Every other provider prices a static stack. Ours is rewritten by an agent that forms one hypothesis at a time, measures it against a frozen quality gate, and reverts anything that does not win. Fourteen days in, the same model costs a tenth of what it did on day one.
The gate is public. So is every kept commit.
The agent proposes a change to the kernel, the scheduler, or the quantisation scheme. It runs the eval suite once. If quality drops below the frozen threshold, the change is reverted and the hypothesis is recorded as dead.
Nothing ships on a hunch. Roughly one in ten attempts survives.
Prices below are live and move down, never up. The rightmost column is what the identical workload cost on the cheapest alternative this week.
Weights stay resident in a shared page cache and the runtime snapshots after warm-up, so a cold container resumes rather than boots. Every stage below is measured on every cold request and published.
The API is OpenAI-compatible, including streaming, tool calls, structured outputs, and the batch endpoint. Existing SDKs work unmodified.
Full API reference →from openai import OpenAI
client = OpenAI(
base_url="https://api.boole.dev/v1",
api_key=os.environ["BOOLE_API_KEY"],
)
resp = client.chat.completions.create(
model="llama-3.3-70b-instruct",
messages=[
"role": "user", "content": "Summarise this ticket.",
],
stream=True,
)No card, no sales call. If the numbers on this page do not hold for your workload, the log will tell you why.