Sandboxes

How Lightdash runs LLM-generated code safely in isolated sandboxes, and how to configure the sandbox provider on a self-hosted instance.

🛠 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.

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.

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.

Provider	SANDBOX_PROVIDER	Use for	Isolation
E2B (default)	e2b	Production / managed	microVM
AWS Lambda MicroVMs	lambda-microvm	Production on AWS (self-hosted)	microVM
Azure Container Apps Sandboxes	azure-sandboxes	Production on Azure (self-hosted)	microVM
Google Cloud Run Sandboxes	gcp-cloud-run	GCP (self-hosted, preview)	gVisor
Local Docker	docker	Local development only	container

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.

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:

SANDBOX_PROVIDER=e2b            # default, can be omitted
E2B_API_KEY=e2b_...            # from your E2B dashboard
ANTHROPIC_API_KEY=sk-ant-...   # the agent (Claude Code) runs inside the sandbox

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:

E2B_TEMPLATE_NAME=lightdash/lightdash-data-app
E2B_TEMPLATE_TAG=<lightdash-version>             # defaults to your Lightdash version
E2B_AI_WRITEBACK_TEMPLATE_NAME=lightdash/lightdash-ai-writeback
E2B_AI_WRITEBACK_TEMPLATE_TAG=<lightdash-version>

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.

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.

Configure the provider

SANDBOX_PROVIDER=lambda-microvm
ANTHROPIC_API_KEY=sk-ant-...   # the agent (Claude Code) runs inside the microVM

# Region the microVMs run in (defaults to eu-west-1, the EU launch region)
LAMBDA_MICROVM_REGION=eu-west-1

# The image ARNs from registering the two MicroVM images above. Required.
LAMBDA_MICROVM_DATA_APP_IMAGE_ARN=arn:aws:lambda:<region>:<account>:microvm-image/...
LAMBDA_MICROVM_AI_WRITEBACK_IMAGE_ARN=arn:aws:lambda:<region>:<account>:microvm-image/...

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.

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:

# IAM role the microVM assumes (e.g. to pull from a private ECR or reach AWS APIs)
LAMBDA_MICROVM_EXECUTION_ROLE_ARN=arn:aws:iam::...:role/lightdash-sandbox

# Network connectors. Default to AWS-managed open ingress/egress; point these at
# your own VPC connectors to constrain traffic. We strongly recommend an egress
# connector that denies outbound traffic by default.
LAMBDA_MICROVM_INGRESS_CONNECTOR_ARN=arn:aws:lambda:<region>:aws:network-connector:aws-network-connector:ALL_INGRESS
LAMBDA_MICROVM_EGRESS_CONNECTOR_ARN=arn:aws:lambda:<region>:aws:network-connector:aws-network-connector:INTERNET_EGRESS

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.

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.

Configure the provider

SANDBOX_PROVIDER=azure-sandboxes
ANTHROPIC_API_KEY=sk-ant-...   # the agent (Claude Code) runs inside the sandbox

# Where your sandbox groups live
AZURE_SANDBOXES_SUBSCRIPTION_ID=<subscription-id>
AZURE_SANDBOXES_RESOURCE_GROUP=<resource-group>
AZURE_SANDBOXES_REGION=eastus2

# Per-feature sandbox group + disk image ID (from the prerequisites above). Required.
AZURE_SANDBOXES_DATA_APP_GROUP=lightdash-data-app
AZURE_SANDBOXES_DATA_APP_DISK_IMAGE=<data-app-disk-image-id>
AZURE_SANDBOXES_AI_WRITEBACK_GROUP=lightdash-writeback
AZURE_SANDBOXES_AI_WRITEBACK_DISK_IMAGE=<writeback-disk-image-id>

# Optional — sandbox size (XS/S/M/L, default M)
AZURE_SANDBOXES_RESOURCE_TIER=M

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.

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.

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.

