Standard billing software has a seats problem (this isn't the first time we say this).

It was built for a world where the unit of value was a human user. You count seats, multiply by price, send invoice. That logic is clean and predictable because the cost of serving one more user is approximately zero.

AI products broke this model.

Every inference call your product makes costs real money. The compute behind a single Claude Opus 4.6 call, a Nano Banana image, or a code completion isn't free. It's a line item on your infrastructure bill.

When a customer makes 10,000 API calls in a month and you charged them a flat $49, you've just discovered the new problem: pricing that doesn't reflect compute costs is margin erosion in disguise.

This is why standard billing software fails AI companies, for the most part. Tools like Stripe were built to count seats or handle one-time purchases.

AI companies need to count tokens, credits, inference events, and outcomes, then reconcile that against variable infrastructure costs to understand whether a customer is profitable.

That's a different problem, so it obviously needs different tools.

What makes AI billing different?

Before comparing platforms, it's worth naming the specific things AI billing requires that standard tools don't handle.

Token metering

AI products bill on consumption of compute: tokens, API calls, images generated, seconds of audio processed. These are high-volume, sub-cent events that need to be ingested, aggregated, and priced in real time. Standard billing systems were designed for monthly subscription events, not millions of metering events per day.

Credit systems as financial architecture

Many AI companies sell prepaid credits. On the surface this looks like a payment method. It's actually an architectural decision: credits are a liability on your books until they're consumed, they can roll over or expire, they can be shared across a team or locked per user, and they need to be reconciled against actual usage. "Credit management" built on top of a subscription billing tool is usually four spreadsheets pretending to be a ledger.

Hybrid pricing models

Most AI companies charge a base subscription plus usage overage, or some variation. This means every invoice combines fixed and variable components, applied against potentially different pricing tiers, with possible volume discounts. Standard billing tools handle subscriptions. Standard billing tools handle metering. Very few handle both accurately in the same invoice.

Inference cost visibility. The real question for AI companies isn't "how much did this customer pay?" It's "how much did this customer cost us?" If you can't see compute cost next to revenue in real time, you're pricing blind. That's not a dashboard problem. It's a data architecture problem in your billing layer.

What to look for in AI billing software

8 AI billing software platforms compared (2026)

Platform deep dives

Solvimon

Solvimon was built by Kim Verkooij (ex-VP Product, Adyen) and Etienne Gerts (ex-SVP Technology, Adyen), who built and operated Adyen's internal billing engine at €970B+ in annual payment volume.

The core architectural difference: Solvimon treats credits and tokens as financial primitives, not as metadata fields or custom line items. A credit wallet in Solvimon has rollover logic, expiry rules, per-user or pooled allocation, and real-time burn-down tracking that connects directly to the revenue ledger.

When your top 5% of users consume 75% of your compute budget while paying the same flat fee as everyone else, Solvimon is the system that surfaces it before you find out on the infrastructure bill.

Solvimon handles hybrid pricing natively: subscriptions, usage metering, credits, outcome-based billing, and enterprise custom contracts. PSP-agnostic (Stripe, Adyen, Checkout.com).

Best for: AI-native companies scaling hybrid models or moving from PLG to SLG. Solvimon is the right fit when pricing needs to evolve faster than engineering can support.

For a deeper look at how Solvimon handles AI billing specifically, see Solvimon for AI.

Chargebee

Chargebee is the most mature platform on this list and the safest default for subscription-heavy SaaS companies adding light AI feature billing. Its 200+ integrations, mature dunning management, and strong Salesforce/NetSuite connectivity make it reliable for standard models. The limitation for AI billing: it wasn't designed for real-time token metering or deep credit architecture. Adding AI feature billing on top of Chargebee often requires the same custom engineering workarounds you were trying to avoid. Best for: Mid-market SaaS with standard subscriptions who are adding AI features at the margin, not rebuilding pricing around them.

Stripe Billing

Stripe launched an LLM token billing feature that auto-syncs token prices for OpenAI, Anthropic, and Google models. You set a markup percentage, and it handles metering through the Stripe AI Gateway, integration partners (OpenRouter, Vercel, Cloudflare), or self-reported usage. This solves the multi-meter problem that forced companies to collapse token types into abstract billing units.

In January 2026, Stripe acquired Metronome for ~$1B, adding enterprise-grade metering to its ecosystem.

