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🛠 This page is for engineering teams self-hosting their own Lightdash instance. On Lightdash Cloud, sandboxes are fully managed for you — there’s nothing to configure.

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What sandboxes are for

Some Lightdash features use an AI agent (Claude Code) that writes and runs code on your behalf. Today that’s:
  • AI writeback — the agent edits your dbt project (e.g. adds a metric or dimension), runs lightdash compile to validate it, and opens a pull request.
  • Data app generation — the agent generates and builds a small web app from a prompt.
Running model-generated code directly on the Lightdash server would be unsafe: the code is untrusted, can run arbitrary commands, and needs its own toolchain (git, dbt, the Lightdash CLI, Node). Lightdash instead runs each agent inside a sandbox — an isolated, disposable environment with a constrained network. The agent does its work there, Lightdash collects the result (a PR, a built app), and the sandbox is torn down. Sandboxes are also what make these features fast and multi-turn: a sandbox can be suspended between turns and resumed later, so a conversation with the agent keeps its state without holding a container open the whole time.

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Sandbox providers

The sandbox backend is pluggable. Lightdash talks to a provider-neutral interface, so the same feature code runs on whichever backend your deployment is configured for. You select the provider with the SANDBOX_PROVIDER environment variable. E2B, AWS Lambda MicroVMs, and Azure Container Apps Sandboxes are all supported production backends. E2B is the managed default; the AWS and Azure providers are for teams who want sandboxes to run inside their own cloud account. More providers (Kubernetes, ECS) are planned.
The local Docker provider is for development only. It launches plain Docker containers via the Docker socket, which is root-equivalent on the host and provides no real isolation between the sandbox and your machine. It refuses to start when NODE_ENV=production. Do not use it for a production deployment.

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E2B (production default)

E2B runs each sandbox as a Firecracker microVM in E2B’s cloud. It’s the default — if you don’t set SANDBOX_PROVIDER, Lightdash uses E2B. To use it you need an E2B account and API key, and the agent needs an Anthropic API key:
The sandbox images are E2B templates. Lightdash uses separate templates for data apps and for AI writeback so they can be pinned or rolled back independently. These default to the published Lightdash templates and rarely need to be set:

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AWS Lambda MicroVMs (self-hosted production)

AWS Lambda MicroVMs run each sandbox as a Firecracker microVM inside your own AWS account, so untrusted agent code and your repository contents never leave your infrastructure. The microVMs have no public IP — your backend reaches each one through an AWS-managed endpoint that requires a short-lived per-microVM token — and you control their outbound network access (see Networking and IAM). This is the recommended sandbox provider for customers deploying Lightdash on AWS — it keeps the sandbox boundary inside your existing AWS account and avoids sending agent workloads or repository contents to a third-party service.

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Prerequisites

Provision these with your own IaC, in the same AWS account and region your Lightdash backend already runs in:
  • Two MicroVM images — one for data app generation and one for AI writeback (they bundle different toolchains). Build them from the Dockerfiles in the Lightdash repo (sandboxes/data-apps/, sandboxes/ai-writeback/, and the exec agent in sandboxes/microvm-agent/), push them to ECR, and register each as a Lambda MicroVM image on the AWS-managed al2023 base — ARM_64, 4 GB memory, with the agent’s /ready hook on port 8080. Each registration returns an image ARN for the config below.
  • Control-plane permissions on your backend’s existing IAM role — add RunMicrovm, GetMicrovm, SuspendMicrovm, ResumeMicrovm, TerminateMicrovm, and CreateMicrovmAuthToken.
Registering an image uses AWS’s create-microvm-image, which needs a build role (trusting lambda.amazonaws.com) and an S3 location to stage the build context. These are build-time only — not the S3 bucket Lightdash already uses for results and snapshots, and the backend never touches them at runtime.

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Configure the provider

The backend uses its ambient AWS credentials (instance role / IRSA / standard SDK credential chain) to call the Lambda MicroVMs control plane, so no access keys are configured here.