1. Enable the Google Cloud APIs

gcloud services enable run.googleapis.com cloudbuild.googleapis.com \
  --project <your-project>

2. Build the gateway image

From the root of the Lightdash repository, run:

cd sandboxes/data-apps

STAGE=$(mktemp -d)
cp -R template lightdash-query-sdk.tgz "$STAGE/"
npm pack @computesdk/cloud-run --pack-destination "$STAGE" >/dev/null
tar -xzf "$STAGE"/computesdk-cloud-run-*.tgz \
  -C "$STAGE" package/dist/gateway.mjs
mv "$STAGE/package/dist/gateway.mjs" "$STAGE/gateway.mjs"

sed 's|^RUN chown -R user:user /app|RUN useradd -m -s /bin/bash user 2>/dev/null \|\| true|' \
  e2b.Dockerfile > "$STAGE/Dockerfile"

cat >> "$STAGE/Dockerfile" <<'EOF'

COPY gateway.mjs /gateway/gateway.mjs
ENV NODE_ENV=production
CMD ["node", "/gateway/gateway.mjs"]
EOF

gcloud builds submit "$STAGE" \
  --tag gcr.io/<your-project>/lightdash-sandbox-gateway:latest \
  --project <your-project>

3. Deploy the gateway

Generate a shared secret and deploy the image:

SANDBOX_SECRET=$(openssl rand -hex 32)

gcloud beta run deploy lightdash-sandbox-gateway \
  --image gcr.io/<your-project>/lightdash-sandbox-gateway:latest \
  --region <region> \
  --project <your-project> \
  --sandbox-launcher \
  --no-cpu-throttling \
  --session-affinity \
  --cpu 4 \
  --memory 16Gi \
  --concurrency 20 \
  --min-instances 1 \
  --max-instances 1 \
  --timeout 3600 \
  --allow-unauthenticated \
  --set-env-vars "SANDBOX_SECRET=$SANDBOX_SECRET"

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.

4. Configure Lightdash

Get the gateway URL:

gcloud run services describe lightdash-sandbox-gateway \
  --region <region> \
  --project <your-project> \
  --format='value(status.url)'

Set these environment variables on both the Lightdash backend and scheduler:

SANDBOX_PROVIDER=gcp-cloud-run
ANTHROPIC_API_KEY=sk-ant-...
GCP_CLOUD_RUN_SANDBOX_URL=<gateway-url>
GCP_CLOUD_RUN_SANDBOX_SECRET=<sandbox-secret>

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).

5. Restrict sandbox egress (recommended)

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):

   gcloud compute routers create sandbox-egress-router \
     --network <network> --region <region> --project <your-project>
   
   gcloud compute routers nats create sandbox-egress-nat \
     --router sandbox-egress-router --region <region> --project <your-project> \
     --auto-allocate-nat-external-ips --nat-all-subnet-ip-ranges
   

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:

   gcloud beta run services update lightdash-sandbox-gateway \
     --region <region> --project <your-project> \
     --network <network> --subnet <subnet> --vpc-egress all-traffic
   

   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.

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:

Image (default tag)	Built from	Used by
lightdash-sandbox:local	sandboxes/data-apps/	Data app generation
lightdash-ai-writeback:local	sandboxes/ai-writeback/	AI writeback

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.

Setup

1. Build the local sandbox images (each builds from sandboxes/<feature>/):

   ./sandboxes/data-apps/build-local-image.sh        # -> lightdash-sandbox:local
   ./sandboxes/ai-writeback/build-local-image.sh     # -> lightdash-ai-writeback:local
   

   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:

   SANDBOX_PROVIDER=docker
   # optional — these are the defaults:
   SANDBOX_DOCKER_IMAGE=lightdash-sandbox:local
   SANDBOX_AI_WRITEBACK_DOCKER_IMAGE=lightdash-ai-writeback:local
   

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.)

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=1800000        # auto-suspend a running-but-idle sandbox (default 30 min)
SANDBOX_SNAPSHOT_RETENTION_MS=604800000 # auto-terminate a suspended microVM (default 7 days); also how long a thread stays resumable

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.

