Alternatives

When not to use Tetrameter.

Six cases where something else is the right answer, and what to reach for instead. If one of them is you, we would rather you found out on this page than three weeks into a trial.

Where we are the wrong tool.

You need to debug prompts, replay conversations or grade output quality.

Use an LLM observability tool — Langfuse, Helicone or Portkey.

We structurally cannot help. No field in our data model holds a prompt or a completion, and the ingest endpoint rejects a request carrying one rather than dropping it quietly. That is the guarantee that gets us through security review in regulated industries, and the price of it is that we will never be the tool you open when an answer looks wrong.

You need carbon accounting across the whole company — buildings, travel, supply chain.

Use Watershed, Persefoni or Sweep.

We measure one line item. A serious inventory has hundreds, and building the other 99% badly to avoid buying it properly is how a disclosure fails assurance. We would rather be the AI figure those platforms cannot produce than a worse version of what they already do well.

You need to manage cloud spend generally, not AI spend specifically.

Use Vantage, Finout, or your provider's own cost tooling.

AI is one of your bills. If the problem is Kubernetes and storage, a FinOps tool sees the whole picture and we see a slice of it. Come back when the AI slice is large enough that nobody can tell you what it is buying.

You want the methodology and can build the rest yourself.

Use EcoLogits, or our own engine — both open source.

This is a real answer and we would rather you take it than pay us for something you do not need. EcoLogits is the best open methodology in this space and a non-profit. Our engine is Apache-2.0 for the same reason: a methodology nobody can check is one nobody should file against. What we charge for is the operated product around it — attribution, the artifact, the restatement log, the waste engine — not the arithmetic.

You want to measure employees using chatbots, not applications calling APIs.

Use a browser-extension or endpoint tool built for that.

Different problem, different data path. We measure what your software does. Somebody's personal ChatGPT session does not pass through your application, so we never see it. (This is a surface we are scoping and do not yet have — saying so beats implying otherwise.)

You run inference on hardware you own, and you can meter it.

Use CodeCarbon, or your own telemetry against the wall.

Measured power beats any estimate, and if you can read the meter you should. Our four-tier framework treats measured energy as Tier 3 and everything we can do for a third-party API as Tier 2 at best, because no commercial provider discloses per-request energy. Where you have the real number, use it.

The narrow case

When we are the right answer.

All four of these, together:

AI is in your product, not just on your employees’ laptops — so the calls run through software you control.
Somebody will have to put a number in a document. A regulator, an enterprise buyer’s procurement questionnaire, or a customer asking what their account emits. A dashboard does not survive that conversation; it needs lineage, factor versions, uncertainty and a restatement history.
Your workload is agentic — one business outcome costs many calls — so a per-call average is answering a different question than the one you have.
You cannot store prompts, or would rather not, because you sell into finance, health, legal or government.

Fewer than four and something on this page is probably cheaper. All four and we are the only tool we know of that does it.

We are pre-launch, running on four of our own products, with independent methodology review scheduled and not yet done. If you need a vendor with a decade of references, that is also a legitimate reason to wait — and the case study is the most honest picture of what we have actually measured so far.

Why a vendor publishes this.

Every figure on this site carries an uncertainty band, and the first case study revises our own headline downward because our engine had a bug that flattered it. A page claiming we suit everybody would sit badly next to that.

The practical version: a compliance figure has to survive somebody hostile reading it. If we will not name the cases against ourselves, there is no reason to believe the cases we make for ourselves.

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