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.