Privately Deployed Unified Gateway

⚠️ Pending Update · 2026-08-29 Verification · Content may be outdated, please refer to official docs Updated: 2026-08-29 · Status: Pending Verification

Background

In healthcare, finance, and government scenarios, data cannot leave the intranet; offline or edge devices (satcom, factory floors) have no public-internet access at all. Deploying open-source models locally via Ollama or vLLM, then registering them as "internal providers" alongside cloud free models in the unified gateway, satisfies both compliance and offline degradation requirements.

Core Architecture

• Hybrid provider pool: local ollama/llama3 and cloud openrouter/deepseek both sit in the routing table. The gateway routes by data-sensitivity tag — sensitive requests are forced to the local pool.
• Local provider onboarding: Ollama exposes http://localhost:11434/v1 and vLLM exposes http://localhost:8000/v1; both are natively OpenAI-compatible, so onboarding needs zero code changes.
• Offline degradation chain: cloud-primary → local-fallback → cache-only. When the internet is up, prefer cloud free; when offline, switch to local; when local cannot run either, return cached responses.
• Resource governance: the local GPU is scarce — cap concurrency (e.g. 2 concurrent) and spill to cloud after a 30s queue.

Code Example

PROVIDERS = {
    "local": {"base": "http://localhost:11434/v1", "key": "ollama",
              "sensitive_only": True, "max_concurrent": 2},
    "cloud": {"base": "https://openrouter.ai/api/v1", "key": "sk-or-..."},
}

sem = asyncio.Semaphore(PROVIDERS["local"]["max_concurrent"])

async def route(req, is_sensitive):
    if is_sensitive:
        async with sem:
            return await call(PROVIDERS["local"], req)
    try:
        return await call(PROVIDERS["cloud"], req)
    except (NetworkError, TimeoutError):
        # offline fallback to local
        async with sem:
            return await call(PROVIDERS["local"], req)

Model Version Pinning

Locally deployed models must be explicitly version-pinned. Ollama pulls latest by default, but llama3:latest last week and this week may be different fine-tunes, causing production drift. Production must use full tags like llama3:8b-instruct-q5_k_M. Maintain a "model manifest" YAML recording each local model's exact version, quantization method, and context length; changes go through PR review — never let "it worked yesterday, the model changed itself today" happen.

Best Practices

• Local model selection: use a 7B model for inference tasks (runs on a single local GPU) and a 70B model (multiple GPUs or quantized) for complex tasks; route by model_size.
• Data redaction: even when traffic stays local, do not log plaintext prompts — log leakage remains a risk.
• Resource monitoring: track local GPU utilization, VRAM, and queue depth in the gateway metrics dashboard so the local provider does not become an invisible bottleneck.
• Canary: a newly deployed local provider first absorbs 1% of traffic, with success rate and latency under observation, before ramping up.
• Cold-start warm-up: a local large model needs 30s+ on first load; the gateway pings it on startup to pre-warm so the first real request is not stuck behind a cold start.

Private deployment is not "local only" — it is "sensitive local, ordinary cloud, gateway as conductor."

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