Best usage-based billing software for GenAI and LLM apps

Your GenAI application just processed 50 million tokens. Your billing system calculated the invoice three days later. By then, you have already served another 200 million tokens with no clarity on whether you are making or losing money on each customer.

This lag between consumption and billing creates real problems for AI companies. Token costs vary by model, context length, and provider. Customers expect timely visibility into spend. Finance teams need reliable billing and revenue reporting. Engineering teams also need pricing changes, backfills, credits, and enterprise contract terms to work without turning every monetization decision into a new billing project.

Usage-based billing ties some or all charges to measured consumption. For GenAI products, it can operate as pure pay-as-you-go pricing or as the variable component of a hybrid subscription, commitment, or credit model. Common usage metrics include tokens processed, API calls made, compute time consumed, or combinations of metrics that reflect customer value or underlying cost.

The operational challenge is broader than metering alone. Pricing decisions span Product, Sales, Engineering, Finance, and RevOps. As products add credits, commitments, custom enterprise terms, more dimensions, and new SKUs, billing complexity compounds. The strongest platforms help teams execute pricing changes, preserve billing accuracy, provide customer visibility, and connect variable usage to finance workflows without repeatedly rebuilding billing logic in product code.

This guide examines seven billing platforms through a GenAI and LLM billing rubric that prioritizes usage event ingestion and data integrity, metric flexibility, dimensional pricing, credits and commitments, customer spend visibility, correction and backfill capability, finance workflow depth, deployment flexibility, and pricing. The ranking reflects fit across those criteria, with particular emphasis on the operational demands of token, API, compute, and hybrid monetization.

Key takeaways

• Dimensional pricing can matter for complex AI economics: AI products whose costs or customer value vary across models, regions, workloads, or service tiers can benefit from dimensional pricing. Many AI products also use a simpler single usage metric or hybrid model, and Stripe's 2026 AI pricing guidance recommends starting with the simplest model that fits the customer.
• Low-latency visibility can reduce billing surprises: Timely usage calculations combined with dashboards, alerts, and spend controls can reduce bill shock and surface anomalies earlier than end-of-period billing. Visibility is most valuable when customers can understand what drives their spend and finance teams can trace charges back to usage.
• Credit systems are a common AI monetization pattern: Prepaid credits are frequently used where companies want upfront commitment and customers want spend control. Platforms with native credit balances, drawdown tracking, expiration handling, and scoped controls can simplify implementation compared with custom prepaid logic.
• Data integrity matters as much as event volume: GenAI apps can generate large volumes of usage events. Billing infrastructure needs ingestion capacity plus idempotency, deduplication, normalization, late-event handling, observability, and correction workflows to protect billing accuracy as volume grows.
• Usage-native architecture can reduce cross-functional billing work: Billing systems that connect usage, pricing, invoicing, corrections, and accounting outputs can reduce reconciliation work and recurring engineering involvement. This architecture is not an ASC 606 requirement, and appropriate revenue recognition still depends on contract terms, performance obligations, variable consideration, and accounting judgment.

1. Orb

Orb is a revenue design platform purpose-built for usage-based and hybrid billing, including complex consumption models used by AI, API, and infrastructure companies. The platform combines metering infrastructure, flexible pricing, simulations, invoicing, and finance workflows on a usage-focused billing foundation. Adyen completed its acquisition of Orb on July 1, 2026. Orb continues to operate as a stand-alone product, and customers can continue using their preferred payment-processing partner.

Key capabilities for GenAI apps

• Dimensional pricing engine: Orb's dimensional price groups support pricing across multiple usage dimensions, such as region, instance type, and environment, using a single pricing configuration for dimension combinations.
• SQL-defined billable metrics: Orb lets teams define billable metrics with SQL, including averages, maximums, minimums, and custom calculations beyond simple event counts. This flexibility supports token-based pricing, compute billing, and hybrid models.
• Scaled usage event ingestion: Orb supports scaled ingestion on its standard metering path and offers Hosted Rollups for configured event streams that benefit from aggregation during ingestion. Orb's metering infrastructure also supports higher-volume aggregation capabilities that can ingest billions of events per day.
• Pricing simulation: The Simulations feature lets teams model price changes against historical usage data before deployment, showing projected customer and revenue impact without affecting production billing.
• Prepaid credit systems: Orb supports prepaid credits, expiration handling, automated usage deductions, and configurable credit balances for usage-based and hybrid models.

