AI Usage & Cost Tracker
Record tokens, latency and spend for every model call, broken out by project, user and team, catch billing anomalies early and cap them with budgets — Helicone, Langfuse and LiteLLM class AI cost observability is the ledger you need to control AI spend in 2026
Tool Interface
Interactive tool will be available soon
Features
- ✓ Drops in with a one-line config change and logs tokens, model, latency and dollar cost per request
- ✓ Splits spend by key, user, team, feature and customer, so you can answer who is spending the money
- ✓ Supports budgets and threshold alerts, with automatic cut-off or downgrade to a cheaper model when exceeded
- ✓ Records cost step by step inside multi-step agent runs, showing which step burns the most rather than just the total
- ✓ Self-hosted open-source options keep cost and prompt data on your own network for compliance and privacy
How to Use
- Quantify first: pull the last three months of vendor bills, look at total spend and its growth curve, and identify your three biggest cost sources
- Choose an approach: a one-line proxy (e.g. Helicone) for the smallest change, Langfuse for tracing plus evaluation, LiteLLM if you already unify many providers
- Tag dimensions (team, customer, feature) and set budget thresholds with a policy for overruns — alert, cut off or downgrade
- Review unit economics weekly (cost per thousand calls, per active user) and keep optimising cache hit rates and model choice
FAQ
What are AI usage and cost tracking tools?
Platforms that make LLM spend observable. They log which model each request used, how many tokens it consumed, how long it took and what it cost, then break it down by key, user, team, feature or customer, with budgets and alerts so you know before you overspend. Notable options include Helicone, Langfuse, LiteLLM, Portkey, Braintrust and Datadog LLM Observability, plus finance-side platforms like Amnic and Finout.
Why aren't vendor bills enough?
Because a vendor bill only shows a monthly total and cannot answer business questions: is this month's 30% rise healthy growth from new customers, or one feature hammering a flagship model? Which user drove the cost up? Is cost per unit improving or deteriorating? Only request-level detail with dimension tags ties cost to business metrics and lets you act before you blow the budget.
Is setup complex? How much code changes?
Most options are light to integrate. Proxy-based tools usually just need a base URL change and a header — one line of config and logging starts, which is exactly Helicone's pitch, giving per-request cost detail within hours. SDK-based approaches require a library but capture finer-grained agent steps. If you already route through a gateway like LiteLLM, cost tracking is often a by-product of routing with almost no extra work.
What do they typically cost?
Most have a free tier, then meter usage or seats. On published figures: Datadog LLM Observability's free tier includes 40,000 LLM spans per month, with Pro from $160 per month for 100,000 LLM spans — and only LLM spans are billed, not tool or retrieval spans; Helicone's free tier covers 10,000 requests per month with paid plans from $79; Braintrust's free tier includes 1M spans per month with Pro at $249. Langfuse and LiteLLM are open source and self-hostable, costing only infrastructure.
Gateway or tracing — which do I pick?
They are two different jobs and often used together. Gateway tools (Helicone, LiteLLM, Portkey) sit between your app and providers, so they inherently see token and price data per call and excel at multi-provider cost and latency overviews with one line of change. Tracing tools (Langfuse, Braintrust, Datadog) go deeper into each reasoning step of agents and RAG, pinpointing which step is slow or expensive while also supporting evaluations. If the organisation needs to charge AI spend back to departments and customers, add a finance-side FinOps platform on top.
Which teams need this most?
Teams whose AI spend is large enough to hit a budget line: platform teams splitting AI cost across product lines, SaaS that bills customers by usage, engineering teams with complex agent chains and unclear cost structure, and companies that must explain the relationship between cost and usage to finance. Once monthly model bills pass a few thousand dollars, or grow significantly faster than revenue, it is time to adopt one.