Ecologits is live · free plan; paid plans on request · last verified 25 Oct 2026 · viability monitor
Ecologits
Estimate and track the carbon footprint and energy use of GenAI inference calls with EcoLogits.
For Python or JS developers who want per-request CO2 and energy numbers on GenAI calls, EcoLogits is the most direct open route — SDK coverage across OpenAI, Anthropic, Cohere, Mistral, Gemini, and Hugging Face, plus a calculator and an API for non-code workflows. It won't hand you a dashboard or buy offsets; expect to read the methodology and cite it carefully.
Verdict generatedVerified 5d ago · liveness 65/100 · cite: rightaichoice.com/tools/ecologits
- Python or JS/TS developers instrumenting GenAI calls who need per-request CO2 and energy figures
- Sustainability teams auditing inference workloads across multiple model providers
- Researchers studying the environmental cost of LLM and generative AI inference
- Organizations with net-zero commitments that want an open, inspectable methodology
- Teams that need a graphical dashboard for ongoing carbon reporting — this is a library, API, and calculator
- Non-technical stakeholders expecting a no-code reporting interface
- Buyers looking for built-in carbon offset purchasing or remediation workflows
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Skip EcoLogits if you need a graphical reporting dashboard, measured (rather than modelled) emissions figures for compliance-grade disclosure, or coverage of self-hosted and model-training workloads.
The core Python library is free and open source, so the cost of adopting EcoLogits is engineering time rather than licence fees. The EcoLogits API is listed as Contact pricing, which means a conversation with the CodeCarbon team before you can budget for it. For teams already tracking cloud spend, the library route is the lower-commitment path.
In short
Ecologits — Estimate and track the carbon footprint and energy use of GenAI inference calls with EcoLogits. Best for Python or JS/TS developers instrumenting GenAI calls who need per-request CO2 and energy figures, Sustainability teams auditing inference workloads across multiple model providers, Researchers studying the environmental cost of LLM and generative AI inference. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
What is Ecologits?
Ecologits — Estimate and track the carbon footprint and energy use of GenAI inference calls with EcoLogits. Best for Python or JS/TS developers instrumenting GenAI calls who need per-request CO2 and energy figures, Sustainability teams auditing inference workloads across multiple model providers, Researchers studying the environmental cost of LLM and generative AI inference. Free to start; it also has paid plans whose price we could not confirm — see the pricing table.
EcoLogits is an open-source suite for estimating the environmental footprint of generative AI models at inference time, built by the CodeCarbon non-profit under the mlco2 project. Rather than tallying emissions after the fact, it returns a life-cycle-based impact estimate tied to each API call — per-request energy use and CO2 figures instead of a quarterly sustainability slide. The toolkit ships in four pieces. The Python library (pip install ecologits) hooks into the official client SDKs developers already use — OpenAI, Anthropic, Cohere, Mistral AI, Google Gemini, and the Hugging Face Inference API — wired in via a context manager or decorator, and it handles both streaming and non-streaming responses. EcoLogits.js brings the same methodology to JavaScript and TypeScript developers via npm install @ecologits/ecologits.js. The EcoLogits API (api.ecologits.ai/v1beta) packages the assessment as a service for other stacks, and the web-based EcoLogits Calculator gives quick ballpark figures with no code at all. The scope has widened: the project announced it now estimates environmental impacts for AI-generated videos, beyond text and image calls. Methodology draws on tools, best practices, and expertise from Data For Good and Boavizta, and the code, methodology, and data are all openly accessible. Because every number rests on documented assumptions about model size, token counts, hardware, and regional grid emissions factors, EcoLogits fits teams willing to read the methodology and cite it honestly. Treat it as instrumentation that feeds wider carbon accounting, not a replacement for it — and look elsewhere if you want a finished reporting dashboard or someone to handle offsetting for you.