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Networking and IAM

Configure the network connectors before going to production. The AWS-managed defaults give the microVM open inbound and outbound access, which means untrusted agent code can reach the public internet from inside your AWS account. We currently recommend pointing the egress connector at a VPC connector that has no outbound access by default, and only opening up the destinations the agent actually needs (your dbt repository host, the Anthropic / Bedrock API, your ECR registry).
Override these to tighten the network boundary or to give the microVM an IAM role:

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Azure Container Apps Sandboxes (self-hosted production)

Azure Container Apps Sandboxes run each sandbox as an isolated, microVM-class environment inside your own Azure subscription, so untrusted agent code and your repository contents never leave your infrastructure. Sandboxes have native suspend/resume (a full memory + disk snapshot with sub-second restore), which is what keeps multi-turn agent conversations fast. This is the recommended sandbox provider for customers deploying Lightdash on Azure — it keeps the sandbox boundary inside your existing Azure tenant and avoids sending agent workloads or repository contents to a third-party service.
Azure Container Apps Sandboxes is currently an Azure preview feature. It requires a Microsoft Entra ID account (personal Microsoft accounts aren’t supported), and its API surface may change while in preview.

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Prerequisites

Provision these in the same Azure subscription and region your Lightdash backend runs in, using the aca CLI or the Sandboxes portal:
  • A sandbox group per feature — one for data app generation and one for AI writeback (they bundle different toolchains). A sandbox group (Microsoft.App/SandboxGroups) is the management boundary that holds a feature’s sandboxes and disk image. Give each group a Memory-mode auto-suspend lifecycle policy so idle sandboxes snapshot and scale to zero.
  • A disk image per group — build the two images from the Dockerfiles in the Lightdash repo (sandboxes/data-apps/, sandboxes/ai-writeback/), push them to a container registry (e.g. Azure Container Registry), and register each as a disk image in its sandbox group. Registration returns a disk image ID for the config below. (Unlike the AWS provider, there is no in-VM agent to build — Sandboxes expose a native command/file API.)
  • A workload identity with the data-plane role — grant your backend’s managed identity the Container Apps SandboxGroup Data Owner role on each sandbox group. Lightdash uses DefaultAzureCredential (workload identity on AKS, or the standard Azure credential chain) to authenticate — no client secret is configured here.

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Configure the provider

Egress is locked down automatically: each sandbox launches with a default-deny egress policy that only allows the hosts the agent needs (the Anthropic API and your git host), with full traffic inspection so the platform enforces the deny on all traffic and blocks non-HTTP egress. Untrusted agent code can’t reach any other destination — outbound requests to non-allowlisted hosts are rejected, and all other ports are blocked.

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Google Cloud Run Sandboxes (self-hosted, preview)

Use the gcp-cloud-run provider to run data app sandboxes in a Cloud Run service. Lightdash connects to the service through an HTTP gateway included in the sandbox image.
Cloud Run Sandboxes is in public preview and currently supports data app generation only. AI writeback is not supported.

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Prerequisites

  • A Google Cloud project with billing enabled.
  • A recent version of the gcloud CLI that supports gcloud beta run deploy --sandbox-launcher.
  • A checkout of the Lightdash repository.
  • External object storage configured for Lightdash.
  • An Anthropic API key.

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1. Enable the Google Cloud APIs

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2. Build the gateway image

From the root of the Lightdash repository, run:

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3. Deploy the gateway

Generate a shared secret and deploy the image:
Save SANDBOX_SECRET securely. You need the same value when configuring Lightdash. Although the Cloud Run service allows unauthenticated requests, the gateway requires this secret for every sandbox operation. Keep the service at one instance. Active sandboxes run inside that instance and are lost if Cloud Run replaces it during a generation.

