Private AI Infrastructure

Open-Weight Model Deployment

We deploy and operate open-weight models in infrastructure you control.

For teams moving beyond managed APIs, we benchmark the model, serving runtime, precision, and accelerator against the real workload—then ship a secure OpenAI-compatible endpoint with cold-start, cache, observability, rollout, and cost controls. The application stays portable across vLLM, SGLang, TGI, and future backends.

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Production blueprint

From deployment decision to an operable inference service

Decide with evidence, benchmark the full serving combination, then deploy one portable interface with explicit controls for latency, reliability, security, and unit economics.

  1. 01

    Workload & Control Boundary

    Prove that customer-controlled inference is the right tradeoff

    We profile data sensitivity, residency, latency targets, traffic shape, context length, availability needs, model licensing, and team ownership. The output is an explicit managed-API versus open-weight decision—not a predetermined self-hosting recommendation.

    Control requirementsTraffic profileLicense review
  2. 02

    Model & Runtime Benchmark

    Measure quality, latency, throughput, and cost together

    Candidate open-weight releases—from DeepSeek, MiniMax, Qwen, Llama-family, or domain-tuned checkpoints—are tested with suitable serving backends such as vLLM, SGLang, and TGI. We compare task quality, time to first token, throughput, tail latency, and cost under representative concurrency.

    Quality evaluationsLoad testingCost per workload
  3. 03

    Artifact & Supply Chain

    Make model weights reproducible, durable, and governed

    We pin model revisions, validate provenance and licenses, choose an appropriate precision or quantization, package the serving image, and place weights in durable storage close to compute. Startup no longer depends on repeatedly downloading a moving checkpoint from the public internet.

    Pinned revisionsQuantization choicePersistent weights
  4. 04

    Portable Serving Layer

    Expose one stable contract while the backend stays replaceable

    The selected runtime is configured for the target GPU topology, context window, batching, memory headroom, and prefix caching. Applications call a secured OpenAI-compatible API, so changing the backend or model is primarily a deployment decision rather than an application rewrite.

    OpenAI-compatible APIKV-cache tuningBackend portability
  5. 05

    Reliable & Secure Operations

    Design cold starts, failures, and releases as product behavior

    We add private networking, authentication, readiness checks, warm-capacity policy, structured telemetry, controlled rollouts, rollback paths, and managed-API fallback where appropriate. Cold starts and cache misses become observable operating states, not surprises for users.

    Warm capacitySafe rolloutFallback paths
  6. 06

    Scale & Unit Economics

    Keep service quality and GPU spend sustainable at volume

    Concurrency tests establish safe capacity, autoscaling bounds, and service targets before launch. Production dashboards track time to first token, decode speed, queueing, cache behavior, GPU utilization, failures, and cost per successful workload so scaling decisions stay evidence-based.

    Capacity modelServing SLOsCost controls

Delivery foundation: Backend-neutral inference blueprint

We treat the model, runtime, precision, and hardware as measured variables—not dogma. The delivered interface stays OpenAI-compatible, supported by reproducible benchmark evidence and an operational runbook your team can own.

Put the right open-weight model into production.

Tell us your workload, control boundary, and expected traffic. We’ll benchmark the options and scope a production-ready deployment your team can operate.

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