GenAI use cases

Orb works with companies including Perplexity and Vercel. The platform supports token metering, API call billing, compute usage pricing, and dimensional pricing for AI and developer-platform monetization.

For generative AI products, Orb can price by dimensions such as model type, region, workload, or other event properties when the business model requires it. Orb ingests usage and uses a query-based billing architecture for invoice generation and revenue reporting. A separate streaming path powers low-latency alerts and threshold workflows, while the Experience Kit provides customer-facing live usage dashboards and billing experiences.

Finance integration

Orb's finance workflows include a native NetSuite integration that creates standard transaction objects rather than summary approximations. Orb's revenue reporting covers recognized, deferred, billed, and unbilled revenue. Its revenue recognition product is designed to support GAAP and ASC 606 accounting workflows. Accounting period locks keep closed periods stable, with catch-up adjustments flowing into the next open period when later billing activity affects a closed period.

Why Orb leads for AI billing

Orb's differentiation is not merely that it supports usage billing. Its standard metering architecture retains raw usage events, and its query-based billing engine computes invoices from usage history. This foundation gives Product and Finance a durable billing data layer while reducing the need to encode pricing logic repeatedly in product services.

The architecture supports retroactive pricing and usage backfills, with affected draft or pending billing recalculated from underlying usage history. Issued invoices can be corrected through auditable credit note workflows. Combined with simulations, this helps teams evolve metrics, pricing, and contract terms while preserving traceability.

Orb states that it was named Best Fintech Startup of 2025 by FinTech Breakthrough. Vercel reported an 80% decrease in time required to build and launch billing for new products. Orb also reports that Replit has seen 40x revenue growth since using Orb to monetize usage.

When to choose Orb

• You need dimensional pricing across model type, region, volume tiers, and other variables
• Your team needs pricing simulation before deploying changes to production
• Finance needs revenue reporting and accounting system integration alongside usage billing
• Product and RevOps need pricing flexibility without making Engineering the gatekeeper for every change
• You need correction and backfill workflows grounded in retained raw usage events

2. Metronome

Metronome provides usage-based billing infrastructure focused on metering, pricing, and analytics for usage-driven businesses. Stripe completed its acquisition of Metronome on January 14, 2026, and Metronome is now part of the Stripe ecosystem.

Key capabilities for GenAI apps

• Usage event ingestion: Metronome supports usage event ingestion for usage-based products.
• Usage dashboards and alerts: Metronome supports usage metering, alerting, and embeddable billing dashboards for customer-facing spend visibility.
• Pricing configurations: Metronome supports usage-based, seat-based, subscription, and hybrid pricing, plus centralized rate cards, commitments, credits, and custom enterprise contracts. Its dimensional pricing supports tailored rates across usage dimensions such as resource type, volume, location, features, performance, and support.
• Audit logs and change history: Metronome supports audit logs and contract edit history with timestamps and user metadata for billing and contract changes.

GenAI use cases

Metronome has been used by AI and data companies including OpenAI and Databricks for usage-based monetization. Its metering and pricing capabilities support token, API, and infrastructure billing patterns common in AI products.

Stripe ecosystem positioning

Following the Stripe acquisition, Metronome is part of Stripe's broader revenue stack. This can fit organizations that want usage metering and pricing within the same vendor ecosystem as Stripe Billing and Stripe payments.

Pricing structure

Metronome's public Startup plan is priced at 0.8% of billing volume plus $0.04 per 1,000 ingested events. Its Custom tier uses tailored pricing.

When to choose Metronome

• Your organization uses Stripe and wants usage billing within the Stripe ecosystem
• You need usage metering, pricing, and analytics for AI or infrastructure products
• Audit logs and contract change history are important requirements

3. Lago

Lago is a billing platform with an open-source edition licensed under AGPL-3.0, plus paid cloud and self-hosted offerings. The platform is relevant to teams prioritizing code transparency, deployment control, and data ownership.

Key capabilities for GenAI apps

• Open-source core: Lago's open-source repository is licensed under AGPL-3.0, enabling self-hosting and modification of the open-source edition.
• Event-based usage metering: Lago ingests usage events and aggregates them through billable metrics for invoicing. Its documentation includes AI and LLM examples with model, input-token, output-token, and other pricing dimensions.
• Configurable billable metrics: Billable metrics can be created through the Dashboard or API, and SQL custom expressions support custom aggregation calculations.
• Payment processor flexibility: Lago supports multiple payment providers, including Stripe, Adyen, and GoCardless, and documents a custom payment-integration path.