Key features
- Estimate energy use and CO2 emissions per GenAI inference request
- Python library via pip install ecologits
- JavaScript/TypeScript library via @ecologits/ecologits.js
- Supports OpenAI API calls
- Supports Anthropic API calls
- Supports Cohere API calls
- Supports Mistral AI API calls
- Supports Google Gemini API calls
- Supports Hugging Face Inference API calls
- Handles streaming and non-streaming responses
- Integrate via Python context manager or decorator
- EcoLogits API service at api.ecologits.ai/v1beta
- Web-based EcoLogits Calculator for no-code estimates
- Estimates environmental impacts for AI-generated videos
- Life-cycle assessment methodology, open and inspectable
Behind the verdict
Where EcoLogits earns its place is instrumentation. If your app makes GenAI calls and someone upstairs wants to know what those calls cost the planet, wiring in a context manager around the SDK you already use is a small, honest change. The output is per-request, which beats retrofitting estimates from token bills months later. Pick it when you have engineers in the loop. The Python library and the newer EcoLogits.js library both fit codebases that call models directly through official SDKs. Sustainability teams auditing inference across multiple providers get a consistent methodology rather than six incompatible vendor claims. Researchers and journalists will like that code, methodology, and data are open — you can check the assumptions instead of trusting a black box. Pass on it if you need a dashboard, automated reporting cadence, or offset purchasing baked in. There's a calculator for quick numbers and an API for integration, but this is measurement infrastructure, not a carbon-management platform. Non-technical stakeholders will find the library rough going. The honest caveat is precision. Every figure depends on modelled assumptions — hardware, model size, grid intensity by region — so the numbers are estimates you should disclose as such, not audited measurements. In practice we'd cite EcoLogits ranges alongside your own energy data rather than treating a single request's CO2 as gospel. Versus CodeCarbon, the sibling project: CodeCarbon tracks energy and emissions of code running on your own hardware or cloud VMs, while EcoLogits targets the inference calls you send to hosted GenAI providers. If your footprint is mostly self-hosted training and serving, CodeCarbon is the closer tool; they're complementary across a full AI footprint. The recent
Models under the hood
as of 2026-09-01
Limitations
- EcoLogits relies on estimated energy models for each provider and hardware profile, which may not match every deployment exactly — the project is explicit that the methodology rests on documented assumptions about model size, token counts, hardware and regional grid factors.
- It tracks API usage through official client SDKs, not self-hosted models.
- Coverage of new providers and models depends on community support, so the supported SDK list moves at the pace of contributions.
- There is no graphical dashboard in the core library, and it does not purchase offsets or run remediation workflows.
as of 2026-10-09
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Is Ecologits still active?
Ecologits is live · free plan; paid plans on request · last verified 25 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Ecologits? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Ecologits?
How we verified this
Every listed fact about Ecologits— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 25 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Ecologits 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed description, who should skip it, summary, our verdict, pricing tiers, who it suits, features
- — changed pricing tiers, description, our verdict, summary
- — changed SSL certificate
- — changed switching in, who should skip it, switching out, description, use cases, our verdict, setup time, hidden costs, pricing fit, limitations
- — changed our verdict, who it suits, description, features, who should skip it, pricing tiers, summary
- — changed summary, switching in, hidden costs, pricing fit, setup time, features, who should skip it, description, switching out
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Ecologits cost in 2026?
Ecologits: Free to start; it also has paid plans whose price we could not confirm — see the pricing table. Pricing last verified 25 Oct 2026.
Plans compared
Every published Ecologits tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open Source (Python / JS libraries) | Free | — | — |
| EcoLogits Calculator | Free | — | — |
| EcoLogits API | Contact | quote | Teams running services outside of Python that still want the same impact assessment methodology in their applications.Adds: Adds a hosted API at api.ecologits.ai/v1beta on top of the open library, so you do not need to embed Python to get estimates. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Ecologits's pricing actually pencils out — and where peers do it cheaper.
The core Python library is free and open source, so the cost of adopting EcoLogits is engineering time rather than licence fees. The EcoLogits API is listed as Contact pricing, which means a conversation with the CodeCarbon team before you can budget for it. For teams already tracking cloud spend, the library route is the lower-commitment path.
Has Ecologits' pricing changed?
No plan price for Ecologits has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Ecologits?
Pick Ecologits if you are Python or JS/TS developers instrumenting GenAI calls who need per-request CO2 and energy figures, Sustainability teams auditing inference workloads across multiple model providers, Researchers studying the environmental cost of LLM and generative AI inference, Organizations with net-zero commitments that want an open, inspectable methodology.
Look elsewhere if you are Teams that need a graphical dashboard for ongoing carbon reporting — this is a library, API, and calculator, Non-technical stakeholders expecting a no-code reporting interface, Buyers looking for built-in carbon offset purchasing or remediation workflows, Precision-critical reporting where modelled hardware and grid assumptions aren't acceptable.
Skip it if Skip EcoLogits if you need a graphical reporting dashboard, measured (rather than modelled) emissions figures for compliance-grade disclosure, or coverage of self-hosted and model-training workloads.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Ecologits actually fits — and what changes day-one when you adopt it.
You wrap your OpenAI or Anthropic client with the EcoLogits context manager, run your existing test suite, and read the CO2 and energy estimate returned alongside each response.
Outcome: You get per-request footprint numbers without changing how your application calls the model, and you can log them next to latency and token counts.
You compare emissions estimates across OpenAI, Anthropic, Google Gemini and Mistral AI calls for the same workload, then use the EcoLogits Calculator for a quick check on models you have not wired up yet.
