A drop-in OpenAI-compatible endpoint.
Deploy any trained model or RAG pipeline as an OpenAI-compatible API — base URL, key, model name, usage dashboard, logs, latency and token metering included.
Swap one line of code
Change the base URL and key. Your existing OpenAI client keeps working — no new SDK, no rewrite.
from openai import OpenAI
client = OpenAI(
base_url="https://api.neva.ai/v1",
api_key="sk-neva-prod-a91f…",
)
resp = client.chat.completions.create(
model="acme-support-v3",
messages=[{"role": "user",
"content": "How do I reset my device?"}],
)
print(resp.choices[0].message.content)From dev workloads to private clusters
The inference layer is the recurring product — start shared, grow into dedicated and reserved capacity.
Small customers and development workloads. Pay per token.
Production and business users. Isolated endpoint, higher limits.
Heavy users with predictable demand. Committed capacity.
Support, SLA, audit logs, and custom terms.
Keys, logs, latency and billing in one place
API keys & scopes
Create, rotate and scope keys per environment.
Token metering
Per-request token counts with included allowance and overage.
Request logs
Inspect every call with latency and status.
Rate limits
Per-key RPS and burst controls.
Audit logs
Enterprise-grade access trails and DPAs.
Billing controls
Monthly minimums, prepaid credits, invoicing.
Deploy once. Bill per token.
Recurring inference is the platform's cash cow — sticky, predictable, and the natural upgrade path from a first RAG project.