Prismatic Labs

The sensing layer for planet-aware AI

You can see your API bill. You can’t see which call, which team, or which feature is driving it – and you have no data on the energy, carbon, or water behind it. Vetch gives you all four, per call – two lines of code, never reads your prompts.

pip install vetch

Works with

OpenAI · Anthropic · Vertex AI · Azure | LangChain · LlamaIndex

The problem

AI consumes planetary resources at scale – and we have zero granular visibility into its impact.

Energy

A single GPT-4 query uses ~10x the energy of a Google search. Extended thinking modes multiply this 2-4x.

Shaolei Ren et al., 2024

Water

US datacenter water use is projected to reach 660 billion litres by 2026. Regional stress varies up to 3x.

IEA, 2025

Hardware

GPU manufacturing adds 5-15% to AI’s real carbon footprint – embodied emissions invisible to most tools.

Gupta et al., 2022

Why now

AI environmental accountability is becoming mandatory – but the tooling isn’t ready

Six of nine planetary boundaries crossed. Stockholm Resilience Centre confirms we’ve exceeded safe limits on climate, freshwater, and biodiversity. The physical constraints are real and tightening.

Regulation is landing – with teeth. EU AI Act (2025-2027) and CSRD require AI environmental reporting. Penalties up to €35M or 7% of global revenue. No standard tooling exists to comply.

Energy costs are structurally volatile. AI energy decisions are now financial and geopolitical decisions.

Cost of non-compliance: up to €35M. Cost of Vetch: two lines of code.

See Vetch in action

Two lines of code. Four dimensions of AI impact.

Wrap your existing LLM calls with Vetch. Your code doesn’t change – you get energy, carbon, water, and cost tracking on every inference.

from vetch import wrap

with wrap(region="eu-west-1") as ctx:
    # your existing code, unchanged
    response = client.chat.completions.create(
        model="gpt-4o", messages=[...]
    )

ctx.event["estimated_energy_wh"]  # 0.509 Wh
ctx.event["estimated_carbon_g"]   # 0.178 gCO2e
ctx.event["estimated_water_l"]    # 0.0009 litres
ctx.event["estimated_cost_usd"]   # $0.0055
ctx.event["signal_quality"]       # "live" | "stale" | "estimated"

Open-source AI monitoring

Measure AI energy, carbon, water and cost – per call

Energy

Know the true electricity cost of every inference, not just the API bill. Model-specific Wh estimates from token counts and provider power profiles.

Carbon

Live gCO2e per call, using real-time grid carbon intensity from Electricity Maps. The number you need for CSRD and EU AI Act reporting.

Water

Datacenter cooling consumes water. Vetch estimates litres per inference using regional water efficiency data. Stress levels vary up to 3x by region.

Cost

Tag every call by team, feature, or customer. Stop guessing which product lines are burning your LLM budget.

Exports to

Datadog · Grafana · any OTLP-compatible sink

Trust by design

Privacy-first: Zero access to prompts or completions. Only model, tokens, region, latency.
Fail-open: If Vetch fails, your LLM call still works.
Fail-loud: Every log includes signal quality. Stale data? We say so.

Vetch roadmap

Start measuring today. Automate compliance next.

NOW

Visibility

Energy, carbon, water, and cost per LLM call. Four providers, LangChain and LlamaIndex support, OTLP export. Available now.

NEXT

Orchestration

Route to cheaper, greener regions – saving 15-30% on compute while cutting carbon. Defer workloads when planetary thresholds breach.

THEN

Accountability

Per-call emissions attribution, carbon budgets, regulatory dashboards. Pre-formatted for EU AI Act and CSRD compliance reporting.

Beyond AI

Making the invisible measurable

AI is reshaping energy grids. Supply chains are rewriting grocery bills.
Both are happening inside systems most people never see – until they get the bill.
We build the meters.

by Prismatic Labs

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In botany, tare and vetch are in the same plant family. Vetch fixes nitrogen in the soil. Tare grows among the wheat, revealing what’s hidden. Same family, different job.

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