Bring Usage Based Billing
to Pydantic AI
Learn how to connect Lago to Pydantic AI and start using 12 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Lago MCP Server?
Connect Lago to your AI agent to automate your metering and billing infrastructure. Lago is the open-source alternative to Stripe Billing, designed for complex usage-based pricing models.
What you can do
- Customer Management — Create and update customer profiles with
upsert_customerand retrieve details withget_customer. - Subscription Lifecycle — Assign plans to customers using
create_subscriptionand monitor them withget_subscription. - Billing Infrastructure — Define billing plans with
create_planand set upcreate_billable_metricto track consumption. - Usage Tracking — Send real-time usage data with
send_eventorbatch_eventsto trigger accurate billing. - Financial Operations — Manage wallets, apply coupons, and list invoices to keep your revenue operations running smoothly.
How it works
- Subscribe to this server
- Enter your Lago API Key
- Start managing your billing logic from Claude, Cursor, or any MCP-compatible client
Who is this for?
- SaaS Founders — quickly check customer subscription status or update plans without leaving the chat.
- Growth Engineers — automate the creation of coupons and wallets for promotional campaigns.
- Finance Teams — list invoices and verify billable metrics through natural language queries.
Built-in capabilities (12)
Apply a coupon to a customer
Send a batch of usage events
Create a billable metric
Create a coupon
Create a new billing plan
Assign a plan to a customer (create subscription)
Create a wallet for prepaid credits
Retrieve a customer by external ID
Retrieve a subscription by external ID
List all invoices
Send a usage event
Requires an external_id. Create or update a customer in Lago
Why Pydantic AI?
Pydantic AI validates every Lago tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
- —
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Lago integration code
- —
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
- —
Dependency injection system cleanly separates your Lago connection logic from agent behavior for testable, maintainable code
Lago in Pydantic AI
Lago and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Lago to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Lago in Pydantic AI
The Lago MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 12 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
Lago for Pydantic AI
Every tool call from Pydantic AI to the Lago MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I track a new usage event for a customer?
Use the send_event tool. You'll need to provide the customer's external ID and the metric code to record the consumption in real-time.
Can I create a new billing plan with specific charges?
Yes, use the create_plan tool. You can define the name, interval, and base amount, as well as pass a JSON object for specific charges.
Is it possible to apply a discount to a customer?
Absolutely. First, create a coupon using create_coupon, then use the apply_coupon tool to link it to a specific customer's external ID.
How does Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your Lago MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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