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4. Configure Lightdash

Get the gateway URL:
Set these environment variables on both the Lightdash backend and scheduler:
Restart the backend and scheduler to apply the configuration.
Cloud Run sandboxes block all outbound traffic by default, but sandboxes that need internet access (data app generation does) are launched with egress fully open — the sandbox layer is on/off only, with no per-host rules. To constrain what sandboxes can reach, route the service through your VPC and filter there (next step).
Sandbox traffic exits through the gateway instance’s network path, so it follows the service’s VPC egress setting and can be filtered with standard VPC networking. None of this is provisioned by Lightdash — you configure it yourself:
  1. A subnet on the VPC network you’ll filter, in the same region as the gateway service.
  2. A Cloud Router and Cloud NAT on that network. Once the service routes all traffic through the VPC, outbound internet access only works via NAT — without it, sandboxes and the gateway itself lose all egress. Reserve a static IP for the NAT if you want a stable egress IP to allowlist on your side (for example, on your git host):
  3. Direct VPC egress on the gateway service. This deploys a new revision, so any active sandboxes are lost — do it while no generation is running:
    All sandbox traffic now leaves through your VPC: its public egress IP is the NAT IP, and VPC firewall egress rules apply to it — a deny rule for a destination range blocks matching sandbox traffic while the rest keeps flowing.
  4. Filter what sandboxes can reach. VPC firewall rules give you IP/CIDR-level control. For a domain-level allowlist, add Secure Web Proxy: create a deny-all-egress firewall rule that only allows traffic to the proxy, and allowlist the domains the sandboxes and the gateway need — at minimum api.anthropic.com, registry.npmjs.org, and your object storage endpoint. Secure Web Proxy is an explicit proxy: clients must be configured to use it (bake proxy settings into the gateway image — sandboxes share its root filesystem), and the deny-all firewall drops anything that bypasses it.
To update the sandbox toolchain, rebuild the gateway image and redeploy the Cloud Run service.

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Local Docker provider (development)

For local development you can run sandboxes as plain Docker containers on your own machine — no E2B account required. This is the recommended way to work on or try the AI features locally. It uses the same images E2B builds, but as plain local Docker images. Two separate images are used (different toolchains), mirroring the two E2B templates:

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Prerequisites

  • Docker running locally, with the daemon reachable from the Lightdash backend.
  • S3-compatible object storage configured (locally this is MinIO). Suspended-sandbox snapshots are tarred to object storage so a conversation survives the container being destroyed — see external object storage.
  • An Anthropic API key (ANTHROPIC_API_KEY) for the agent.

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Setup

  1. Build the local sandbox images (each builds from sandboxes/<feature>/):
    These are large (the writeback image bundles dbt, the Lightdash CLI and Claude Code) and only need rebuilding when the sandbox toolchain changes.
  2. Point Lightdash at the Docker provider:
  3. Restart the backend and the scheduler so both pick up the new environment. Data app generation runs in the scheduler worker, so a stale SANDBOX_PROVIDER there will keep it on E2B. (With PM2, a plain restart reuses the cached env — delete and re-start the processes, or restart with --update-env, to actually reload the env file.)

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Snapshot lifecycle

Every turn suspends its own sandbox, so in steady state nothing sits idle. Two timers configure the cloud-side idle policy as a backstop — when to auto-suspend a sandbox left running and when to auto-terminate a suspended one:
SANDBOX_IDLE_TIMEOUT_MS feeds the auto-suspend policy on both the Lambda MicroVMs and Azure Sandboxes providers (for Azure it sets each sandbox’s Memory-mode auto-suspend interval). SANDBOX_SNAPSHOT_RETENTION_MS is read only by Lambda MicroVMs — on Azure, suspended-sandbox retention is governed by the sandbox group’s own auto-delete policy. E2B manages idle sandboxes itself, and the Docker and Cloud Run providers have no cloud-side idle handling — both persist suspend-time snapshots to object storage and destroy the sandbox, so nothing idles in the first place.

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Environment variable reference