What is it
AI cost tracking pricing is pricing for platforms that track, analyze, and optimize AI API spending — the observability layer for AI infrastructure costs.
As LLM API costs grew from negligible to line-item-significant in engineering budgets, cost visibility became a product category in its own right. Helicone and Portkey both offer cost tracking as a core capability: they proxy LLM API traffic, capture every token count and model pricing signal, and surface that data in dashboards that attribute costs to users, features, teams, or individual prompts. This is the FinOps layer for LLM-heavy applications — the same discipline covered in our introduction to usage-based pricing guide, applied to your own AI bill.
The pricing irony is notable: platforms that help customers reduce AI spend are themselves AI infrastructure products metered by the same kind of usage they help manage. Helicone charges by log volume — the number of LLM requests proxied — while Portkey meters on recorded logs (one log per gateway request). In both cases cost tracking is a feature within the observability plan, not a separate meter, so the cost of tracking your AI costs scales with your AI usage — a structure that naturally aligns vendor and customer interests.
Cost tracking is closely related to LLM observability (see LLM Observability Pricing): observability captures the full request/response context, and cost tracking is the financial analytics layer built on top of those logs. Both Helicone and Portkey list observability and cost-tracking as core use cases, effectively bundling the two.
How it works
AI cost tracking works by intercepting LLM API traffic at the proxy layer. Each request is logged with its token counts; the platform applies the provider’s pricing schedule to estimate the per-call cost and aggregates that across dimensions — user ID, session, prompt template, model, or team tag. The meter that drives the bill is the log (request) volume, not the cost analytics itself.
| Company | Cost tracking mechanism | Free tier | Paid entry point |
|---|---|---|---|
| Helicone | Proxy-based; every request captures token counts + cost estimate, attributed by user, session, prompt template, model, or custom property | Hobby — free, 10,000 requests/mo + 1 GB storage | Pro $79/mo (10K req + 1 GB included, then usage-based overage); Team $799/mo |
| Portkey | Proxy/gateway-based; real-time cost attribution on recorded logs, with semantic caching and budget/rate limits | Developer — free, 10,000 recorded logs/mo | Production $49/mo (100K logs included, then $9 per additional 100K requests up to 3M); Enterprise custom |
The cost tracking workflow is consistent across both platforms:
- Developer routes LLM API calls through the proxy or gateway (a simple base-URL change).
- The proxy logs token counts and applies provider pricing schedules to estimate cost.
- The platform aggregates cost by attribution dimension (team, feature, user, model).
- Dashboards surface cost per prompt, cost per user, and spend trend over time.
- Budget alerts and rate limits fire when spend crosses configured thresholds.
Worked example on Portkey’s published rates: a team on the $49/month Production plan that processes 300,000 gateway requests consumes its 100,000 included logs, then pays overage on the remaining 200,000 requests at $9 per 100K — an extra $18 — for a $67/month total. The same predictable “flat floor + usage overage” shape appears on Helicone, where $79 Pro includes 10K requests and 1 GB before usage-based overage on logs and storage begins. Choosing which unit to meter on is exactly the trade-off covered in our guide to choosing the right usage metric.
Companies using this
Two companies in the corpus list AI cost tracking as a core use case: Helicone, with a proxy-based cost attribution model, an open-source (Apache 2.0) self-host path, and a generous free Hobby tier, and Portkey, an MIT-licensed AI gateway that layers cost attribution, semantic caching, and budget controls on top of its observability. Both price the cost tracking capability within their log-volume plans, keeping AI spend monitoring free or low-cost for workloads that stay within the free-tier limits.
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FAQ
What does AI cost tracking software do and how is it priced?
AI cost tracking platforms sit as a proxy in front of LLM APIs to capture every request, attribute the token cost to teams or features, and surface budget alerts and spend analytics. Helicone and Portkey both offer cost tracking as a core capability, and both meter their own pricing on log (request) volume rather than charging separately for the cost analytics. Helicone's paid tiers are $79/mo (Pro) and $799/mo (Team), each including 10,000 requests plus usage-based overage; Portkey's Production tier is $49/mo with 100,000 recorded logs included, then $9 per additional 100K requests.
How much does an AI cost tracking tool cost per month?
Both leading cost-tracking platforms have a free tier and a low-cost paid entry point. Helicone's Hobby plan is free (10,000 requests/month, 1 GB storage), Pro is $79/month and Team is $799/month, each with usage-based overage on logs and storage above the included allotment. Portkey's Developer plan is free (10,000 recorded logs/month), and its Production plan is $49/month with 100,000 logs included plus $9 per additional 100K requests up to 3M — capping the self-serve bill around $310/month before Enterprise.
Do AI cost tracking tools charge extra for the cost analytics feature?
No. On both Helicone and Portkey the cost attribution, per-prompt cost estimates, and budget dashboards are bundled into the log-volume plan rather than sold as a separate meter. You pay for the observability infrastructure — the number of LLM calls logged — and cost tracking is included, so the price of tracking your AI spend scales with your AI usage itself.
How do AI cost tracking tools help reduce LLM API spend?
They make invisible costs visible: which prompts are most expensive, which features over-consume, and where caching or model-switching would have the highest ROI. Helicone surfaces per-prompt cost estimates and attributes them by user, session, or custom property; Portkey adds active spend controls like semantic caching and budget/rate limits on top of the same visibility. Because the platform fee is small relative to LLM API spend, it is typically recouped in the first month of optimization work.
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