● GATEWAY · IN PRODUCTION

Enterprise AI gateway and multi-model routing

An OpenAI-compatible API. Change one line of base_url and both model ecosystems are yours.
Every call is on the record: metered, billed, governed.

# Zero changes to existing code. One line.
client = OpenAI(
  base_url="https://api.tokenpower.ai/v1",
  api_key=os.environ["TOKENPOWER_API_KEY"]
)
resp = client.chat.completions.create(
  model="auto", # or pick one: deepseek / qwen / claude / nova …
  messages=[…]
)

FOUR JOBS · ONE AUDIT CHAIN

Multi-model routing

Two ecosystems, one catalog. Routing by cost, latency or capability, with automatic fallback. No vendor lock-in.

Full audit chain

Every call records the model, tokens, cost, latency, caller and policy hits. The audit is the product, not a log file.

Policy and rate limits

Permissions, quotas and content policy are enforced at the gateway. Governance does not rely on the model behaving — it relies on the gateway.

Cost and quota

Billing split by team and project. Budget caps and alerts. Metering down to the token.

MODEL CATALOG

VolcengineQwenDeepSeekZhipuMoonshot NovaClaudeLlama…

International catalog served as an authorized AWS reseller (Bedrock).

WHERE IT SITS

GATEWAY is the base of the four-layer platform

Every delivery engagement's ledger starts from this layer's audit chain. The gateway already runs in production on real traffic — which is why we can afford to charge on results.

See the four layers →

DEPLOY · Critical-environment deployment

AGENTS · Trusted execution

GRAPH · Business-object graph

GATEWAY · Model access & governance ● in production

Above the API sits a full delivery service

The gateway handles access and governance. Forward-deployed engineers turn AI into results you can read off a ledger.

Book a Bootcamp Read the whitepaper →