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#24 of 30 · AI search product

Perplexity

Search-augmented answers on other people's models, with nothing disclosed.

San Francisco, California·perplexity.ai ·Verified Oct 16, 2026
DLittle data
Index score
Little data

In plain words

How scores work

Perplexity scores 37 out of 100 — laggard. Little or no disclosure, fossil-backed capacity, or active disputes with regulators and communities. Confidence in this score is little data (D).

What helps

  • Nothing on record.

What hurts

  • Efficiency · Retrieval-augmented pipelines run multiple model calls per answer; no figures published.
  • Transparency · No environmental disclosure of any kind.
  • Commitments · No targets.

What nobody knows

  • Per-prompt energy disclosed
  • Per-prompt water disclosed
  • Training emissions in model cards
  • Annual environmental report
  • Location-based Scope 2 reported
  • +4 more

Perplexity layers web retrieval on top of frontier models from OpenAI and Anthropic and its own Sonar family, hosted on AWS and Cerebras. Retrieval-augmented answers typically cost more compute than a plain chat turn, and the company has published no environmental data at all.

Products
Perplexity · Sonar API · Comet browser
Hosting
AWS (EC2 P5) plus Cerebras inference and third-party model APIs.

Key figures

Per-prompt and fleet metrics. Hover a title for what it means; open “How we got this number” for the working.

How to read the labels:Company figureCompany figure. Published by the company itself. We take it as given and record the scope next to it.Measured independentlyMeasured independently. Measured or estimated by researchers or analysts with no stake in the result.ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.— hover any label.
Energy per promptElectricity used to answer one typical request. Under 0.3 Wh is good. A 10 W LED bulb uses 0.3 Wh in about two minutes.
0.80
Wh / typical prompt
≈ 66.7 smartphone charges per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Amazon Web ServicesCerebrasEpoch AI

ecogpt.co estimate; no company figure.

How we got this number

A retrieval-augmented answer runs a web search pass plus one or more model calls — Sonar on Cerebras, or OpenAI and Anthropic frontier models — so roughly two to three times a plain 0.3 Wh chat turn. Uncertainty roughly ±3×.

Water per promptWater evaporated cooling the servers for one request. Scope matters: on-site cooling only, or also the water used to generate the electricity (which adds ≈1.8 mL per Wh).
0.12
mL / typical prompt
≈ 0.24 500 mL bottles per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Amazon Web ServicesCerebrasEpoch AIAmazon

On-site cooling only, so comparable with Google and OpenAI's figures.

How we got this number

0.8 Wh per prompt × 0.15 L/kWh (AWS's disclosed fleet WUE). Counting the water evaporated to generate the electricity adds ≈1.44 mL.

Carbon per promptGreenhouse gas from one request on the grid where it actually runs. Below 0.1 g is clean. A kilometre in a petrol car is about 170 g.
0.312
gCO₂e / typical prompt
≈ 1.84 km driven (petrol car) per 1,000 prompts
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Amazon Web ServicesCerebrasEpoch AI

Location-based: what the grid actually emitted. Renewable certificates would bring the market-based figure near zero.

How we got this number

0.8 Wh × 390 gCO₂/kWh (US grid, location-based).

Annual emissionsThe company's whole yearly footprint, in million tonnes of CO₂e. Clouds and device makers dwarf pure labs because they also count shops, logistics or products. Compare the trend, not the size.
0.02
MtCO₂e / year (AI compute only)
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Amazon Web Services

Location-based estimate for the AI fleet only; not comparable with a full corporate inventory.

How we got this number

7 MW × 8,760 h × 80% utilisation × 390 gCO₂/kWh (US grid). Compute only — nothing else the company does is counted.

Emissions trendDirection of travel against last year or the company's own baseline. Negative is good. Positive means the AI build-out is outrunning clean energy.
+100
% (query volume roughly doubling year on year)
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Amazon Web Services
How we got this number

Perplexity reports no inventory; its query volume has roughly doubled each year, and compute follows it.

Water per kWh (WUE)Litres of water consumed per kilowatt-hour of server electricity, across the fleet. 0.15 is excellent, 0.3 is good, 1.8 is the US data-centre average.
0.15
L/kWh (inherited from AWS)
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Amazon
How we got this number

No disclosure; AWS's fleet figure applies to the EC2 capacity it rents.

Clean electricityShare of electricity matched with renewable or carbon-free sources. Annual matching counts certificates bought anywhere; hourly matching on the same grid is far stricter.
100
% annual renewable matching (inherited from AWS)
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Amazon
How we got this number

AWS matches 100% of its electricity annually; Perplexity has no procurement of its own.

