LLM hosting decisions should follow the workload, not the hype cycle.
Dan advises on hosted APIs, local inference, private deployment, hybrid routing, OpenAI-compatible gateways, model selection, request logging, token metrics, latency, cost, and production reliability.
Inference strategy
Pick the right model path for privacy, cost, latency, and product quality.
AI teams often jump between hosted APIs, local models, private cloud, and open-source inference without a stable decision framework. Dan helps companies evaluate model behavior, context needs, tool-calling needs, data sensitivity, operating cost, latency, throughput, and fallback strategy.
The goal is not to self-host everything. The goal is to know which workloads belong on hosted frontier models, which can move to private or local inference, and how to observe the system well enough to make that decision with evidence.
Advisory scope
- Hosted, private, local, and hybrid inference architecture.
- OpenAI-compatible gateway and routing design.
- Request, response, tool-call, latency, cost, and token observability.
- Model selection and upgrade planning for real product workloads.
- RAG, context-window, prompt, and structured-output considerations.
- Evaluation practices for usefulness, reliability, and regression detection.
Useful for
- Companies moving from prototype to production AI features.
- Teams worried about API cost, throughput, or data sensitivity.
- Enterprises considering local model serving for internal workflows.
- Products that need provider failover or model-specific routing.
- Engineering teams that need prompt, completion, and tool-call traces to debug AI behavior.