# How to track LLM cost for Ollama and local models > A local model sends no bill, so its cost is invisible rather than absent. How to price Ollama traffic against what the same tokens would have cost. URL: https://getculpa.com/track-llm-cost-ollama Last reviewed: 2026-08-02 ## Answer A local model running under Ollama produces no invoice, so its spend is invisible rather than zero. The useful number is what the same token volume would have cost on a hosted model, because that's what running it yourself is worth. Culpa, a local-first LLM cost, margin, and forecast ledger, counts local calls through the same route as any provider and prices that comparison. ## Why this happens Self-hosting moves cost from a line item to a fixed asset, and fixed costs get forgotten. The tokens still exist, the machine still runs, and the electricity and the hardware are still real, but no invoice arrives to make any of it visible. That's fine until somebody asks whether the local deployment is worth keeping, at which point nobody can answer, because the comparison needs a token count nobody was recording and a hosted rate nobody applied to it. ## What this usually looks like - Local model traffic is uncounted because no invoice ever prompted anyone to count it. - Nobody can say whether self-hosting is cheaper than the hosted equivalent for your actual volume. - The decision to self-host was made once, at a different volume, and never revisited. - Hosted and local traffic sit in separate mental buckets and never get compared. ## Common mistakes - Treating a local model as free because it sends no bill. Why it hurts: The hardware, the power and the operational time are real, and none of them are on a rate card. Do instead: Count the tokens anyway, then price them against a hosted equivalent to get a comparable number. - Leaving local traffic out of your ledger entirely. Why it hurts: Your call volume and your feature attribution both go wrong, not just your spend. Do instead: Route local models through the same capture path as everything else and label them as local. - Deciding self-hosting once and never re-testing it. Why it hurts: The answer is a function of volume, and volume moves while the decision stays put. Do instead: Recompute the comparison quarterly, or whenever your local volume changes materially. ## Self-check - Count input and output tokens through your local model for a full month. - Pick the closest hosted equivalent and price the same volume at its published rates. - That figure is what self-hosting saved you in provider spend this month. - Put your real hardware, power and operational costs beside it and compare honestly. - Re-run the comparison whenever local volume changes by a large factor. ## What a month of local inference would have cost hosted (illustrative) A local Llama 3.3 70B under Ollama handling 60 million input and 12 million output tokens a month, priced against the hosted equivalent on Groq at real rates of $0.59 per million input and $0.79 output. Volumes are modelled. Provider invoice for the local model: $0.00, because nobody sends one The same volume hosted: (60 x $0.59) + (12 x $0.79) = $35.40 + $9.48 = $44.88 So self-hosting avoided $44.88 of provider spend this month At ten times the volume it would avoid $448.80, and the hardware cost wouldn't necessarily change Below roughly this volume the hosted route is cheaper than almost any dedicated machine Forty-five dollars a month is the honest value of this deployment, and it's a number worth knowing before anyone defends the machine it runs on. The comparison only exists if somebody counted the tokens. ## Cost figures Every figure carries its confidence and its source. No figure on this site is provider-reported. - $44.88 — modelled monthly hosted-equivalent value of a local Llama 3.3 70B deployment [calculated] Source: (60 x $0.59) + (12 x $0.79) per million, using real Groq Llama 3.3 70B rates from the price book, effective 2026-07-02 and re-verified 2026-08-01. Token volumes are modelled. - $0.00 — provider invoice for local inference, which is why the counterfactual is the only comparable number [calculated] Source: No provider bills a model running on your own hardware, so the invoice line is zero by construction. ## FAQ Q: Can Culpa track a local model like Ollama? A: Yes. Local models expose an OpenAI-compatible endpoint, so they route through the same capture path as a hosted provider, with the local server as the upstream. The calls land in the same ledger and carry the same labels. Q: What does cost mean for a model with no invoice? A: The most useful definition is the counterfactual: what the same tokens would have cost on the closest hosted model. That converts an invisible fixed cost into a number you can compare against your hardware and your power bill. Q: Is self-hosting cheaper? A: It depends entirely on volume, and the answer moves as volume does. Price your real monthly tokens against a hosted equivalent and put that beside your actual hardware and operational costs. At low volume the hosted route usually wins. ## Sources - Groq pricing: https://groq.com/pricing Run the free Cost Leak Scan: https://app.getculpa.com/scan?source=pseo&slug=track-llm-cost-ollama&cluster=integration Machine-readable index of every guide: https://getculpa.com/api/pages Human-readable index of every guide: https://getculpa.com/guides Site overview: https://app.getculpa.com/llms.txt Privacy: Culpa runs on your infrastructure. Your prompts and responses never leave it. Culpa counts calls to run your plan, and it fails open, so if it ever breaks your app keeps running.