Own your product's intelligence
Get the best intelligence for your product at the best cost. Choose the right model for the task, whether that's from a big lab or a custom one.
Do you know what your product is spending tokens on?
Case study AI-native product team
Their spend was concentrated in a handful of functions, so Metergraph replayed real production traffic against cheaper candidates, route by route. On the heaviest one the swap cleared every quality bar in replay at a fifth of the cost — and the evals built from their own traces caught the expensive model inventing details the cheaper one stayed grounded on.
Fast-moving dev teams should focus on new feature work, not optimizing every model call
$ npm install metergraph
import { meter } from "metergraph"
export const llm = meter(client, {
key: process.env.METERGRAPH_KEY
})
What Metergraph does
Automatically create custom benchmarks based on your product’s usage
Automatically test models across providers against your usage to see which is best: highest quality, lowest cost, and fastest to respond
Automatically train and deploy a model that is specific to you and your product
Use cases
Model swaps, caching, batching, context tuning — every lever found in your own traffic, validated before it ships.
Upgrade from obsolete modelsWhen a model you depend on is deprecated, replay your traffic against the candidates and migrate on proof, not hope.
Automate post-deployment optimizationPrices fall and models improve monthly. Benchmarks stay fresh from live traces, so yesterday's best pick doesn't quietly become overspend.
Reduce LLM failure and retry ratesSee which routes fail and retry, what those retries cost, and which configuration holds up reliably on your real inputs.
Assess token efficiencyFind the functions burning tokens on bloated context or verbose output, and measure exactly what trimming saves.