GenAI use cases

Lago supports token metering and API call billing for teams that want a managed billing service or a self-hosted deployment. Its usage-ingestion documentation includes AI and LLM examples with per-token pricing that can vary by model and distinguish input from output tokens.

Deployment options

Lago offers an open-source self-hosted path alongside managed cloud and paid self-hosted offerings. This gives teams multiple deployment models for billing infrastructure.

Pricing structure

Lago's open-source edition is free. Lago Premium is available in cloud and self-hosted deployments, while Enterprise offerings use tailored pricing.

When to choose Lago

• Self-hosting or deployment control is a priority
• Your team values an open-source billing core
• Code transparency and payment-provider flexibility are important requirements

4. Stripe Billing

Stripe Billing provides subscription billing and usage-based billing within Stripe's broader revenue and payments ecosystem. Stripe's current Billing pricing page directs usage-based billing scenarios such as multidimensional rates, negotiated contracts, and marketplace transactions to Metronome, a Stripe product. This can fit teams that want billing and payments within one vendor ecosystem.

Key capabilities for GenAI apps

• Meters API: Stripe's usage-based billing documentation describes meters for usage such as API requests, processing time, storage, and LLM token counts.
• Hybrid and dimensional pricing: Stripe's revenue stack supports pricing plans that combine usage-based rates, dimensional pricing, recurring fees, and credits, with Metronome positioned for multidimensional and negotiated usage-based billing workflows.
• Unified invoicing: Stripe Billing can combine recurring subscription fees and variable usage charges on a single invoice.
• Payments and multiprocessor support: Stripe Billing integrates natively with Stripe payments and supports billing workflows that involve off-Stripe payment processors.

GenAI use cases

Within Stripe's current revenue stack, token, API, and other usage can be handled through Stripe's usage billing capabilities, with Metronome positioned for multidimensional and negotiated structures. This can fit teams that want subscription billing, payments, and usage monetization within the Stripe ecosystem.

Pricing structure

Stripe Billing's current U.S. pay-as-you-go pricing is 0.7% of Billing volume. Its annual subscription plans begin at $620 per month for up to $100,000 of monthly Billing volume, with 0.67% pricing for additional Billing volume on the published tiers.

When to choose Stripe Billing

• Your organization already uses Stripe for payment processing or revenue operations
• You want usage structures within the Stripe ecosystem, using Metronome where Stripe positions those use cases
• You want billing and payments consolidated within one vendor ecosystem

5. Chargebee

Chargebee combines subscription lifecycle management with usage-based billing capabilities for hybrid revenue models. Its 2026 usage architecture supports AI, subscription, and consumption monetization in the same platform.

Key capabilities for GenAI apps

• Subscription lifecycle management: Chargebee supports trials, upgrades, downgrades, prorations, and renewals for subscription-based pricing.
• Usage metering architecture: Chargebee supports usage event ingestion for signals such as AI tokens and compute units.
• Metering and data integrity: Chargebee supports SUM, COUNT, and SQL queries for aggregating raw usage events, along with event deduplication.
• Hybrid and pure usage billing: Chargebee supports subscription base fees, usage-based charges, credits, and pure consumption structures.
• Revenue operations: Chargebee supports automated invoicing, tax handling, and dunning workflows for finance teams.
• Customer self-service and usage visibility: Chargebee provides a hosted self-service portal for account, subscription, and billing management. Its Usage APIs can also power customer-facing usage dashboards.

GenAI use cases

Chargebee supports teams monetizing AI features as part of existing subscription products as well as teams launching usage-based or hybrid AI pricing. Its combination of subscription lifecycle tooling and usage billing can fit companies managing both recurring and consumption revenue.

Pricing structure

Chargebee's current Flow plan has two published options: $0 plus 0.80% of monthly invoicing volume, or $99 per month plus 0.65%. Flow includes 100 million usage events per month. Enterprise pricing is tailored, with additional usage capacity available as an add-on.

