Cut your LLM bill.Pay only from what we save.
Rakuvo audits your OpenAI, Anthropic and Gemini usage, finds the waste, and proves every change on your own prompts before you switch.
- We run the analysis for you
- Read-only usage data
- No savings, no fee
Estimated savings
28–59%
levers overlap, so ranges combine
Calls analysed
10,360
synthetic logs
Levers found
5
caching, batch, size, context, repeats
Fee if nothing is saved
$0
you pay from verified savings
- Prompt caching15–25%high confidence · config change
- Model right-sizing7–33%medium · verify with replay
- Batch API for bulk jobs5–10%low · needs your OK on latency
- Oversized context3–8%low · measure quality while trimming
- Duplicate-response cache1–2%high · code change
kept waste · illustration
Reads usage exports from
The problem
LLM bills grow faster than usage, and most of the waste is fixable.
~$8.4B
Enterprise spending on LLM APIs more than doubled in six months, according to Menlo Ventures. Agent workflows make it worse: they send far more tokens per task than a chatbot.
~50%
Providers already offer big discounts for cached prompts and batch jobs that many teams never switch on. And swapping a big model for a small one saves money only if quality holds, which almost nobody checks.
How it works
Find it. Prove it. Ship it.
- 01
Export usage
Send us a read-only usage export or request logs (OpenAI, Anthropic, Gemini, LiteLLM, Langfuse, Helicone). No code access, no API keys.
- 02
Audit
We find caching, batching, model-size and context waste, and give each one a savings range with the assumptions shown.
- 03
Prove
Before any switch, we replay a sample of your own prompts on the cheaper setup and measure agreement and cost.
- 04
Ship & share savings
We help roll out the changes with a fallback. You pay a share of the savings we verify, and nothing if there are none.
What we look for
Five places LLM money leaks
Prompt caching
Long system prompts and reference documents re-sent on every call, but not billed at the cached rate.
Model right-sizing
Short classification and extraction calls running on a flagship model that a smaller one can handle.
Batch jobs
Bulk, non-urgent work billed at real-time prices when a batch API costs about half.
Oversized context
8,000+ tokens of history or retrieved text in, a two-line answer out.
Repeated prompts
The same question answered again and again when a response cache could answer it once.
Not sure which applies?
The free audit tells you, with numbers from your own logs.
Proof, not promises
A cheaper model can be 80% cheaper and still change your results.
We classified 38 support tickets with GPT-4.1, then replayed the same prompts on smaller models and measured both cost and agreement.
| Switch | Measured cost | Same label | Every field identical |
|---|---|---|---|
| GPT-4.1 → GPT-4.1 mini | ~80% cheaper | 89–92% (34–35/38, two runs) | ~70% |
| GPT-4.1 → GPT-4.1 nano | ~95% cheaper | 87% (33/38, one run) | ~70% |
The gap comes from one subjective field (urgent) that the smaller models set differently. The replay shows exactly which field diverges, so you decide before you switch. Small test: 38 AI-generated tickets, one task. Results moved between runs (34 vs 35 of 38 for mini), which is itself the point: measure on your own prompts, and re-measure. It illustrates the method; it is not a benchmark.
Pricing
You pay when you save.
Audit
Free
- Usage analysis from your logs
- Savings ranges per lever, with assumptions
- A written report you keep
Verified savings share
~20% of verified savings
- We implement and verify the fixes with you
- Typical range 15–25%, agreed up front, for 6–12 months
- Replay evidence before every change
- No verified savings, no fee
Fixed fee
Quoted after the audit
- For teams that prefer a predictable cost
- Same audit, replay and rollout help
- Scoped to the findings
Terms are written down before any work starts: how savings are measured, the baseline, the cap, and how either side can stop.
Calculator
What would pay-from-savings look like on your bill?
$500 to $500,000
An assumption for illustration. Your audit measures the real figure, which may be higher, lower or zero.
- Savings per month
- $6,690
- Rakuvo fee
- $1,338
- You keep
- $5,352
- You keep per year
- $64,224
If verified savings are $0, you pay $0.
Your data
Your data stays yours.
Done for you
We run the audit and the replay on our side from a read-only export. You install nothing and get a written report.
Least access
Read-only usage exports are enough to start. We do not need your API keys, source code or production access for the audit.
Your terms
We sign an NDA on request and delete anything you send us when the engagement ends.
Who is behind this
Built by an engineer who ships LLM systems.
Rakuvo is founded by Naman Omar, an AI/ML engineer with hands-on experience in production LLM pipelines, fine-tuning and quantization, evaluation, and guardrails, and a published researcher (six papers, IIIT Kottayam). Rakuvo is early and honest about it: that is why the first audit is free and every claim comes with evidence you can check.
FAQ
Questions teams ask
Early access
Get early access
Tell us a little about your usage. We reply within two business days.