The 8 Best LLM Cost Tracking Tools in 2026

The best LLM cost tracking tools in 2026, ranked

The single most useful shift in LLM cost tracking is deciding what you are accounting for. The unit of AI cost is no longer the API key — it is the agent. A key tells you that "the marketing workspace spent money"; an agent-level view tells you that "the prospecting agent spent it, on Sonnet, retrying a tool call 300 times." Every tool below helps you see spend, but they split into two families: developer observability tools that trace and debug spend at the request level, and org-level meters that attribute spend to the agents, teams, and budgets a business actually manages. We rank Fleece AI Brain first for the second job — company-wide per-agent cost tracking and budgets — while being clear that engineering teams debugging traces will often want one of the observability tools alongside it. Many companies run both, and that is the right answer more often than not.

We built our own agents on this stack, so this is not a spec-sheet roundup. It is the shortlist we would hand a founder or a platform lead who has just realized their AI bill is real money and nobody can say which workflow is eating it. Here is the field, ranked by how well each answers the question a business actually asks: which of our agents cost what, and are any of them out of control?

How we ranked them

We scored each tool on three axes that determine whether it can answer that question. First, granularity: does it stop at the API key and model, or can it attribute spend down to an individual agent, workflow, or run? Second, deployment model: is it a gateway you route traffic through, an SDK you instrument code with, a cloud you ship logs to, or a local layer that reads your own systems? Third, who it is for: an engineer debugging a slow chain has different needs from a leader who wants one budget view across every agent the company runs.

A tool being lower on this list is not a criticism — several are best-in-class at the developer job and simply were not built to be the company's cost map. Match the tool to the seat asking the question. The comparison table near the end lays all of this out at a glance.

1. Fleece AI Brain — org-level per-agent cost and budgets

Fleece AI Brain is our pick for the company-level meter: the tool that attributes AI spend to agents, tools, and people and puts budgets around them, rather than tracing individual requests. On the Teams plan, AI cost tracking is one of three pillars alongside the AI Organization Map and the shared company brain, so cost is not a separate dashboard you check — it is an attribute of every agent on the map. You see the prospecting agent, its model, its call volume, and its cost in the same view where you see what it does and what it remembers.

The reason we lead with it is that it answers the leadership question directly. When someone asks "what are we spending on AI, broken down by team?", most tools make you assemble the answer from request logs. Here it is the default shape of the data: per-agent costs roll up into per-team budgets, budgets have thresholds, and the whole thing sits on the same local-first Markdown foundation as the rest of the Brain, so the numbers live on infrastructure you control. It is a desktop app for macOS and Windows, priced at €49/user/mo for Teams, with a 14-day trial, no card required.

The honest limit: Fleece AI Brain is a business-level meter, not a request tracer. If your engineers need to see the exact prompt, the token-by-token breakdown of one call, and the latency of each step to debug a chain, that is an observability tool's job, and you should pair the Brain with one. We are the map of what every agent costs; the tools below are the microscope on any single request. See the enterprise brain overview for how the cost pillar fits the whole.

2. Helicone — the fastest way to start seeing spend

Helicone is the lowest-friction on-ramp to LLM cost visibility, because in its common configuration you route requests through a proxy and get logging, cost, and latency without instrumenting much code. Helicone is open-source with a hosted option, which is why we reach for it when we want spend and usage dashboards standing up the same afternoon.

It shines for teams that want per-request logs, cost per model, and caching insights with minimal setup. Its granularity is strong at the request and user level, and it supports custom properties so you can tag traffic. Where it stops, for our purposes, is the org picture: it tells you what your requests cost beautifully, but it is a developer observability tool, not a company org-and-budget map. Pair it with an org-level meter if leadership wants the rolled-up view.

3. Langfuse — open-source tracing and cost analytics

Langfuse is the strongest open-source choice when you want deep tracing and cost analytics you can self-host. Langfuse instruments your app with an SDK and gives you traces, spans, evaluations, and cost breakdowns, with a self-hostable core that appeals to teams with data-residency requirements.

We rate it highly for engineering teams that treat their LLM app like a product: the trace view, the ability to attach evaluations, and the cost-per-trace analytics make it a genuine observability platform rather than just a spend counter. The trade-off is that it lives in the engineering domain — it instruments code and reasons about traces, which is exactly right for developers and largely invisible to the finance-and-operations seat that just wants a per-team number.

4. LangSmith — observability for LangChain and beyond

LangSmith is the natural cost-and-observability layer if your stack is built on LangChain, though it works with non-LangChain apps too. LangSmith provides tracing, evaluation, and monitoring, with token and cost tracking attached to runs, so a team already living in the LangChain ecosystem gets cost visibility as part of a broader debugging and evaluation workflow.