When to choose Chargebee

• Your business model combines subscriptions with usage-based or hybrid pricing
• You need subscription lifecycle management alongside usage billing
• Your team prioritizes broader revenue operations workflows

6. m3ter

m3ter provides a metering, rating, and billing engine that sits between product usage data and downstream revenue systems. Its scope includes metering, pricing, bill calculation and management, commitments, credits, and usage-data operations, while final invoice issuance and payment collection can be handled by downstream systems. Salesforce completed its acquisition of m3ter on July 1, 2026.

Key capabilities for GenAI apps

• Usage data normalization: m3ter ingests usage through an API and supports cleaning, transforming, and standardizing usage data for metering and billing.
• Configurable metric aggregation: Meters, aggregations, compound aggregations, and segmented aggregations support measurement across usage dimensions.
• Bill calculation and management: m3ter supports pricing configuration, bill calculation and management, commitments, prepayments, balances, and credits.
• Downstream integrations and data exports: m3ter can send bill-computation output to external invoicing systems and export usage and operational data for finance and analytics workflows.
• Usage data APIs and explorer: Usage Data Explorer v2 supports queries over metered usage with filters, aggregations, and grouping by account, time, or dimension, with API endpoints for programmatic access.

GenAI use cases

m3ter supports multi-source usage data common in AI products. When token counts come from multiple models, providers, and deployment regions, the platform can normalize, meter, rate, and calculate bills from that data for downstream revenue workflows.

Architecture considerations

m3ter provides metering, rating, pricing, bill calculation and management, commitments, and credits in a modular architecture. This structure can fit organizations that want specialized usage monetization while retaining existing downstream invoicing or payment systems.

Pricing structure

m3ter does not publish fixed dollar pricing. Its pricing model combines a monthly core platform fee based on scale, including allowances for usage data ingestion and bill calculations, with optional add-ons, enhanced support, and implementation services.

When to choose m3ter

• You want a modular metering, rating, and bill-calculation engine connected to existing revenue systems
• Usage data comes from multiple sources and needs normalization
• Your organization wants to retain downstream invoicing and payment systems

7. Amberflo

Amberflo positions itself as an AI monetization platform that combines billing with cost visibility. The platform emphasizes connecting usage data to revenue and infrastructure costs.

Key capabilities for GenAI apps

• Cost and revenue convergence: Amberflo supports usage, cost, margin, invoice, and revenue recognition workflows in one system.
• Margin visibility: Amberflo supports customer-level cost and margin analysis alongside billing.
• AI pricing models: Amberflo supports usage-based, tiered, volume, hybrid, credit-based, outcome-based, minimum-commit, and multi-dimensional pricing.
• Spend attribution: Amberflo supports cost attribution across customers, teams, agents, and workloads for FinOps visibility.

GenAI use cases

Amberflo targets AI companies where per-customer profitability is evaluated alongside billing. Its model connects usage, billing, and cost data for scenarios involving variable token costs, multiple infrastructure providers, and margin analysis.

Platform positioning

Amberflo differentiates through the combination of metering, billing, and connected cost data. This can fit teams that want margin visibility alongside usage monetization.

Pricing structure

Amberflo combines fixed monthly plans with included usage allowances and variable usage charges. Its current public pricing lists a $99 per month Startups plan and a $599 per month Growth plan, with billing-volume and meter-event overages on Growth and custom pricing for larger deployments.

When to choose Amberflo

• Per-customer profitability analysis is a business requirement
• You need cost visibility alongside billing for margin management
• Your AI infrastructure spans multiple models and vendors

Why Orb stands out for GenAI and LLM billing

For teams building generative AI applications, billing complexity extends beyond simple metering. Token costs vary by model, context length, and provider. Enterprise contracts add commitments, credits, custom terms, and pricing exceptions. Customers need transparent usage and spend information. Finance needs a traceable path from usage through invoices and revenue reporting. Product teams need room to evolve monetization without making every pricing decision dependent on a new engineering project.

Orb addresses these requirements through purpose-built AI billing capabilities that connect billing automation, pricing execution, and revenue operations on top of a usage-native foundation.

Dimensional pricing without configuration sprawl: Orb's dimensional price groups support pricing across multiple usage dimensions, such as region, instance type, and environment, using a single pricing configuration for dimension combinations. For AI companies, those dimensions can also represent model type, workload, or other attributes that influence value or cost.

Raw usage events with query-based billing: Orb's standard metering architecture retains raw usage events and uses a query-based billing architecture to compute billing from usage history. That foundation supports retroactive pricing, backfills, metric changes, and traceability while giving teams a consistent billing data layer.

