Top 6 billing software for AI companies

So you’re ready to roll out new pricing for your AI products? Whether we’re talking prepaid credits, tiered usage, volume discounts, or hybrid models, your billing platform needs to have the capabilities to support your pricing strategy.

As we’re leaving traditional per-seat models behind and opt for usage-based or outcome-based pricing, the best billing solutions for AI companies can track custom metrics (API calls, tokens, etc.), adapt pricing tiers or thresholds, and automate revenue recognition.

We’re evaluating the top billing software for AI companies to see which platforms are up for the challenge and which ones fall short. Solutions like Alguna, Stripe Billing, Orb, Togai, Paid, and Zenskar all offer different approaches compared to legacy players like Chargebee and Maxio.

In this article, we’ll explore why AI monetization is unique, what it means for billing software for AI companies, and how each platform measures up.

Why monetizing AI is different

AI workloads have variable costs and complex usage patterns that break traditional billing models. Unlike fixed SaaS subscriptions, AI compute costs fluctuate with usage spikes and task complexity. Prompts, computations, and API calls all come at a cost, so if you don’t set your billing model right, you might end up with unexpected expenses.

The problem is that budgets aren’t built for this volatility, but the good news is that the top billing software for AI platforms are.

What we need to keep in mind is that customers expect to pay proportionally to value, which is why we’re seeing a shift from per-seat to outcome- or usage-driven pricing. Billing approaches now measure units like tokens, generated outputs, or inference cycles. For example, some AI companies bill per conversation or per successful outcome, while others meter token usage or GPU-minutes.

This complexity means AI companies often implement tiered pricing (volume discounts) or threshold billing to manage large-scale usage.

Key differences for AI monetization include:

• High compute and data costs: GPU/TPU time, token processing and data storage drive variable expenses.
• Usage variability: Customer usage can spike unpredictably; billing models must adapt in real time.
• Multiple metering units: Beyond seats, AI uses metrics like tokens, API calls, feature interactions, or model inferences.
• Outcome/value alignment: Clients expect pricing tied to results (conversations, resolutions) rather than features.

These factors make AI billing complex. The right platform must flexibly capture AI usage metrics and automate the complex pricing and billing that follow to make sure your customers aren’t in for a surprise.

What to look for in a billing solution for AI companies

When evaluating billing solutions key features include:

• Real-time AI usage tracking: The system should capture AI-specific metrics (tokens, API calls, inference events, credit usage) on-the-fly. AI billing tools “track usage of your product in real time,” ensuring accurate, up-to-date metering.
• Flexible pricing models: Support for pay-as-you-go, tiered, thresholds, and hybrid plans is essential. For AI, that means handling prepaid credits, volume tiers, burst pricing and hybrid models (base subscription + usage add-ons).
• Unified quoting-to-cash workflows: Ideally, quoting, contracts, invoicing and payments are unified. This avoids manual handoffs and errors. Look for platforms that embed AI billing in your finance workflow to “eliminate manual reconciliation” and create clear, itemized invoices.
• Self-service and approvals: Non-technical teams should configure complex deals without engineering help. Good solutions provide no-code dashboards or builders for CPQ (configure-price-quote) and approvals.
• Developer-friendly APIs: A robust API (with SDKs or webhooks) ensures engineering teams can integrate usage data and pricing logic smoothly.
• Scalability and reliability: The chosen system must handle high volumes of usage events with low latency (some can process tens of thousands of events/sec).
• Reporting and forecasting: Built-in analytics (usage trends, revenue forecasting) and sandbox testing allow rapid iteration on pricing without risk.

In short, a billing solution for AI companies needs to combine flexible billing, robust developer tools, and solid integrations with the rest of your financial tech stack.

Product overview

Below, we’ve put together an overview of the top billing software for AI platforms, including the pricing models they support, use cases, along with their (sometimes elusive) pricing.

Alguna - Best overall billing solution for AI companies

Usage metering analytics in Alguna.

Alguna is an end-to-end revenue management platform built specifically for AI companies. It unifies CPQ, quoting, invoicing, and billing in one system. Non-technical teams can define any pricing model (subscriptions, usage, one-time fees, credit-based metering) via a no-code visual interface. 

Once a contract is approved (embedded e-signatures), Alguna auto-generates invoices with the negotiated terms and syncs everything to your ERP.

Best use case: High-growth SaaS and AI companies that want to experiment with pricing, roll out new plans fast, and ensure accurate billing for their customers while aligning internal teams.