The caveats: the LLM token feature is still rolling out. The 0.7% billing fee on top of payment processing fees pushes total cost toward 1.5%, which compounds at scale. Credit systems are limited. And you're locked to Stripe for payments, which matters when enterprise customers need invoicing through a different PSP.

If you're evaluating Stripe alternatives specifically, we wrote a deeper comparison: Stripe Billing alternatives (2026).

Best for: Early-stage AI companies already on Stripe who want the simplest path to token billing. If you need PSP flexibility or deep credit architecture, look elsewhere.

Orb

Orb is a usage-based billing engine built for high-volume metering. Its ingestion layer handles 250K+ events per second with deduplication, and billable metrics are defined via SQL or a visual interface. Engineering teams get precise control over how token usage maps to charges.

The credit system uses a block-based architecture: multiple credit blocks per customer, each with its own expiry date and amount. More flexible than a single balance field, but requires careful configuration for scenarios like pooled team credits with tiered exchange rates. (For more on why credits are a ledger problem, not a pricing problem, see our deep dive.)

Orb Simulations let you model pricing changes against historical data before pushing them live. Useful when AI model economics shift and you need to understand the revenue impact before committing.

The limitation: Orb is Stripe-only for payment processing. If you're in a region with limited Stripe coverage, or enterprise customers need invoicing through a different PSP, that's a hard blocker.

Best for: Engineering-heavy AI startups already on Stripe who want maximum control over pricing logic and don't mind investing integration time.

Lago

Lago is the open-source option. The core billing engine is free and self-hostable, with a cloud-hosted version for teams that don't want to manage infrastructure.

It ships pre-built pricing templates for OpenAI and Mistral models, with dimension-based aggregation that separates input from output tokens at different rates per model. Mistral AI uses Lago in production, generating 32,000+ invoices monthly with per-token billing.

The wallet system supports up to 5 active wallets per customer with scoping to specific billable metrics, configurable priority, and auto-topup rules.

Where Lago stands out: multi-PSP support. Native integrations with Stripe, Adyen, GoCardless, and Cashfree. If PSP flexibility matters, Lago is the only open-source option that handles this natively.

The tradeoff: self-hosted Lago requires Postgres, Redis, and ClickHouse at scale. No built-in dunning or collections workflows. You're trading platform cost for engineering cost.

Best for: Technical teams that want full control over their billing infrastructure and need PSP flexibility.

Metronome (now part of Stripe)

Before Stripe acquired it for ~$1B in January 2026, Metronome was the metering layer behind OpenAI, Anthropic, Databricks, and NVIDIA. It processes billions of usage events daily on streaming infrastructure built on Apache Kafka.

The architecture separates metering, pricing, and contract management into distinct layers. You can change pricing logic without touching measurement code. Post-acquisition, Metronome's roadmap is merging with Stripe Billing. If you're committed to Stripe's ecosystem, that's a feature. If you need PSP flexibility or want a billing vendor whose roadmap isn't tied to a payments company's priorities, that's a structural constraint.

Implementation is heavy: expect weeks of configuration, SQL knowledge, data pipeline setup, and ongoing data engineering bandwidth. No historical data backfilling means you can't retroactively correct pricing errors, and there's no native invoicing, you're relying on Stripe.

Best for: Enterprise AI companies with dedicated billing engineering teams who are already deep in the Stripe ecosystem and need proven scale. Evaluate Metronome knowing that choosing it means choosing Stripe permanently.

For a more detailed comparison of token billing platforms including Metronome, see: Token billing for AI: 7 platforms compared.

Maxio

Maxio came out of the merger of SaaSOptics, Chargify, and RevOps.io. It combines billing operations with financial reporting and ASC 606 revenue recognition in a single platform. CFOs like it because they get SaaS metrics (MRR, ARR, churn, retention) alongside billing without running separate tools.

Maxio launched Maxio Metering in 2025, which added real-time usage event ingestion and multi-dimensional metering. You can track multiple attributes per event (product tier, geography, infrastructure cost) and price based on any combination. It supports per-unit, tiered, volume, and hybrid models.

The limitation for AI billing: Maxio was built subscription-first. The metering capabilities are newer and less battle-tested at the scale that dedicated usage-billing platforms like Orb or Metronome operate at. Credit ledger depth is limited compared to Solvimon. Setup takes engineering time and the UI reflects its merged-product heritage.

