GreenPick

Write greener, cheaper cloud infrastructure — before you deploy.

A design-time sustainability co-pilot that runs as an MCP server inside your vibe-coding environment (Cursor / Claude Code / Kiro). Ask it where to deploy, when to run a job, or how to right-size — and it answers with an honest carbon + cost + latency tradeoff, in-editor, in seconds. (It responds when you ask; auto-nudging on infra-code changes is on the roadmap.)

Status: working — 15 tools. Live carbon for US (EIA-930) and EU (ENTSO-E), cross-cloud cost parity (Vantage-refreshed), per-axis R/Y/G ratings, carbon-aware scheduling + day-ahead forecast, GPU-training and LLM-inference carbon, right-sizing, serving-pattern and data-locality/residency checks — all shipped. Falls back to clearly-labelled mock data offline or without API keys.

The same 40-GPU-hour training run emits ~0.7 kg CO₂ in Stockholm vs ~19 kg in Sydney — a ~30× difference from region choice alone. Ask GreenPick and it surfaces that (with the cost and latency tradeoff) while you're still in the editor, before you commit.

▶ See the 5-minute demo — three scenarios with real tool output (training placement, the egress backfire, the forecast).

Features

• 🧮 Right-sizing that cuts cost and carbon together — the FinOps win with a carbon twin. The money saved is the reason to run it; the carbon comes free.
• ⏱ Carbon-aware scheduling — run deferrable jobs (ETL, batch, training) when the grid is cleanest, now or day-ahead. A decision you make for every recurring job.
• ⚡ GPU training & LLM inference carbon — estimate a run's footprint and the greener where + when; the size/placement choices that actually move the number.
• 🌍 Greener region picks — ranked by carbon, with the cost + latency tradeoff shown, not hidden.
• 🛡 Backfire guards — flags when a greener move loses to egress, or crosses a data-residency line.
• 🔌 Live US + EU grid data, honest mock fallback — no API keys required to start.

Contents

• Why this exists
• What it is / is NOT · How it differs
• Scope
• MCP tools
• Data sources
• Honest limitations
• Quick start
• FAQ
• Contributing
• License

Why this exists

The same web service emits very different CO₂ depending on which region it runs in, because electricity grids differ in carbon intensity. Existing tools either rank regions in the abstract, or measure carbon after you deploy. GreenPick does it at the moment you choose, and tells you what the greener choice costs you in latency and dollars — so you can accept or reject it with eyes open.

What it is / is NOT

• IS: directional "greener choice" guidance for a developer picking a cloud region while building. Guidance, with tradeoffs stated.
• IS NOT: audited carbon accounting, ESG/Scope-2 reporting, or a real-time scheduler. GreenPick does not claim GHG-Protocol or ISO compliance — that's what the paid data vendors (Electricity Maps, WattTime) provide. GreenPick is for decisions, not disclosures.

How it differs from what's out there

• Measurement tools (Cloud Carbon Footprint, CodeCarbon, Kepler) report what you already emitted, after you deploy. GreenPick advises before — at the choice.
• Cloud providers' own tools (AWS CCFT, Azure, Google's "Low CO₂" region labels) cover one cloud and show carbon alone. GreenPick is cross-cloud and weighs carbon against cost and latency so you see the tradeoff, not just a green badge.
• CI/CD carbon tools act at merge time on a pipeline job. GreenPick works earlier, in the editor, across more decisions (scheduling, right-sizing, training/inference, egress & residency).
• Grid-data SDKs (Electricity Maps, WattTime, GSF Carbon Aware SDK) are building blocks; GreenPick is the decision layer on top of that kind of data.

The combination — cross-cloud, carbon + cost + latency, at design time, in the editor, with backfire guards — is the part nothing else puts together.

Scope

• Providers: AWS, GCP, Azure regions.
• Workloads: long-running web/API services, GPU training runs, and deferrable jobs (ETL / batch / backups) — placement and timing for each.
• Carbon = grid carbon intensity by region (directional, point-in-time; live US + EU), plus a day-ahead forecast (EU) for forward-looking scheduling.
• Cost = compute + egress ballpark (deterministic static table, apples-to-apples across clouds; never LLM-invented).
• Latency = geographic estimate to a stated user location.
• Efficiency levers: greener instance family (perf-per-watt), right-sizing (cost + carbon together), and a carbon-metric scaffold to track your app over time.
• Guardrails: data-locality / egress backfire checks and cross-jurisdiction residency flags.
• Free data only. No flow-traced consumption accounting; carbon forecast is relative, not absolute.

Roadmap: data-format/engine efficiency advice, sub-national grid zones, CI/CD integration. See docs/user-questions-backlog.md.

MCP tools

15 tools, grouped by what you're trying to do.

Pick a region

Tool	What it does
compare_regions_tri_axis	Rank candidate regions by carbon with cost + latency tradeoffs; recommend a pick, each axis rated R/Y/G. Pass regions to rank only the ones you'd actually consider.
greener_alternative_to	"You picked X — the greenest region within +N ms latency is Y (−Z% carbon, Δcost)." The core nudge.
region_carbon_now	Current gCO₂/kWh for a region, with source + timestamp + freshness.

Time a job (scheduling)

Tool	What it does
cleanest_hours_to_run	Cleanest recent hours to time a deferrable job (ETL, backups, training), + % reduction. Pass window_hours for a job of known duration → cleanest contiguous window to start at.
cleanest_time_of_day	For a recurring job (nightly batch): the region's cleanest time of day, averaged across recent days. A daily pattern, not a 7-day weekly one.
carbon_forecast	Forward-looking: which upcoming hours will be cleanest, from the EU day-ahead wind+solar forecast (ENTSO-E). Relative ranking; US/uncovered zones degrade to the recent historical pattern.

