When local wins
- Data residency or confidentiality blocks cloud vendors.
- Ultra-low latency for on-prem apps.
- Cost control for steady, predictable workloads.
Architecture we deploy
Model hosting: Llama variants with quantization for throughput.
Retrieval: On-prem vector store (pgvector/Weaviate) with access controls.
Gateway: Auth, rate limits, and routing between local and cloud.
Observability: Traces, logs, and cost/latency dashboards.
Security and guardrails
- Network isolation and per-service credentials.
- Redaction and filtering before inference.
- Approvals for writes, payments, and PII actions.
- Drift monitoring and regression evals.
Deployment steps
- Assess data, latency, and cost targets; pick model size.
- Stand up hardware; containerize model + gateway.
- Implement retrieval and logging; run benchmarks.
- Pilot with one workflow; expand via hybrid routing.
Local LLMs are viable - when scoped, secured, and paired with clear routing to cloud models when needed.
FAQ
Are local models accurate enough?
For many workflows, yes - especially with retrieval and tuning. Use cloud only for high-complexity reasoning.
How do we update models?
Versioned deployments with rollback; retest using regression evals before promotion.
What about cost?
Predictable for steady workloads; hybrid routing keeps costs optimized.
Do we lose features?
Some; mitigate with plugins/tools and a gateway that supports both local and cloud routes.