Low-latency operational visibility: Orb combines its billing architecture with a streaming path for alerts and threshold workflows. Customer-facing live usage experiences can be delivered through the Experience Kit, helping customers understand consumption before the invoice arrives.

Pricing simulation before deployment: The Simulations feature lets teams model price changes against historical usage before affecting production. This gives Product, Finance, and RevOps a way to inspect projected customer and revenue impact before a pricing rollout.

Finance workflows connected to usage: Orb's native NetSuite integration, revenue recognition reporting, and accounting period controls connect usage billing to finance operations. Structured transaction data can reduce manual mapping and reconciliation work while preserving traceability into downstream accounting systems.

Credits and hybrid monetization: Orb supports prepaid credits, commitments, usage-based charges, and hybrid pricing patterns in the same billing foundation. This is especially relevant for AI products that combine platform fees, prepaid balances, minimum commitments, and consumption overages.

Scaled ingestion options: Orb supports granular standard metering and Hosted Rollups for configured streams that benefit from aggregation during ingestion. Orb's metering infrastructure supports higher-volume aggregation capabilities that can ingest billions of events per day.

Customer outcomes reinforce the operational value. Vercel reported an 80% decrease in time required to build and launch billing for new products. Stytch reports a 75% reduction in time spent processing bills and invoicing. Orb also reports that Replit has seen 40x revenue growth since using Orb to monetize usage.

For teams evaluating billing infrastructure for GenAI applications, Orb provides a particularly strong combination of pricing flexibility, traceability to raw usage events, correction and backfill workflows, customer usage visibility, simulations, and finance integration. That breadth is why Orb ranks first in this comparison while the other platforms remain relevant for specific deployment, ecosystem, subscription, metering, or cost-analysis priorities.

Frequently asked questions

What makes billing for GenAI and LLM apps different from traditional SaaS billing?

Many GenAI infrastructure and API products incur costs that scale with consumption, making tokens, requests, compute time, or outcomes useful billing metrics. Other AI applications use subscriptions, seats, capability tiers, outcomes, or hybrid combinations. Stripe's 2026 AI pricing framework identifies six core models and notes that the market has not settled on one dominant approach. Compared with fixed-fee billing, AI monetization can place greater demands on usage ingestion, measurement, pricing flexibility, credits, customer spend visibility, corrections, and cross-functional coordination.

How can usage-based billing software help prevent revenue leakage for AI services?

Revenue leakage can occur when usage goes unbilled because of event loss, duplicates, late data, aggregation errors, contract mismatches, or pricing configuration mistakes. Reliable billing infrastructure combines idempotency, deduplication, validation, late-event handling, observability, auditable data lineage, and usage or spend alerts so anomalies can be identified and corrected earlier. In Orb, SQL-defined metrics and query-based billing help teams express billing logic precisely against retained usage history on the standard ingestion path.

What are the most critical features for high-volume AI usage billing?

High-volume AI applications benefit from metering infrastructure that can sustain expected event volumes while protecting data integrity through idempotency or deduplication, validation, transformation, late-event handling, and observability. Timely customer-facing usage and spend visibility supports alerts and cost controls. Dimensional pricing can be useful when economics vary across models, regions, or workloads, while credit and commitment support covers common prepaid and hybrid monetization models. At extreme scale, architectural choices around retention and aggregation of raw usage events affect correction workflows, pricing flexibility, and analytics.

Can usage-based billing systems integrate with accounting software like NetSuite?

Yes, though integration depth varies by platform. Orb's NetSuite integration creates native transaction records including invoices, credit memos, customer deposits, and sales-order workflows. This sends structured transaction data that finance teams can reconcile and trace in NetSuite, reducing manual mapping and reconciliation work.

How do companies support financial compliance and audit readiness with complex AI usage data?

Audit readiness for usage-based revenue benefits from traceable data lineage from usage through pricing, invoice generation, corrections, and accounting outputs. Retention of raw usage events can strengthen retroactive verification where the platform architecture preserves those events, while audit logs, contract history, invoice-state controls, and accounting-period locks provide additional control evidence. AICPA describes SOC 1 and SOC 2 as examination and reporting services, so they are more accurately described as reports than product certifications. Revenue recognition software and billing reports can support processes aligned with ASC 606, but appropriate treatment still depends on contract terms and accounting judgments.