Feb 19, 2026
Arnon Shimoni
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
Capability | Why It matters | Red flag to watch out for |
|---|---|---|
Real-time usage metering | Tokens accumulate fast. Billing on stale data means inaccurate invoices and disputes | "Usage syncs nightly" |
Native credit/wallet primitives | Credits aren't line items; they're financial objects. You need rollover logic, expiry, pooling, and burn-down tracking | "Use the notes field for credit balances", or "Credits are managed separately" |
Hybrid invoice support | A subscription + usage invoice shouldn't require reconciliation across two systems | Separate platforms for "subscription billing" and "usage billing" |
Inference cost reconciliation | You need revenue-per-customer next to cost-per-customer | No cost data at all |
Engineering-light pricing changes | AI pricing evolves fast. A pricing change shouldn't require a sprint | "Engineering changes code to update pricing logic" |
PSP flexibility | Don't get locked into one payment processor; your payments stack will evolve | Only works with one processor |
6 AI billing software platforms compared (2026)
Platform | Best For | AI Billing Strengths | Limitations | Pricing |
|---|---|---|---|---|
Solvimon | AI-native companies billing by tokens, credits, outcomes, and hybrid contracts | Credits and tokens as first-class billing primitives; real-time metering; hybrid P&L visibility; built by ex-Adyen (€970B+ volume) | Newer platform; not designed for simple subscription-only companies | |
Orb | High-volume SaaS with complex usage-based pricing logic | Advanced event streaming; fast pricing iteration; strong PLG-focus | Engineering-first; limited no-code interface; less suited to enterprise contract billing | Custom |
Chargebee | Mid-market SaaS adding light AI feature billing on top of subscriptions | Mature integrations; strong dunning; large ecosystem | Usage billing not real-time; AI metering is not native; limited credit ledger depth | From $599/mo |
Lago | Engineering-led teams that want open-source billing infrastructure | Highly customizable; open-source; strong usage event primitives | Requires engineering ownership; no native CPQ; no no-code UI for non-technical teams | Free for the limited open-source version / ~$1-3K+/mo |
Stripe Billing | Early-stage AI companies on Stripe Payments, simple pricing | Best developer UX; easy to start; seamless payments integration | 0.7% revenue fee scales poorly; token/credit metering requires custom code; no hybrid billing native | 0.7% of billing volume + Stripe fees - often closer to 1.5% |
m3ter | Enterprise AI companies with complex multi-geo billing and FX | Prepaid drawdown accounts, complex tier logic, FX handling | Steep learning curve; designed for technical buyers; limited RevOps UI | Custom |
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.
It 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.
Orb
Orb is purpose-built for metered billing at high volume. Its event ingestion and pricing abstraction layers are designed for companies that make frequent pricing changes and handle millions of usage events per day.
Where it shines: fast iteration on complex usage pricing. Where it's limited: enterprise contracts and CPQ aren't Orb's focus, and the interface is built for engineers, not RevOps.
Best for: Engineering-led, high-volume SaaS companies where usage-based pricing is the core model (not hybrid).
Lago (Open-Source)
Lago is the open-source billing infrastructure option. If your team wants complete architectural control to build credits, usage metering, and pricing logic exactly as you need it, Lago provides the foundation.
The honest tradeoff: you own the infrastructure. That means engineering investment, maintenance, and ongoing development. For teams with the appetite and the bandwidth, this is a powerful foundation. For teams trying to offload billing complexity, it moves the problem rather than solving it.
Best for: Developer-led companies with strong engineering capacity who want full ownership of billing logic.
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.
Decision framework: How to choose AI billing software
Start with the question your billing system needs to answer.
If it's "what did this customer consume this month?" you need strong metering.
If it's "is this customer profitable?" you need inference cost reconciliation.
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.
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 $5M billed, then 0.40% of volume. Check out Solvimon's AI offering