Estimate a workload's carbon

Tool	What it does
estimate_workload_carbon	Rough kgCO₂/month for a workload in a region (an estimate, not a measurement).
estimate_training_carbon	Carbon of a GPU training run (TDP-based energy × live grid), with greener where (region ranking) + when (cleanest hours) — the two levers that compound for portable, deferrable training.
estimate_inference_carbon	Energy + CO₂ of an LLM inference workload, by model size class × output tokens × requests (× live grid). A low–high band; smaller size class is the big lever. Self-hosted-friendly; not a named-model leaderboard.

Efficiency & guardrails

Tool	What it does
greener_instance_family	A greener same-size family in the same region via perf-per-watt (newer generation / ARM Graviton-class does the same work on less energy).
rightsizing_check	Right-sizing (the #1 FinOps win) with a carbon twin: if you're over-provisioned, a smaller size cuts both cost and carbon → the right-sized target with monthly $ and kg CO₂ saving.
data_locality_egress_check	Warn when running compute away from where your data lives backfires: directional egress $ + coarse transfer carbon, and whether a greener-region move is still a net win.
serving_pattern_advice	For a user-facing platform: serve from one region or several? Traffic-weighted latency + carbon, with the multi-region data-sync egress and cross-jurisdiction residency catches.

Instrument & understand

Tool	What it does
carbon_metric_scaffold	Emits paste-in instrumentation (Python) so your app reports its own estimated carbon over time — observable proxies × GreenPick's power model × live grid. Self-tracking, directional; not a runtime meter.
explain_methodology_and_limits	The assumptions and what GreenPick explicitly does not claim.

Data sources (free; swappable behind one interface)

• EIA-930 (US balancing authorities) and ENTSO-E (EU zones) — live grid carbon, auto-routed by region. Both also drive cleanest_hours_to_run from recent hourly history.
• Mock — deterministic, offline default; clearly labelled quality: "mock" so nothing is mistaken for a real reading. Used wherever a key is missing or a region isn't covered.
• Cost: a dated static pricing table (data/pricing.json), refreshed from live Vantage pricing via scripts/refresh_pricing.py using one matched method across all three clouds.
• Instance-family efficiency: directional perf-per-watt multipliers (data/instance_families.json).
• Latency: great-circle distance from region coordinates to the user's location.

Set EIA_API_KEY and ENTSOE_TOKEN in .env (both free) for live carbon; GREENPICK_CARBON_SOURCE selects the source (auto routes US→EIA, EU→ENTSO-E, else mock).

The data layer is a swappable repository (MOCK by default, works offline). Nothing here invents numbers: the LLM writes the rationale, the code supplies the figures.

Honest limitations (shipped in the tool itself)

Directional, not audited. Region→grid-zone mapping is approximate. Carbon is point-in-time, not flow-traced consumption. Cost is a ballpark, not a quote. Latency is a geographic estimate. "Greener" assumes grid intensity dominates and ignores embodied/hardware carbon and provider RECs.

Quick start

# Python 3.11+
pip install greenpick          # from PyPI
python -m greenpick            # run the MCP server (stdio)

Or from source, for development (editable install + tests):

git clone https://github.com/bithug01/greenpick.git
cd greenpick
pip install -e ".[dev]"
cp .env.example .env            # defaults to mock data — works offline
python -m greenpick             # run the MCP server (stdio)
pytest                          # run tests

Connect it to an MCP client (e.g. Kiro / Claude Code / Cursor) by pointing the client at python -m greenpick in this directory. See .env.example for switching the data source.

FAQ

Do I need API keys? No — GreenPick runs on clearly-labelled mock data out of the box. For live carbon, set two free keys in .env (EIA_API_KEY for US, ENTSOE_TOKEN for EU) and GREENPICK_CARBON_SOURCE=auto. Without them it still works; it just says quality: "mock".

Why "directional" and not audited numbers? Because it's for a design-time decision, not a compliance report. "Stockholm is ~30× cleaner than Sydney" is what you need to pick a region in seconds — a certified figure is a different job for a different tool (Electricity Maps, WattTime). GreenPick is for decisions, not disclosures, and says so in every tool's limitations.

My cloud says this region is already 100% renewable — so does region even matter? That's market-based accounting (the provider matches usage with renewable certificates/PPAs over a year). GreenPick uses location-based accounting — the actual carbon intensity of the grid you physically draw from, hour to hour. Both are legitimate (the GHG Protocol asks for both), and they answer different questions: market-based is about procurement claims; location-based is about the real grid your workload leans on right now. GreenPick is deliberately location-based because that's what a placement/timing decision actually affects — you can't change the provider's annual REC portfolio, but you can choose a cleaner grid or a cleaner hour. We ignore RECs by design, not oversight.

What happens for a region with no live data? It falls back to the recent historical pattern or to mock, and labels which — it never presents a fallback as a live reading. Forecast likewise degrades to historical (labelled) where no day-ahead feed exists (e.g. US).

Can it rank Claude vs GPT vs Llama for carbon? No, by design. estimate_inference_carbon works by model size class (self-hosted-friendly), not a named-model leaderboard — vendors don't disclose the serving details that would make such a ranking honest.

How do I ask it something? You don't call tools directly — you talk to your MCP client in plain language ("where's the greenest region to run this?") and it invokes the right tool. See the demo for the kinds of questions that trigger each one.

Contributing

Contributions of any kind are welcome — a bug report, a data-source correction, a sharper caveat, or code. See CONTRIBUTING.md for the short version. The one thing GreenPick asks: keep it honest and directional — no invented numbers, no precision the data can't back (that discipline is the whole point).

License

MIT — see LICENSE. © 2026 Data Science Advisers, LLC.