Best for: Finance-led SaaS companies that need billing plus ASC 606 compliance plus SaaS metrics in one tool, and are adding usage-based AI features on top of an existing subscription model.

Paddle (and a note on Polar.sh)

Paddle is a Merchant of Record, which puts it in a different category from the other platforms on this list. Your customers buy from Paddle, Paddle handles global tax compliance, fraud, chargebacks, and payment processing across 200+ countries, and then Paddle pays you.

For AI companies, the MoR model solves the tax headache of selling globally. You don't need to register for VAT in 30 countries.

The limitation: Paddle's usage-based billing capabilities are basic compared to purpose-built AI billing platforms. If you need deep credit ledger logic, real-time token metering at scale, or hybrid P&L visibility showing compute cost next to revenue, Paddle doesn't solve that problem. Its 5% + $0.50 per transaction fee structure also gets expensive fast at higher volumes.

Polar.sh is worth mentioning in the same breath. It's an open-source MoR gaining traction with developers and indie SaaS builders, charging 4% + $0.40 per transaction with event ingestion adapters for OpenAI and Anthropic. Polar recently raised a $10M seed from Accel and is moving fast. If you're a solo developer shipping an AI tool and want billing live in an afternoon, Polar can do that. Where both Paddle and Polar fall short for AI companies at scale: neither handles hybrid enterprise contracts, deep credit architecture, or inference cost reconciliation. They solve the payments and tax problem. The billing architecture problem is a different animal.

Best for: SaaS companies that primarily need subscription billing with tax compliance handled, and whose AI billing needs are simple. If your main problem is "I don't want to deal with international tax," Paddle (or Polar for developer tools) is a fit. If your main problem is "I need to meter tokens and reconcile them against inference cost," look at the platforms higher on this list.

Decision framework: How to choose AI billing software

Start with the question your billing system needs to answer.

1. If it's "what did this customer consume this month?" you need strong metering.

2. If it's "is this customer profitable?" you need inference cost reconciliation.

3. If it's "how do we model a custom enterprise contract on top of a usage model?" you need hybrid billing with CPQ.

AI companies in 2026 typically need all three. The billing systems designed for one of them tend to break when you try to make them do the other two.

You should ask this question early: does my billing infrastructure support the pricing model I want to run in 18 months? Not the one they're running today.

If the answer is no, or "we'd need to build around it", that's the signal to evaluate alternatives before the complexity compounds.

For more on the AI billing landscape, see:

• Token billing for AI: 7 platforms compared

• AI credit pricing models: how tokens, credits, and hybrid billing actually work

• Best billing systems for AI startups in 2026

Solvimon handles hybrid pricing, AI token and credit metering, and enterprise contracts in one system. Built by the team that scaled Adyen to €970B+ in annual volume. Free up to $3M billed, then 0.40% of volume. Check out Solvimon's AI offering

FAQ

Why can't I just use standard subscription billing for my AI app?

Standard billing was designed for "seats", fixed monthly costs for human users. AI products incur variable infrastructure costs for every interaction (inference). If you don't meter usage in real-time, you risk margin erosion where a heavy user costs you more in compute than they pay in their subscription.

What is the difference between token metering and credit systems?

Tokens are the raw units of compute (e.g., words generated, images created, or seconds of audio). Credits are the financial layer that sits on top. A customer buys a "wallet" of credits, and those credits are burned down as tokens are consumed. Effective AI billing requires managing both: tracking the raw usage and the financial liability of the credits.

What does "Inference Cost Reconciliation" mean?

It is the process of comparing what a customer paid you against what their API calls (to OpenAI, Anthropic, etc.) actually cost you. Without this, you are pricing in the dark. Platforms like Solvimon surface this P&L data so you can identify unprofitable customers in real-time.

Is there an open-source option for AI billing?

Yes, but they typically require significant engineering overhead to manage the underlying infrastructure like ClickHouse and Redis - as well as deploy it yourself. We don't recommend this.

What happened to Metronome?

As of January 2026, Metronome was acquired by Stripe. It is being integrated into the Stripe Billing ecosystem to provide enterprise-grade metering for high-volume AI companies like OpenAI and Anthropic, though it currently requires a Stripe-only commitment.

What is a Merchant of Record (MoR) and do I need one?

An MoR like Paddle legally sells the product on your behalf. This means they handle global sales tax, VAT, and compliance in 200+ countries. You should choose an MoR if you're comfortable sharing your revenue in exchange for simpler operations.