FinOps Framework · Optimize Usage & Cost · Sustainability
Know the carbon cost of every AI call
SAVI calculates CO2e per LLM API call at ingest time using model energy benchmarks and the Azure regional grid carbon intensity. Track, attribute, and report AI carbon emissions by team, model, and intent cluster.
AI compute is a measurable and growing Scope 2 emissions contributor
ASX-listed companies must disclose climate-related risks and emissions under ASIC guidance aligned with TCFD. AI compute is a growing and measurable contributor to Scope 2 emissions - but no existing FinOps tool tracks it at team and project granularity. SAVI is the only place where AI-specific carbon data exists at the level of every API call, every team, and every intent cluster.
ASIC climate disclosure requirements are tightening for ASX-listed companies
TCFD-aligned disclosure now requires quantitative Scope 2 emissions data. AI compute needs to be in scope - and none of your provider invoices give you the granularity to report it.
Without per-call attribution, you can't tell which AI workloads are high-emitters
A GPT-4o call emits approximately 10× the CO2e of a Haiku call for equivalent output. Without per-call measurement, model selection for carbon-conscious procurement is guesswork.
Prompt inefficiency wastes both money and emissions
Bloated prompts that return far less output than their input implies are both expensive and carbon-intensive. SAVI surfaces these as optimisation opportunities - with estimated savings in both $ and kg CO2e.
Carbon tracked on every AI call - automatically, in real time
Every AI call that passes through SAVI is automatically assessed for carbon impact - no batch job, no manual data entry, no lag. SAVI uses published model energy benchmarks, updated annually, combined with real-time regional grid carbon intensity data to estimate the CO2e footprint of each call. The result is a carbon number on every LLM event, attributed to the team, project, and intent cluster that generated it.
- Carbon calculated automatically at call time - no separate data pipeline or nightly job
- Model benchmarks updated annually as new models launch and hardware efficiency improves
- CO2e attributed to team, project, and intent cluster - same granularity as your cost data
Carbon tracking · Every AI call · Automatic and real-time
| Metric | Granularity | Use |
|---|---|---|
| CO2e per team per month | Team × month | Internal chargeback + ESG reporting |
| CO2e per model family | GPT-4o vs. Claude Haiku vs. Gemini | Carbon-conscious procurement |
| Carbon cost of a cache miss | Per event | Justify semantic caching investment |
| Monthly carbon trend | 13-month rolling | TCFD trend disclosure |
| Carbon by intent cluster | Per cluster | Identify high-emission workloads |
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Available carbon metrics and their uses
Track CO2e by team, model, and intent cluster
SAVI attributes carbon to the same dimensions as cost: team, project, model family, and intent cluster. FinOps teams receive CO₂e chargeback reports alongside financial chargebacks, with full visibility into emissions by team, model, and use case. Procurement can compare the carbon impact of different models in a single view and factor emissions into selection decisions.
- Track carbon like cost with team-level CO₂e chargeback delivered monthly alongside financial allocation
- A clear CO₂e comparison across leading model families to support sustainability-driven vendor selection
- Track emissions over a 13-month rolling window for compliant TCFD reporting
Prompt optimisation carbon insights
When an intent cluster uses more tokens than needed for its complexity, SAVI highlights it in the CFO dashboard with clear savings and emissions impact. Engineering applies prompt improvements, and SAVI automatically measures the before-and-after cost and CO₂e reduction for every change.
- Intent cluster carbon insights surfaced in CFO Dashboard when token bloat is detected
- Estimated $ and kg CO2e savings shown per recommendation
- Before/after measurement: SAVI tracks the carbon impact of every implemented optimisation
Carbon insight → recommendation → measurement loop
ESG & Carbon
Can you answer your board's climate disclosure questions for AI compute?
6 questions. Answer honestly.
Do you know how much carbon your AI estate produced last quarter?
Can you attribute AI carbon emissions to specific teams and workloads - not just a total number?
If your board asked for AI climate disclosure data today - could you produce it without a manual audit?
Can you compare the carbon footprint of different AI models running the same task?
Do you know which AI workloads are your biggest carbon emitters - and can you act on that information?
Can you show dollar savings and carbon savings from AI optimisation in the same view?
This checks one slice of your AI estate. Take the full AI Cost Ownership Assessment →
Your AI gap score
Answer to begin
Complete all questions to see your result
AI carbon visibility - from the first API call.
SAVI calculates CO2e on every LLM call at ingest time, attributed to team, model, and intent cluster. Book a demo to see your AI carbon footprint.