Service · Open Source AI
You used to pay OpenAI. Now you run an open model and keep the difference.
Today's open-source models handle most real-world use cases — chatbots, classification, extraction, RAG, agents — at a fraction of GPT or Claude prices. I do the full migration: evals on your data, deployment, and integration without touching your code.
The case, in numbers
- 280× drop in GPT-3.5-level inference cost in just two years (Nov 2022 → Oct 2024): from $20 to $0.07 per million tokens. Stanford HAI · AI Index 2025
- 60% of 700+ surveyed tech leaders report lower implementation costs with open source AI than with proprietary tools. McKinsey · Mozilla (2025)
- 2/3 of organizations say open source AI is cheaper to deploy than proprietary models. Linux Foundation · Meta (2025)
Price per million tokens: closed vs. open
Official API prices (USD per 1M output tokens). The short bars are the point: the difference is not percentages, it is orders of magnitude.
Price per million tokens: closed vs. open — input + output
| Model | USD/1M (input) | USD/1M (output) | Source |
|---|---|---|---|
| GPT-5.6 (sol) · OpenAI | $5.00 | $30.00 | ↗ |
| Claude Opus 4.8 · Anthropic | $5.00 | $25.00 | ↗ |
| Gemini 3.1 Pro · Google (≤200k) | $2.00 | $12.00 | ↗ |
| Claude Haiku 4.5 · Anthropic | $1.00 | $5.00 | ↗ |
| Kimi K2.6 · Together AI | $1.20 | $4.50 | ↗ |
| DeepSeek V4 Pro · API DeepSeek | $0.43 | $0.87 | ↗ |
| Llama 3.3 70B · Groq | $0.59 | $0.79 | ↗ |
| GPT-OSS-120B · Groq | $0.15 | $0.60 | ↗ |
| DeepSeek V4 Flash · API DeepSeek | $0.14 | $0.28 | ↗ |
What it means for your bill
A product processing 50M input tokens and 10M output tokens per month would pay, per model:
| Model | Monthly bill | vs. most expensive |
|---|---|---|
| GPT-5.6 (sol) · OpenAI | $550 | — |
| Claude Opus 4.8 · Anthropic | $500 | −9% |
| Gemini 3.1 Pro · Google (≤200k) | $220 | −60% |
| Kimi K2.6 · Together AI | $105 | −81% |
| Claude Haiku 4.5 · Anthropic | $100 | −82% |
| Llama 3.3 70B · Groq | $37.40 | −93% |
| DeepSeek V4 Pro · API DeepSeek | $30.45 | −94% |
| GPT-OSS-120B · Groq | $13.50 | −98% |
| DeepSeek V4 Flash · API DeepSeek | $9.80 | −98% |
Real cases, with sources
No promises: companies and teams that published their numbers after moving to open models. Every quote links to its source.
- 10x cheaper
Lindy
The AI-assistant startup migrated 100% of its traffic from Anthropic to DeepSeek. Its Anthropic bill exceeded payroll for its 25+ employees; the switch saved millions of dollars.
“It was just 10x cheaper… So it was a very, very simple business decision. — Flo Crivello, CEO”
Source: NPR (jul 2026) ↗ - $7,000 → $800/mo
Checkr
Replaced GPT-4 with a fine-tuned Llama-3-8B for background-check classification: from ~$7,000/mo to ~$800/mo, with better accuracy on hard cases and ~30x faster responses.
“With the SLM, it costs about $800 a month. — Vlad Bukhin, Staff ML Engineer”
Source: Computerworld ↗ - Qwen in production
Airbnb
Its customer-service agent relies mostly on Qwen (Alibaba’s open model) instead of OpenAI, for speed and cost, per CEO Brian Chesky.
“We're relying a lot on Alibaba's Qwen model. It's very good. It's also fast and cheap. — Brian Chesky, CEO”
Source: SCMP / Bloomberg ↗ - up to 100x
Cresta
The contact-center AI company serves fine-tuned open models (Mistral base) with per-customer LoRA adapters: up to 100x lower cost than GPT-4, while outperforming it on its RAG tasks.
Source: Fireworks AI ↗ - 10x · +8% F1
Convirza
Call analytics with 60 LoRA adapters on a single Llama-3-8B: 10x lower operating cost than OpenAI, with 8% better average F1 and 80% higher throughput.
Source: ZenML LLMOps DB ↗ - up to 10x
Argentine government
MIA, the national AI agent integrated with the TINA chatbot (~1.5 million conversations/month), runs on Meta's Llama, chosen for speed and savings of up to 10x versus other high-performance models.
Source: BNamericas ↗
How I work
- 01
Usage audit
I analyze your current OpenAI/Claude spend: endpoints, token volume, tasks. That yields your real potential savings — not the marketing number.
- 02
Evals on your data
I test 2-3 candidate open models against your real cases with automated evals. If an open model doesn't reach the quality you need, I tell you and we don't migrate that part.
- 03
Deployment
Depending on volume and privacy: a provider API (Groq, Together, DeepSeek) or self-hosting with vLLM in your cloud. With per-token cost numbers for each option.
- 04
Integration & monitoring
Open models speak the same API as OpenAI: switching means changing a URL and a key. I leave quality and cost monitoring in place, plus a rollback plan.
When it's NOT worth it
If your volume is tiny (a typical migration takes 2-4 engineer-weeks; a $100/month bill won't pay for it), if you depend on the very latest frontier reasoning (the best open models run months behind the top closed ones), or if you want self-hosting with nobody to operate infrastructure, staying on a proprietary API can be the right call. Part of the service is saying no when the numbers don't add up.
FAQ
Do I lose quality by moving to an open model?
It depends on the task. For chat, classification, extraction, summarization and RAG, current open models perform at proprietary level. For frontier reasoning, not always yet. That is why step 2 is evals on your data: we only migrate what keeps the quality.
Do I have to rewrite my code?
No. Open-model providers and servers like vLLM expose the same OpenAI API. In practice you change the base URL and the API key.
Self-hosting or a provider API?
At low or medium volume, a provider (DeepSeek, Groq, Together) already delivers huge savings with zero ops. Self-hosting pays off with sustained high volume or strict privacy requirements. The audit decides with numbers.
What about my data?
With self-hosting, your data never leaves your infrastructure — the preferred option in healthcare, banking and regulated data. An extra benefit of open models beyond price.
How much does the service cost?
The initial audit has a fixed price and gives you the estimated savings number before you decide anything. The migration is quoted by scope; the goal is that it pays for itself within a few months of savings.
Shall we see how much you can save?
Tell me what you use today (model, rough volume, task) and I will send back an honest savings estimate and a plan.