- and AI-generated code execution as an intended use. The service is one concrete managed option; the architecture of using microVMs for isolated execution is not exclusive to AWS.
- Package the application. In AWS’s documented pattern, package application code and a Dockerfile in an archive and upload it to S3.
- Build an initialized snapshot. AWS provisions a fresh microVM, executes the Dockerfile, starts the application, optionally waits for a readiness response, and captures memory and disk state.
- Launch an instance for a session or job. A caller invokes
run-microvm. The application is restored from the snapshot and exposed through a dedicated HTTPS endpoint. - Suspend when idle, if retaining state is useful. Suspension preserves memory and disk. The instance can resume on traffic or through an explicit API call.
- Terminate when work is done. Termination releases the instance’s resources; it is the appropriate end state when the session no longer needs to persist.
This design can avoid repeating dependency installation and application initialization for every session. It also makes the snapshot a shared starting point, which matters for both security and correctness.
Keep per-session data out of the shared snapshot
Anything captured during image creation may be present in every instance launched from that image. AWS warns that unique IDs, secrets, and network connections created during initialization can therefore be common across instances. Generate session-specific secrets and other unique values only after launch, using the runtime hook, rather than baking them into the snapshot.
That distinction also helps with correctness: a restored connection or identifier may be stale or inappropriate for a new session. Treat the snapshot as reusable software and baseline state, not as a place to store live per-user identity or credentials.
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Where the trust boundary really is
A VM boundary is a meaningful isolation layer, but it does not decide what the application may read or do. AWS describes configurable ingress and egress. The controller and its integrations still determine what crosses that boundary. AWS’s agent-sandbox example keeps agent orchestration and session handling outside the execution VM, using a Lambda MicroVM as the tool-call worker environment. AWS describes per-environment Firecracker isolation, snapshot launch, and vertical scaling as service properties; those are vendor descriptions, not independent security or performance measurements.
Filesystems and host workspaces
Docker’s sandbox documentation illustrates why mounts must be treated as explicit permissions. A direct workspace mount is read-write, so sandbox changes are visible on the host. Clone mode mounts the repository read-only and gives the sandbox a private clone. A mountless sandbox has no host workspace mount. These are Docker-specific options, not universal microVM defaults; select the model that matches whether code should be able to alter the original checkout.
Network access and credentials
Docker documents outbound requests passing through a host proxy and network policy: outbound TCP is governed by policy, UDP is blocked by default unless an experimental feature is enabled, and ICMP is blocked. Its defaults can include broad wildcard domains, so inspect the active rules instead of assuming that “sandboxed” means “offline.”
Docker also documents a product-specific design in which a host-side proxy injects credentials into outbound HTTP request headers without placing raw credential values in the VM. Do not assume another sandbox offers the same mechanism. For any platform, decide which destinations code may contact, which credentials it needs, and whether secrets could be exposed through logs, output, or an allowed network path.
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Tools and host integrations
A process that runs outside the VM remains outside its isolation boundary, even if the agent can call it. Docker notes that local stdio MCP servers run on the host. Treat these as trusted host integrations, not as code contained by the sandbox. Apply the same scrutiny to forwarded credentials, host proxies, mounted skills, and any service that accepts requests from the VM.
Isolation also does not decide which tools an agent may invoke or which deployment actions it may take. AWS’s secure-code-execution guidance presents execution isolation, up-to-date domain expertise, and deterministic governance as separate layers. An isolated worker still needs tool authorization and application-level rules.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which workloads fit a microVM sandbox?
AWS lists interactive code environments, AI code execution, analytics using supplied scripts, security scanning, reinforcement-learning environments, multi-tenant CI/CD, and game servers running user scripts as candidate uses. These share a practical profile: code is untrusted or user-supplied, needs OS-level capabilities, and should have a separately controlled environment per session or job.
- Good fit: an agent needs to install or run packages, manipulate files in a private workspace, or execute arbitrary scripts without sharing a process environment with other users.
- Potentially unnecessary: the task is a short, tightly bounded operation that does not need a persistent workspace or broader OS capabilities.
- Requires additional controls: the agent can reach sensitive services, receive credentials, alter mounted data, or call tools that can affect production systems.
These are architecture considerations, not a claim that microVMs universally outperform containers, gVisor, or other execution services. The AWS material cited here does not establish an independent controlled comparison of security, launch time, resume behavior, or cost. Test representative jobs, including idle periods and resume events, and measure the policy and operational work your design actually requires.
AWS Lambda MicroVM facts to check before adopting it
Product limits and availability change, so check AWS’s current service documentation and pricing for your account and region before implementation. The following figures are specifically AWS’s dated descriptions, not general properties of microVMs:
Quick Recap
- Maximum lifetime: AWS’s 2026 product documentation and announcement describe sessions lasting up to eight hours.
- Regions: AWS’s June 22, 2026 announcement listed US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). That is the announcement’s list, not a guarantee of current availability.
- Allocation and scaling: AWS’s September 18, 2026 Compute Blog describes initial allocations from 0.25 vCPU/0.5 GB to 4 vCPU/8 GB and says an instance can scale up to four times its initial CPU and memory allocation without recreation. These are vendor-described service limits, not benchmark results.
- Earlier stated default: AWS’s launch blog described a baseline default of 1 vCPU and 2 GB of memory, with a maximum baseline of 4 vCPUs and 8 GB. Check current documentation rather than relying on that launch-era description for a deployment.
- Lambda scale statistic: AWS’s current developer guide says Lambda functions powered by Firecracker handle “15 trillion+ monthly invocations.” AWS uses this figure to describe Lambda Functions broadly; it is not a microVM performance result or a count of Lambda MicroVM sessions.
Design checklist for an AI execution sandbox
- Separate control plane from execution. Keep session orchestration and authorization outside the worker environment; pass only the request and permitted inputs it needs.
- Define one boundary per user or job. Decide whether the environment is per session, per task, or reusable, and ensure concurrent users cannot see one another’s filesystem, credentials, or state.
- Build a clean reusable snapshot. Initialize dependencies and application services before capture, but create unique identifiers, secrets, and live connections only after the microVM starts.
- Make every crossing explicit. Review mounts, network egress, forwarded credentials, HTTP proxies, tool integrations, and any host process reachable from the worker.
- Choose a state policy. Suspend a session when preserving memory and disk is useful; terminate it when the work is complete or the retention window expires.
- Test failure and cost behavior. Exercise startup, readiness failures, network denial, suspension, resume, termination, and cleanup using representative code. Measure the actual workload and idle/run pattern rather than assuming a performance or cost advantage.
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