Hardware & materials

The chips, power, copper and future e-waste behind the fleet — the part sustainability reports leave out

Factors we use
AI chips in service
5,000
GPU-equivalents (order of magnitude)
≈ 69 racks of 72 GPUs
ecogpt.co estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.Amazon Web ServicesCerebras

GPUs, TPUs or custom accelerators the company runs. Analyst figures are in H100-equivalents, company figures in physical chips.

How we got this number

AWS's case study describes peaks of 100,000 queries an hour on EC2 P5 (H100) instances; Sonar runs on Cerebras wafer-scale systems. A few thousand GPU-equivalents of leased capacity. Uncertainty ±3×.

Power draw
7.00
MW
≈ 5,250 US homes' worth of power
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Amazon Web ServicesCerebrasSemiAnalysis

Megawatts the AI fleet pulls. One megawatt runs roughly 750 US homes.

How we got this number

5,000 accelerators × 1.3 kW each, all-in. Fleet power implied by the chip count.

Electricity a year
0.05
TWh / year
≈ 4,762 US homes for a year
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Amazon Web ServicesCerebrasSemiAnalysis

Terawatt-hours per year. One TWh is a year of electricity for about 95,000 US homes.

How we got this number

7 MW × 8,760 h × 80% utilisation. Compute fleet only.

Carbon baked into the hardware
2.6
ktCO₂e embodied in chips and servers
≈ 565 petrol cars driven for a year
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Amazon Web ServicesCerebrasarXiv 2607.05408NVIDIA developer blog

CO₂e emitted making the chips and servers before they answer a single prompt.

How we got this number

5,000 accelerators × 514 kg CO₂e each (chip plus share of the host server). Emitted in manufacturing, before the first prompt is served.

Future e-waste
85.0
tonnes of servers to retire within ~5 years
≈ 0.5 empty Boeing 747s by weight
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Amazon Web ServicesCerebrasNVIDIAThe Register

Tonnes of server hardware that will be retired in three to five years.

How we got this number

5,000 accelerators × 17 kg of server hardware each. Tomorrow's e-waste unless reused; generative AI could produce 2.5 Mt of it a year by 2030.

Copper in the build
189
tonnes of copper in the build
≈ copper for 2,277 electric cars
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Amazon Web ServicesCerebrasSemiAnalysisBHPS&P Global Market Intelligence

Tonnes of copper in cabling, busbars and transformers.

How we got this number

7 MW × 27 t of copper per MW (cabling, busbars, transformers).

Fossil generators on site
3.00
backup diesel generators (implied)
≈ 7.8 MW of backup capacity
DerivedDerived. Arithmetic on a disclosed or independent input using a published conversion factor.Amazon Web ServicesCerebrasSemiAnalysisLatitude MediaLatitude Media / Floodlight

Diesel backup units or gas turbines at the data centres, permitted or observed.

How we got this number

7 MW ÷ 2.6 MW per generator, the average size permitted at US data centres. Permit counts are not published fleet-wide; this is the number a fleet this size normally carries.

Hardware reused or recycled
—
No hardware reuse or e-waste figure published.
Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.

Share of retired servers and parts given a second life.

Mineral depletion
—
Only Mistral has published a mineral-depletion figure.
Not disclosedNot disclosed. The company publishes nothing on this and no defensible estimate exists.

Kilograms of antimony-equivalent, the life-cycle unit for scarce minerals consumed.

Score breakdown

Each pillar is 0–100; weights are fixed across the index

Energy efficiency — How much electricity one answer costs.weight 20% · confidence D
40
Carbon intensity — How clean that electricity is, and whether total emissions are rising.weight 25% · confidence D
50
  • Hosted on AWS and upstream model providers; inherits their claims only. Amazon
Water — How much water the data centres consume, and whether it is replaced.weight 15% · confidence D
50
  • No data; AWS fleet WUE of 0.15 L/kWh applies to its own infrastructure share. Amazon
Transparency — How much the company actually publishes.weight 20% · confidence D
5
  • No environmental disclosure of any kind. Amazon
Commitments — Whether it has dated, measurable climate and water targets.weight 10% · confidence C
15
Conduct — How it behaves: permits, lawsuits, gas turbines, community water.weight 10% · confidence C
60
  • No environmental enforcement actions on record. Amazon

Models

What this company ships, and what each prompt costs

ModelWeightsEnergy / promptWaterTraining CO₂e
Perplexity Sonar (search answer)
Llama 3.3 70B fine-tune + retrieval
Closed0.80 Wh Estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.0.120 mL Estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.500 t Estimateecogpt.co estimate. Our own estimate from published factors. The working is shown next to the number; treat it as an order of magnitude, not a measurement.

Where the compute lives

Named sites from company disclosures and press reporting

OperationalConstructionPlanned
  • AWS regionsUSA