What pricing models and revenue workflows can Lago support?

Lago supports usage-based, subscription, and hybrid pricing, and covers the revenue workflow from quote to final invoice. It is an open-source billing platform aimed at companies that need to meter consumption (including AI tokens), apply credits and adjustments, and report on revenue. If your pricing is purely flat-rate with no metering, credit, or reporting needs, Lago's metering and usage features may be more than you need; if you bill on consumption, subscriptions, or a mix of both, its feature set maps directly to that work.

Pricing models Lago covers

Lago is described as supporting "any pricing model," with three patterns shown explicitly on the site:

• Usage-based billing — charges derived from metered consumption. The site's AI example meters input, output, and cached tokens separately and prices each.
• Subscription billing — recurring plans, listed alongside usage and hybrid pricing in the site's description.
• Hybrid pricing — combining subscription and usage in one bill, which the site names as a supported model.

The AI metering example makes the usage model concrete. In one illustration, 1.2M tokens are metered and billed as:

A second illustration shows the same mechanics at finer grain: 1,185,000 tokens metered over 18 days (input 654.00K, output 236.00K, cached 295.00K), with each token type priced and attributed across models — for example, input tokens split 50% GPT-4.1, 30% Sonnet 4, 20% Gemini Flash. That model-level attribution is what lets you bill different rates per model within one usage stream.

The end-to-end revenue workflow

Lago positions itself as "one platform for every revenue workflow. From the first quote to the final invoice." The capabilities named on the page include:

• Billing & invoicing — generating invoices from metered and subscribed charges.
• Customer management — maintaining customer records and balances.
• Pricing & packages — configuring the plans and rates customers are billed on.
• Promotions & adjustments — modifying amounts before they reach the final invoice.
• Prepaid credits — drawing down a customer's credit balance against charges.
• Spend alerts — triggering when usage crosses a threshold.
• Revenue & insights — reporting on top of billing data.

How credits and alerts work in practice

The site's example ties several of these together in one flow: usage reaches a $1,500 spend alert, $500 in prepaid credits is applied to a $1,560 invoice, leaving $1,060 due. A separate panel shows a customer's credit balance with an expiration field ("No expiration date" in the example), so credit expiry is a configurable attribute rather than a fixed rule.

Reporting and revenue insights

Lago exposes billing data for custom reporting. The page shows a Finance Assistant that turns a request — "Get the MRR for the past quarter. Segment it per customer." — into a revenue chart and report. The sample MRR report breaks the past quarter into:

• New MRR (rising from $10,000 to $25,000 across Dec 2023–Mar 2024)
• Expansion MRR ($20,000 to $35,000)
• Lost MRR (−$10,000 to −$15,000)
• MRR growth rate (10% to 15%)

Total shown: $500,100.00, with a 100.54% change in the selected period. Because the platform is open source, reports can be built on top of Lago rather than only inside it.

Who this fits

The page names CoreWeave, Mistral AI, and PayPal as customers and frames the product for "fast-growing AI scaleups. And global enterprises." Two signals matter for fit:

• AI and token-based businesses — the metering model is built around input, output, and cached tokens, with per-model attribution, which suits products whose cost basis is model inference.
• Teams that need finance-grade output — the workflow runs through to invoices, credits, and MRR reporting, so it targets finance and revenue operations, not just engineering metering.

Enterprise-grade security and privacy is listed as a platform attribute; the page does not detail specific certifications, so verify those against your own requirements before committing.

What to check before deciding

• Your pricing shape — if it is usage, subscription, or hybrid, Lago's named models apply. If it is something outside those, confirm against the open-source codebase or a demo.
• Credit and promotion rules — the examples show prepaid credits and adjustments; check whether your specific expiry, rollover, or proration rules are configurable.
• Reporting depth — MRR segmentation is demonstrated; if you need other revenue metrics, confirm they can be built on the platform's data.
• Pricing and deployment terms — the site links to a pricing page and a "Book demo" call to action, but the provided material does not state plan costs or access conditions. Treat pricing and any login or self-host requirements as unconfirmed until you check those pages directly.