Outcome: You get a provider-level comparison grounded in one open methodology rather than four vendor claims, useful for internal reporting.
You run EcoLogits in a CI job and fail the build when the estimated emissions for a benchmark prompt set exceed a threshold.
Outcome: Regressions in model choice or prompt length that raise the estimated footprint get caught before they reach production.
Use cases
- Measure the carbon footprint of your ChatGPT API integration to report sustainability metrics
- Compare emissions across different LLM providers and models to choose the lower-impact option
- Set automated alerts when API usage exceeds a monthly CO2 budget
- Integrate EcoLogits into CI/CD pipelines to flag deploys with excessive estimated emissions
- Educate stakeholders on the environmental cost of generative AI using concrete per-request data
- Instrument a non-Python service by calling the EcoLogits API at api.ecologits.ai/v1beta
- Get a quick pre-build estimate for a model or prompt from the web calculator
- Estimate the environmental impact of AI-generated videos before committing to a video pipeline
Setup time & first value
How long it actually takes to get something useful out of Ecologits — broken out by persona, not the marketing-page minute.
For a Python developer already calling a supported SDK, adding EcoLogits is a pip install plus a context manager or decorator around the client — a matter of minutes. Teams going the API route at api.ecologits.ai/v1beta should budget time for wiring an HTTP call and deciding where to store results, and anyone new to the methodology should read the methodology page before trusting a number in a
Switching to or from Ecologits
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual spreadsheet estimates: replace hand-calculated token-to-energy figures with per-request estimates returned by the library.
- →From a general cloud carbon tracker: keep it for infrastructure, and add EcoLogits to cover the inference calls it does not see.
- →From an ad-hoc logging wrapper: swap your custom wrapper for the context manager or decorator so estimates come from one documented methodology.
- ↗To a full carbon accounting platform: export the per-request estimates and fold them into your organisational inventory.
- ↗To measured hardware telemetry: move to metered power data if you need figures your auditors will accept as measured rather than modelled.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Ecologits”, and we withheld 6: 6 could not be judged, because “Ecologits” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Ecologits.
Official links
What are the best Ecologits alternatives?
The closest alternatives to Ecologits we list are NannyML, AgentOps and Athina AI. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| NannyML | Freemium | Pricing not published | — |
| AgentOps | Freemium | Pricing not published | — |
| Athina AI | Freemium | Pricing not published | — |
Alternatives to Ecologits
View all
Tools that pair well with Ecologits
Common stack mates teams adopt alongside Ecologits, with the specific reason each pairing earns its keep.
NannyML
NannyML estimates your ML model's live performance without ground-truth labels and alerts you only when it actually matters
AgentOps
Trace, replay and debug AI agent runs — LLM calls, tool use and multi-agent interactions in one timeline, via a single SDK
Athina AI
Athina is a shared workspace where your team writes prompts, runs evals, annotates datasets, and traces LLM calls in production.
Ecologits vs its closest rival
Ecologits's closest head-to-head is with Spider Cloud — Choose Ecologits if your primary goal is to monitor and reduce the carbon footprint of generative AI API calls; it's free, lightweight, and integrates with major AI providers. Choose Spider Cloud if you need fast, reliable web scraping and crawling to feed data into AI agents or RAG pipelines—its recent Browser AI commands and scraper catalog make it powerful for dynamic extraction. They solve entirely different problems, so decision hinges on whether you need environmental metrics or web data. Verdict generated
Head-to-head comparisons
Ecologits vs Spider Cloud
Choose Ecologits if your primary goal is to monitor and reduce the carbon footprint of generative AI API calls; it's free, lightweight, and integrates with major AI providers. Choose Spider Cloud if you need fast, reliable web scraping and crawling to feed data into AI agents or RAG pipelines—its recent Browser AI commands and scraper catalog make it powerful for dynamic extraction. They solve entirely different problems, so decision hinges on whether you need environmental metrics or web data.
GeneratedEcologits vs Temporal Ai
EcoLogits is a free, lightweight Python library for measuring API carbon footprints — perfect for green developers but limited in scope. Temporal AI is a full‑fledged durable execution platform for fault‑tolerant AI workflows, now with usage‑based billing for better cost transparency. Choose EcoLogits for sustainability monitoring; choose Temporal for reliable orchestration at scale.
GeneratedEcologits vs Screenplayiq
Ecologits is a must-have for AI developers or sustainability teams who need to measure and minimize the carbon footprint of their generative AI usage. ScreenplayIQ is purpose-built for film industry professionals seeking data-driven script evaluation and box office projections. Choose based on your domain: eco-conscious AI development vs. screenplay market analysis.
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