Pros:

• All-in-one no-code billing solution for AI platforms (no stitching together multiple tools)
  • Support self-serve plans and CPQ customer enterprise flows
• Unlimited usage models (per use, per call, credit-based, per feature)
• Flexible pricing builder (create any plan with pay-per-unit, tiers, volume discounts, or flat rates)
• Strong workflow automation (approval chains, e-signatures, auto-issue invoices)
• Integrates with the rest of your tech stack (CRMs, QuickBooks, NetSuite, etc.)
• Integrated payments out of box and without additional engineering integration
• Predictable flat pricing (no % cut of revenue)

Cons:

• Newer player (backed by Y Combinator)
• Fixed monthly fee may be higher upfront than basic alternatives

Key features:

• No-code product catalog and quote builder
• Real-time usage metering and reporting
• Multi-entity and multi-currency invoicing
• Flexible dunning/payment flows (AlgunaPay or integrated processors such as Stripe)
• Dedicated customer portal and reporting dashboards built-in

Pricing: Flat subscription model. Starts at $399/month for full platform access. This includes implementation and white glove onboarding.

Stripe Billing - Best for AI startups that have traditional seat based plans

Stripe Billing is a mature, developer-first billing system that supports subscriptions, one-time invoices, and usage-based billing. Its huge advantage is global scale and reliability: Stripe’s payments platform processes over $1.4 trillion/year and has 99.999% uptime.

Best use case: Stripe is ideal for AI startups with plenty of engineering resources that need to get up and running fast.

Pros:

• Get set up and start billing for simple and traditional plans with some metered component
• Robust usage-based billing (meter per API call or token, custom usage units)
• Developer-first APIs and extensive documentation
• Built-in features like automatic invoicing, card updates, retry logic and basic dunning
• Seamless global payments and compliance (tax and fraud tools available)

Cons:

• Quoting and custom contracts are limited out-of-the-box
• Advanced CPQ or enterprise workflows typically require engineering or add-ons
• Stripe’s fees are transaction-based (≈0.5–0.8% per invoice volume), so costs rise with revenue
• Complex usage models may need significant setup

Key features:

• Flexible metered billing with usage metering (tokens, API calls, inference cycles)
• Powerful integrations 
• Hosted customer portal for self-service

Pricing: Standard Stripe rates apply (percentage of transaction value plus credit card fees). Billing charges ~0.5–0.8% of invoiced volume. There’s no flat platform fee, so early-stage companies pay very little until revenue grows.

Togai - Best combined with Zuora

Togai  is a high-scale usage metering and rating engine. It’s designed to handle complex, event-driven billing (raw events can be ingested, metered, and rated in real time).

Togai’s “low-code builder” allows configuration of intricate usage-based models, letting developers and finance teams collaborate on pricing. The platform is optimized for developer-finance collaboration allowing developers to define metrics and ingest data, followed by finance approving pricing via the UI.

Best use case: Large enterprises that are already using Zuora and have custom consumption requirements.

Pros:

• Extremely scalable (handles up to 1 billion+ events/day)
• Flexible rating (tiered, inclusive allowances, overage fees)
• Real-time usage ingestion and a revenue simulator for forecasting
• Part of Zuora, so can integrate with full order-to-cash workflows (subscriptions, payments, RevRec)

Cons:

• Togai itself isn’t a full billing platform, it’s just the metering/rating component
• Companies need Zuora Billing (or another invoicing system) to complete their billing workflows
• It’s an enterprise tool, so it may be overkill (and expensive) for smaller outfits.
• Togai’s acquisition is very recent (closed May 2024), so products may still be in transition

Key features: 

• Low-code pricing model builder
• Import events from any source
• CRM/ERP connectors and revenue simulations
• Auditable usage trail to reduce disputes

Pricing: Enterprise licensing (Zuora usually charges per revenue or seat). Contact Zuora for Togai access.

Orb - Best for developer-first usage based PLG plans

Orb is a usage-based billing platform built specifically for modern software companies. Similar to Alguna, it allows companies to bill on any given metric, including seats, API calls, tokens or composite metrics. Orb’s strength is in letting engineering teams define custom billing metrics and in a real-time UI so teams can experiment with pricing strategies. 

Best use case: Orb’s design makes it great for AI builders to set up self-serve custom plans.