Environment variable reference

Variable	Default	Description
SANDBOX_PROVIDER	e2b	Sandbox backend: e2b, lambda-microvm, azure-sandboxes, gcp-cloud-run, or docker.
ANTHROPIC_API_KEY	—	API key for the Claude Code agent running inside the sandbox.
E2B_API_KEY	—	E2B API key (required when SANDBOX_PROVIDER=e2b).
E2B_TEMPLATE_NAME	lightdash/lightdash-data-app	E2B template for data app sandboxes.
E2B_TEMPLATE_TAG	Lightdash version	Tag of the data app template to launch.
E2B_AI_WRITEBACK_TEMPLATE_NAME	lightdash/lightdash-ai-writeback	E2B template for writeback sandboxes.
E2B_AI_WRITEBACK_TEMPLATE_TAG	Lightdash version	Tag of the writeback template to launch.
LAMBDA_MICROVM_REGION	eu-west-1	AWS region the microVMs run in (lambda-microvm).
LAMBDA_MICROVM_DATA_APP_IMAGE_ARN	—	Image ARN for data app microVMs (required when SANDBOX_PROVIDER=lambda-microvm).
LAMBDA_MICROVM_AI_WRITEBACK_IMAGE_ARN	—	Image ARN for writeback microVMs (required when SANDBOX_PROVIDER=lambda-microvm).
LAMBDA_MICROVM_EXECUTION_ROLE_ARN	—	Optional IAM role the microVM assumes.
LAMBDA_MICROVM_INGRESS_CONNECTOR_ARN	AWS-managed ALL_INGRESS	Optional ingress network connector.
LAMBDA_MICROVM_EGRESS_CONNECTOR_ARN	AWS-managed INTERNET_EGRESS	Optional egress network connector.
AZURE_SANDBOXES_SUBSCRIPTION_ID	—	Azure subscription holding the sandbox groups (required when SANDBOX_PROVIDER=azure-sandboxes).
AZURE_SANDBOXES_RESOURCE_GROUP	—	Resource group holding the sandbox groups (required when azure-sandboxes).
AZURE_SANDBOXES_REGION	eastus2	Region the sandboxes run in (selects the data-plane endpoint).
AZURE_SANDBOXES_DATA_APP_GROUP	—	Sandbox group for data app sandboxes (required when azure-sandboxes).
AZURE_SANDBOXES_DATA_APP_DISK_IMAGE	—	Disk image ID for data app sandboxes (required when azure-sandboxes).
AZURE_SANDBOXES_AI_WRITEBACK_GROUP	—	Sandbox group for writeback sandboxes (required when azure-sandboxes).
AZURE_SANDBOXES_AI_WRITEBACK_DISK_IMAGE	—	Disk image ID for writeback sandboxes (required when azure-sandboxes).
AZURE_SANDBOXES_RESOURCE_TIER	M	Sandbox size: XS, S, M, or L.
AZURE_SANDBOXES_API_VERSION	2026-02-01-preview	Azure Sandboxes data-plane API version.
AZURE_SANDBOXES_TOKEN_SCOPE	https://management.azuredevcompute.io/.default	Entra token scope for the data plane.
GCP_CLOUD_RUN_SANDBOX_URL	—	URL of the sandbox gateway Cloud Run service (required when SANDBOX_PROVIDER=gcp-cloud-run).
GCP_CLOUD_RUN_SANDBOX_SECRET	—	Shared secret the gateway was deployed with (required when gcp-cloud-run).
SANDBOX_DOCKER_IMAGE	lightdash-sandbox:local	Local image for data app sandboxes (docker provider).
SANDBOX_AI_WRITEBACK_DOCKER_IMAGE	lightdash-ai-writeback:local	Local image for writeback sandboxes (docker provider).
SANDBOX_IDLE_TIMEOUT_MS	1800000 (30 min)	Auto-suspend a running-but-idle sandbox (lambda-microvm and azure-sandboxes). Ignored by e2b/docker.
SANDBOX_SNAPSHOT_RETENTION_MS	604800000 (7 days)	Lambda MicroVMs idle policy: auto-terminate a suspended microVM (also how long a thread stays resumable). Ignored by e2b/azure-sandboxes/docker.