Lago

Agentic-first, open-source monetization infrastructure for AI products
Meter tokens, compute, API calls, or any product usage.
Turn usage into pricing, credits, entitlements, invoices, payments, and revenue.
Use Lago Direct for your own product, or Lago Embedded to power monetization for your customers.

Run the AI billing demo · Documentation · Talk to us about Lago Cloud

“Lago has been able to follow the pace of our releases and has allowed us to focus on what we do best.”
Timothée Lacroix, CTO at Mistral AI · Read the customer story

See Lago price an AI workload

Run the maintained demo from this repository:

./examples/agentic-ai-demo/run.sh

It starts the Lago version that matches this checkout, creates a disposable local organization, and prices three illustrative AI requests. Each request sends one input-token event and one output-token event:

3 AI requests
5,000 input tokens  x $0.000002 = $0.01
1,250 output tokens x $0.000008 = $0.01
Lago usage total                   = $0.02

Docker Compose starts an isolated Lago service, then a small script seeds and verifies the example through Lago's API. It retrieves current usage, independently reconciles the result, and retries one transaction to confirm that usage does not increase. Everything stays on your machine; the demo does not access Lago Cloud. The bundled credentials and API key are disposable and only intended for this local demo.

Log into http://localhost:8080 with [email protected] / agentic-ai-demo-local-password, then open Customers → Agentic AI Demo Customer → Agentic AI Demo subscription → Usage.

Requirements: Docker, curl, and jq. If ports 8080 or 3001 are occupied, set LAGO_DEMO_UI_PORT and LAGO_DEMO_API_PORT before running. The demo keeps Lago running so you can inspect the customer, metrics, plan, subscription, events, and usage in the UI. Remove its isolated Compose project and data volume when you are done:

./examples/agentic-ai-demo/run.sh --cleanup

Run Lago's maintained Agentic AI demo by following this README. Keep it local;
do not modify source files or unrelated Docker resources. When it passes, give
me the UI URL, token usage and charges, idempotency evidence, and cleanup
command. Then offer to connect this agent to Lago's local MCP server.

What is Lago?

Lago is the programmable system between product usage and revenue. Send events from your application, turn them into billable metrics, apply pricing and entitlements, then generate invoices and collect payments.

Usage events -> Metering -> Pricing and credits -> Entitlements -> Invoices -> Payments -> Revenue

Use Lago to launch and change pricing without rebuilding billing:

• tokens by model, input, output, cache, reasoning, or tool call
• GPU, CPU, storage, and other compute consumption
• API calls, transactions, seats, active users, or custom events
• prepaid credits with automatic top-ups
• subscriptions with allowances, minimum commitments, and overages
• self-serve plans and negotiated enterprise contracts in the same system

Lago is headless and API-first. Your application, internal tools, and agents use the same billing primitives through the REST API, SDKs, webhooks, and MCP server. Product, finance, and operations teams can use the Lago UI when a human interface is faster.

How Lago fits into your stack

Lago keeps metering and pricing independent from payment processing. Connect Stripe, Adyen, GoCardless, or another provider without making its product catalog your source of truth.

Lago Direct and Lago Embedded

The same Lago engine supports two operating models:

Lago Direct is the standard way to use Lago: send your own product usage, model your pricing, and bill your customers. Lago Embedded uses the same primitives to let platforms, marketplaces, AI builders, fintech products, and developer tools offer metering and billing to their customers.

Both models start with Lago's open-source billing engine and can be paired with Premium deployment, customization, security, and support options. With Lago Embedded, you control the customer experience and which capabilities your users can access.

Agentic-first by design

Agentic-first means Lago's billing model is available as structured, inspectable interfaces instead of being trapped in a dashboard.

The MCP server exposes read and write tools and inherits the permissions of its Lago API key. Treat agent credentials as privileged, control who can access the agent, review tool calls, and keep human confirmation around sensitive billing changes.

Platform