Guide · Tooling

AI Cost Tracking Tools Compared: Pick by the Question You Need Answered

By Ghiles Asmani, founder of Weckr · Published August 7, 2026

Short version:the tools in this space answer different questions. Helicone and Langfuse answer “what are my LLM calls doing?” (observability and debugging). LiteLLM answers “how do I route and budget traffic across providers?” (a gateway). Weckr answers “which customers cost more than they pay me?” (per user margin and caps). Pick by question, and know that running an observability tool next to a margin tool is normal, not redundant.

Full disclosure: we build Weckr, so read the framing critically. To keep this useful we compare by capability rather than by feature checkbox, we say plainly where the other tools are the better choice, and we avoid quoting competitors’ prices, which change faster than articles.

The four, by the question they answer

Helicone: observe every call

Open source LLM observability. Requests, latencies, errors, costs, caching, sessions, available as a proxy or async logging. If your question is “what is happening in my LLM traffic and why did this call cost/fail/slow down”, this is the mature answer and the fastest to adopt. Cost is tracked per request and aggregable by custom properties, including a user id you attach. What it does not hold is what each user pays you, so margin stays a spreadsheet exercise.

Langfuse: engineer the LLM app

Open source LLM engineering platform: traces, evals, prompt management, datasets, cost usage per trace. The right tool when prompts are a product surface you iterate on seriously, with A/B tests and regression evals. Cost is a dimension of engineering quality here, not a business ledger. Deepest of the four, and the most to learn.

LiteLLM: gateway with budgets

Open source proxy speaking a unified API across many providers, with routing, fallbacks, and budgets or rate limits per key and team. Built for platform teams governing internal AI usage: give each team a key and a budget, route everything through one place. Sits in the request path by design. Budgets are per key or team, which is the internal governance grain, not the end customer grain. Their public model price dataset is also the reference our own LLM pricing JSON feed is watched against.

Weckr: margin per customer

Two lines around your existing client. Every call is logged with your user id, feature, and the user’s plan price, cost is recomputed server side from tokens, and the dashboard answers which accounts are unprofitable, per user, per feature. Per plan spending caps block or downgrade a user before the call. By design it never stores prompts or outputs, which means it cannot be your debugging tool, and is not trying to be. The method behind it is in tracking AI costs per user and calculating margin per user.

How to choose in practice

  • Debugging quality, latency, or weird bills per call: Helicone, or Langfuse if you also want evals and prompt management.
  • Platform team governing many internal consumers: LiteLLM at the gateway, budgets per team.
  • SaaS founder pricing plans against variable AI cost: Weckr, because the unprofitable customer question needs revenue joined to cost, which observability tools do not model.
  • Serious LLM product at scale: plausibly one from each column. They overlap less than their landing pages suggest.

Two structural things to check whatever you pick. Request path or not: a proxy in the hot path adds capability and a dependency; async logging adds neither latency nor risk (Weckr fails open on purpose, an outage of ours cannot block your calls). And price freshness: any tool computing cost from tokens is only as truthful as its price table, ask how it updates, since a July price cut silently broke every stale table including, for six days, ours.

FAQ

What is the best tool for tracking AI API costs?

Depends on the question you need answered. For debugging and observability across LLM calls, Helicone and Langfuse are the mature choices. For gateway level routing with budgets per team or key, LiteLLM. For the business question, which customers cost more than they pay you, Weckr, since it tracks cost against each user’s plan price. Many teams run an observability tool and a margin tool side by side.

How is Weckr different from Helicone or Langfuse?

Observability tools answer engineering questions: traces, latencies, errors, prompt versions, evals. Weckr answers a business question: per user margin. It stores what each user pays alongside what each user costs, flags accounts that are underwater, and enforces per user spending caps that block or downgrade before the call. It deliberately does not store prompts or outputs at all, so it is not a debugging tool.

Do these tools sit in the request path?

Some can. Proxy style setups route your LLM traffic through the tool, which enables features like caching but adds a dependency in the hot path. SDK wrapper setups log asynchronously after the call, adding no latency and failing open if the tool is down. Helicone offers both patterns, LiteLLM is a proxy by design, Langfuse and Weckr log out of band.

Are these tools open source?

Helicone, Langfuse, and LiteLLM are open source with hosted offerings. Weckr’s SDKs and Claude Skills are MIT licensed and public, with a hosted backend, and its free tier covers 50,000 requests a month. Check each project’s current license and pricing pages before deciding, since tiers change faster than articles.

Can I just build cost tracking myself?

A basic version, yes: log tokens per call with your user id, multiply by a price table, aggregate. The maintenance is the trap: prices change monthly (one model dropped 80 percent in July 2026), models launch constantly, caching complicates the math, and the per user margin view needs your billing data joined in. The build is a weekend; keeping it truthful is a part time job.

Keep reading

Track AI Costs Per User

Why the total bill lies, and how to fix it.

LLM Pricing as JSON

A free live feed of model prices, kept accurate weekly.

Calculate Gross Margin Per User

Plan revenue minus AI cost, measured per user.

If your question is the margin one

You can evaluate Weckr’s answer to it without signing up: the live demo shows the per user margin view on seeded data, the free tier covers 50,000 requests a month, and the wider playbook is the AI cost and margin guide.

See the dashboard with real data, no signup needed.

Try the demo →