Pros:

• Fast setup – Orb provides a sandbox to A/B test pricing without code; supports mixed models (subscriptions + metered) and sophisticated features like threshold billing (automatically limits usage to prevent fraud)
• Prepaid credits ledger for stable forecasting and real-time revenue reporting
• Orb also detaches usage from pricing rules, giving teams freedom to try new models without data loss

Cons:

• Support only fixed plans with minimum flexibility for customizations
• Relatively new company (founded 2021)
• Narrower feature set outside billing
• Some companies may find they still need separate tools for quoting or accounting sync
• Pricing is tiered (per usage), so costs scale with volume of events.

Key features:

• SQL-based metric definitions (custom usage tracking)
• Seamless invoicing and reporting (Orb ties usage to AR and SaaS metrics).
• Fraud prevention with threshold billing

Pricing: Orb’s website does not list pricing. It likely uses usage tiers (e.g. per million events). Interested customers request a demo.

Paid - Focused on outcome-based models

Paid is purpose-built for agentic AI products and is currently in private beta. As a new billing solution for AI companies, it focuses on capturing agent usage and billing. The platform also invoices customers and also tracks your margins, i.e. how much it costs you to run each agent and workflow, helping set profitable pricing.

Best use case: Startups building AI agent platforms or marketplaces (e.g. automated chatbots, scheduling agents, generative AI services) where the product is an “agent” performing tasks.

Pros:

• All-in-one agent monetization stack (pricing plans, invoicing, margin tracking, and executive ROI reporting are integrated).
• Easy to get started (embed their SDK in minutes)
• Supports charging per conversation, per agent-seat, or per successful outcome
• The platform automatically generates customer-friendly ROI dashboards to ease renewals

Cons:

• Still in early access/beta phase
• The feature set is narrowly focused on AI agents; it may not suit non-agent AI platforms products
• Requires significant domain knowledge and understanding of what you want to achieve and doesn’t have abstraction like Alguna or Orb so your engineering require to track a lot more information
• Details on pricing and contracts are scarce (you join an invite list). Customization options beyond agents are unclear

Key features:

• Mixed pricing engine (seat, activity, outcome, or custom signals)
• Margin management (track costs per action/agent to set minimum pricing)
• Executive reporting and ROI analytics for customers to drive renewals.

Pricing: Not publicly disclosed – Paid is in private beta. Presumably pricing is based on feature set and usage.

Zenskar - Best for enterprise teams with engineering resources

Zenskar is a flexible SaaS billing and finance automation platform, with strong emphasis on usage-based pricing. It appeals to API companies and SaaS teams that need to centralize subscriptions, metered billing, and accounting.

The platform allows complex pricing (pay-per-use, tiered, committed-use discounts, burst pricing) via a no-code builder or API.

Best use case: SaaS or API-first businesses with rigorous finance processes.

Pros:

• Supports multiple pricing scenarios including subscriptions, usage-based, tiered discounts, and free trials
• Hundreds of integrations and APIs mean you can connect it to any usage source or accounting system
• Built-in forecasting and analytics give finance teams visibility (real-time usage data with historical trends)
• Zenskar handles ASC 606/IFRS15 compliance: it can track prepaid credits and defer recognition until usage

Cons:

• As a comprehensive finance platform, Zenskar can be complex to implement (FAQ suggests ~2–3 months integration)
• It may be more than needed for very early-stage companies
• Pricing is likely on the higher end (enterprise SaaS pricing).

Key features:

• Visual pricing plan builder (create pay-per-call, tiered, volume-discount plans without code)
• Real-time usage dashboards and alerts;
• Automated revenue recognition for any model

Pricing: Custom. Cost will range between $15-24k annually.

Choosing your next billing platform

AI-first companies face unique billing challenges due to fluctuating compute costs and multifaceted usage. That’s why the best billing solutions for AI companies need to be highly flexible. That said, your billing platform should also serve as the source of truth for your revenue data.

Solutions like Orb and Zenskar specialize in usage-based models, Stripe Billing offers rock-solid payment infrastructure, and Togai (by Zuora) provides an enterprise-grade metering engine. But if you want juggling multiple tools to be a thing of the past, Alguna offers an all-in-one billing solution for AI companies that handles pricing, quoting, and billing in a unified workflow.

Remember, billing readiness determines your ability to scale, so make sure to choose a solution that can support your growth plans for the long term. Plus, AI monetization is still evolving, making it crucial to opt for a billing solution that’s innovating as the market changes.