Integration

Baseten integration

Baseten is a ML model deployment platform that enables teams to deploy, scale, and monitor machine learning models in production. Build inference APIs for any model, auto-scale based on traffic, deploy custom models or popular open-source models, and integrate with your ML workflow. Perfect for deploying LLMs, computer vision models, recommendation systems, and custom ML pipelines.

Connect Baseten How integrations work

Ask it in chat

Say what should happen.
The workflow builds itself.

When this happens, do that in Baseten.Built from the 24 Baseten tools below, on the connection you add once.

Common Baseten use cases

reduce infrastructure costs
enable scale-to-zero
remove unused models
remove old deployments
store HuggingFace token
view deployment history
audit configured secrets
find available GPU types
real-time ML predictions
inventory deployed models

What you can do with Baseten

Each of these is one operation on one endpoint, available both as a workflow step and as a tool an AI agent can call on its own. Nothing bundles several operations into one action, so what a run did stays readable afterwards.

Most endpoints run on the free plan. A few, mainly the ones that publish on your behalf, need a paid plan.

Deployment, 12 operations

activate_deploymentActivate deploymentPOSTActivates a deactivated Baseten deployment to make it available for inference.
deactivate_deploymentDeactivate deploymentPOSTDeactivates a Baseten deployment, stopping all replicas and inference traffic.
delete_deploymentDelete deploymentDELETEDeletes a specific deployment of a Baseten model by ID.
get_deploymentGet deploymentGETRetrieves detailed information about a specific model deployment including status, replicas, and autoscaling settings.
get_development_deploymentGet development deploymentGETRetrieves the development deployment for a Baseten model.
get_production_deploymentGet production deploymentGETRetrieves the production deployment for a Baseten model.
list_deploymentsList deploymentsGETLists all deployments of a Baseten model with status, autoscaling settings, and configuration.
promote_deploymentPromote deploymentPOSTPromotes a deployment to production status, routing all inference traffic to that deployment.
promote_development_deploymentPromote development deploymentPOSTPromotes the development deployment to production, creating a new production deployment.
update_autoscaling_settingsUpdate autoscaling settingsPATCHUpdates autoscaling configuration for a Baseten model deployment. Adjust replica counts and scaling behavior.
wake_development_deploymentWake development deploymentPOSTWakes a scaled-to-zero Baseten development deployment.
wake_production_deploymentWake production deploymentPOSTWakes a scaled-to-zero Baseten production deployment to avoid cold start latency.

Prediction, 6 operations

cancel_async_requestCancel async requestDELETECancels a queued async prediction request on Baseten.
get_async_request_statusGet async request statusGETRetrieves status metadata for an async Baseten prediction request.
run_async_inferenceRun async inferencePOSTRuns asynchronous inference on a Baseten model. Returns a request ID immediately for status tracking.
run_development_async_inferenceRun development async inferencePOSTRuns asynchronous inference on a Baseten model's development deployment for testing.
run_development_inferenceRun development inferencePOSTRuns synchronous inference on a Baseten model's development deployment for testing.
run_production_inferenceRun production inferencePOSTRuns synchronous inference on a Baseten model's production deployment. Sends input and receives predictions in real-time.

Model, 3 operations

delete_modelDelete modelDELETEDeletes a Baseten model and all deployments. Stops replicas and removes inference endpoints permanently.
get_modelGet modelGETRetrieves detailed information about a specific Baseten model including deployment IDs and instance type.
list_modelsList modelsGETLists all deployed ML models in your Baseten workspace. Returns model IDs, names, deployment counts, and active deployment IDs.

Secret, 2 operations

list_secretsList secretsGETLists all secrets in your Baseten workspace. Returns names and creation dates, not values.
upsert_secretUpsert secretPOSTCreates or updates a secret in your Baseten workspace for use in model deployments.

Instance type, 1 operation

list_instance_typesList instance typesGETLists all available GPU and CPU instance types for Baseten model deployments.

Use Baseten from Claude Code, Cursor or Codex

Connect Baseten in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its Baseten operations become tools the client can call, next to every other integration you connected.

Claude Code

claude mcp add --transport http livecontext https://livecontext.ai/mcp --header "X-API-Key: YOUR_API_KEY"

Codex (~/.codex/config.toml)

[mcp_servers.livecontext]
url = "https://livecontext.ai/mcp"

[mcp_servers.livecontext.http_headers]
"X-API-Key" = "YOUR_API_KEY"

Cursor, Claude Desktop and other JSON configs

{
  "mcpServers": {
    "livecontext": {
      "type": "http",
      "url": "https://livecontext.ai/mcp",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}

Set up the MCP server step by step

How to connect Baseten

Create your workspace
Sign up free. Nothing to install.
Connect Baseten once
Add your Baseten connection in Settings. Every tool from this integration reuses it, and secrets are encrypted at rest and visible only to you.
Describe the job in chat
The workflow builds itself in front of you and picks the endpoints it needs. Drag the steps yourself on the canvas whenever you would rather be explicit.

Baseten integration FAQ

What can you do with the Baseten integration?

LiveContext exposes 24 operations on Baseten, covering deployment, prediction, model and secret. Common uses include reduce infrastructure costs, enable scale-to-zero and remove unused models.