Use Cases

Pricing for AI agent platforms — products that perform multi-step autonomous tasks on the user's behalf.

Pricing for AI model inference services — APIs and platforms that run trained models on user inputs, typically billed per token, per request, or per GPU-hour.

Pricing for AI products whose core job is analytics — querying, evaluating, and reporting on data, models, or market signals.

Pricing for AI marketing products — content generation, ad creative, outbound campaigns, and sales-marketing automation.

Pricing for data collection, scraping, and pipeline services — platforms that extract, transform, and deliver web data, typically billed per request, per GB, or per record.

Pricing for AI products that automate customer service — chatbots, ticket triage, and autonomous resolution agents.

Pricing for AI-native developer tools — code editors, completion engines, and agent platforms that write or modify code.

Pricing for AI services whose primary output is generated source code, typically measured in tokens, requests, or completed tasks.

Pricing for products that monitor AI systems and software — LLM observability, evaluation in production, and security monitoring.

Pricing for platforms that host web applications, typically billed across multiple dimensions — bandwidth, requests, compute, and storage.

Pricing for AI-enabled billing and payment infrastructure platforms that help software companies meter usage, generate invoices, and collect revenue.

Pricing for usage-billing and metering platforms — the vendors that meter, rate, and invoice usage for other companies.

Pricing for CRM, sales-engagement, and revenue-intelligence platforms — the incumbents adding AI to the sales motion, typically a per-seat license with a separate AI credit meter on top.

Pricing for serverless function platforms, billed per invocation plus compute time consumed.

Pricing for AI products that generate UI components or full pages from prompts — typically billed per credit or generation.

Pricing for platforms that track, analyze, and optimize AI API spending — the observability layer for AI infrastructure costs.