Its strength is depth for complex chains and agents: when a single user action fans out into many model calls and tool invocations, LangSmith's run tree is where you find the expensive step. As with Langfuse, the orientation is developer-first. It answers "why did this run cost what it did?" superbly and "what is the whole company spending on AI, by department?" only if you build that rollup yourself.

5. Portkey — a gateway with governance built in

Portkey sits in front of your models as a gateway and adds cost tracking, routing, caching, and guardrails in one layer. Portkey is a strong fit when you want a control point for many models and providers at once — spend limits, fallbacks, and observability applied centrally to all traffic that passes through it.

We like it for platform teams standardizing how an organization calls models, because the gateway position lets it enforce budgets and see spend across providers without every app instrumenting itself. The consideration is architectural: a gateway means routing your traffic through it, which is a deliberate infrastructure choice. If you want that control point, Portkey is one of the better ones; if you would rather not put a proxy in the hot path, a metering or observability approach fits better.

6. LiteLLM — gateway-level spend tracking across providers

LiteLLM is the pragmatic open-source choice for normalizing many providers behind one interface and tracking spend at the gateway. LiteLLM gives you a unified API across dozens of model providers plus a proxy server with budget and spend controls per key, team, and user.

For teams that already use LiteLLM to avoid provider lock-in, the built-in spend tracking is a natural bonus: you can set budgets per virtual key and see usage without adding a separate tool. Its accounting is organized around keys and teams rather than semantic agents, so it answers "which key or team spent what" well and "which named agent, doing which job" less directly. It is a superb building block, and a fine spend tracker for infrastructure-minded teams.

7. OpenMeter — usage metering for AI billing

OpenMeter is the tool to reach for when your problem is not just watching spend but billing for it. OpenMeter is open-source usage metering built for consumption-based pricing, which makes it a fit for companies that resell AI features and need to meter tokens or requests per customer accurately enough to invoice on.

We include it because a real slice of AI cost tracking is actually revenue-side metering, and general observability tools are not built for that. If you are packaging AI into a product and charging by usage, OpenMeter's metering pipeline is more appropriate than a tracing tool. If you only need internal cost visibility, it is more machinery than the job requires.

8. Datadog LLM Observability — for shops already on Datadog

Datadog LLM Observability is the sensible choice when your organization already runs Datadog for everything else. Datadog extends its monitoring platform to LLM applications with tracing, cost and token metrics, and quality checks, so LLM spend shows up beside the rest of your infrastructure telemetry.

The advantage is consolidation: one platform, one bill, one place your SRE team already lives. The consideration is that it is an enterprise observability suite, so it fits organizations already invested in Datadog far better than a small team looking for a focused, inexpensive cost view. For the right shop it removes a tool from the stack; for the wrong one it adds a platform.

The tools side by side

Here is the field at a glance. "Granularity" is the finest unit each tool naturally attributes cost to; "deployment" is how it sits in your stack; "who it's for" is the seat that gets the most from it.

Which one should you actually pick?

Pick by the seat asking the question, not by feature count. If an engineer needs to debug why one chain is expensive, choose a tracing tool — Langfuse, LangSmith, or Helicone — and instrument the app. If a platform team wants a central control point over all model traffic, choose a gateway — Portkey or LiteLLM. If you bill customers for AI usage, choose OpenMeter. If you already run Datadog, extend it. And if a founder, COO, or department lead wants one honest map of every agent the company runs and what each one costs, choose the org-level meter — that is where Fleece AI Brain sits, and it is the gap the developer tools were never meant to fill.

The reason we keep insisting on "both" is that these answer different questions and neither substitutes for the other. An observability tool without an org meter leaves leadership guessing at the total; an org meter without observability leaves engineers unable to debug the expensive run. The mature setup we recommend is one tracing tool for the engineers and one org-level meter for the business, feeding a shared picture. That org picture is most useful when cost sits next to what each agent does — which is why we bind it to the agent registry and the sprawl audit rather than treating it as a standalone dashboard.

The bottom line

The best LLM cost tracking tool depends on who is asking. For request-level debugging, Helicone, Langfuse, and LangSmith lead; for centralized model traffic, Portkey and LiteLLM; for usage billing, OpenMeter; for Datadog shops, Datadog. For the question a business actually asks — which agents cost what, and are any out of control — the org-level meter wins, and Fleece AI Brain is our pick because it attributes spend to agents and teams on the same map that shows what they do and remember. Start with a download and a 14-day trial, no card required, or compare the plans on pricing. Most teams end up running one tracing tool and one org meter — and that is exactly right.