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TokenDam

FreemiumSelf-Hosted

You're watching your LLM bill climb, but your provider dashboard tells you nothing about which agent step burned it or why — just a monthly token total with nowhere to point the blame. TokenDam reads your captured traces and gives you a per-step cost breakdown, a percentage of tokens that did no real work, and 12 named waste patterns, each with a dollar figure attached.

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Summary

The tool runs entirely in the browser — nothing you paste leaves your machine — which means no SDK swap, no gateway, no routing prod traffic through a third party. Drop a trace file (OpenAI, Anthropic, LangSmith, Langfuse, Vercel AI SDK, or raw logs), and the report identifies duplicate context, low-value prompt content, and caching misses in roughly the time it takes to read this sentence. The CI integration is the sharper edge: `tokendam --ci` fails the build when token cost regresses, turning a vague cost concern into a hard pipeline gate. The tool does not monitor live traffic — it audits what you've already captured. Teams that need runtime dashboards or routing failover will reach for a gateway alongside it.

Bottom line: TokenDam earns its place in any CI pipeline where token-cost regressions need a pass/fail gate — but the moment you need live traffic visibility or runtime caching, you're adding a gateway anyway and the tool becomes an audit layer, not a control plane.

Find your fit

Before you choose TokenDam

More options in LLM Observability. Compare pricing, platforms, and features side by side.

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Pros

  • Client-side tokenizer execution means your prompt content never leaves the browser, so you can run real production traces through the analyzer without a security review or a data-processing agreement.
  • Exact tokenizer-based cost measurement — token counts × current prices — rather than an LLM's opinion, which means the number is reproducible and can serve as a CI pass/fail threshold that won't give a different answer on Tuesday.
  • Per-agent, per-step cost breakdown surfaces which specific node in your pipeline is burning the budget, so you avoid the common outcome of optimizing the wrong prompt while the real waste sits in a retrieval step.
  • Twelve named waste patterns with dollar figures and paste-ready fixes, so an engineer who has never read a tokenizer spec can find and remediate duplicate context or low-value content in a single sitting.
  • Zero routing integration required — no SDK swap, no gateway, no prod traffic redirection — which means adoption is a trace capture and a paste, not a sprint.

Cons

  • The tool only analyzes traces you've already captured; it has no visibility into live traffic. Teams that need a real-time cost dashboard or want to catch a runaway agent mid-run will find nothing here — they're adding a gateway regardless, at which point TokenDam becomes a periodic audit step rather than a primary cost control.
  • Token count accuracy is stated as exact for OpenAI (o200k tokenizer) and estimated for non-OpenAI models. Teams running Anthropic or open-weight models in production and relying on the CI budget gate for billing precision need to validate those estimates against actual invoices before treating a pipeline failure as authoritative.
  • There is no API and no self-serve programmatic output format described beyond the CI pass/fail result, so teams that want to feed cost-per-step data into an internal analytics platform or a Slack alert will have no structured export path — they're copying numbers from a report manually or waiting on a paid-tier feature that may not exist yet.

About

Platforms
Browser, CLI (npm)
API Available
No
Self-Hosted
Yes
Last Updated
August 16, 2026

Best For

Who it's for

  • Teams tracking LLM spend without routing traffic through proxies
  • Engineers needing exact tokenizer-based cost measurements
  • Projects requiring CI gates on token-cost regressions

What it does well

  • Audit LLM agent traces for hidden token waste
  • Generate per-agent cost breakdowns from captured calls
  • Enforce token-spend budgets in CI pipelines
  • Identify and remediate duplicate, cached, or low-value prompt content

Integrations

OpenAIAnthropicLangSmithLangfuseVercel AI SDKraw logs

Frequently Asked Questions

Is TokenDam free?
TokenDam offers a free option alongside paid upgrades. Check the current allowances and terms before choosing a plan.
Can I self-host TokenDam?
Yes. TokenDam supports self-hosting on your own infrastructure.
What platforms does TokenDam support?
TokenDam is available on: Browser, CLI (npm).

Curated lists that include this category

LLM bills climb with no clear culprit

Your provider dashboard shows only a monthly token total. TokenDam reads captured traces from OpenAI, Anthropic, LangSmith, Langfuse, Vercel AI SDK, or raw logs and returns a per-step cost breakdown plus the share of tokens that produced no useful output.

How it works

The entire analysis runs in the browser so pasted traces never leave the machine. It flags duplicate context, low-value prompt content, and caching misses, then attaches a dollar figure to each of 12 named waste patterns. A CLI command, tokendam –ci, fails the build when token spend regresses.

Limitations

The tool audits traces already captured and has no view into live traffic. Token counts are exact for OpenAI models and estimated for others.

Who it is for / who should skip it

Teams that want to track spend without routing traffic through proxies, engineers who need reproducible tokenizer-based measurements for CI gates, and projects that must enforce token budgets in pipelines will find it useful. Teams that require real-time dashboards or precise billing data for non-OpenAI models should skip it.

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