SaaS & Technology

Move from API to self-hosted model

Cut inference cost and keep data private with a fine-tuned open model.

Discuss this use case

How we approach it

  1. Collect production traces as training data
  2. Distil into a fine-tuned open model
  3. Serve with vLLM on autoscaling GPUs behind your gateway

How success is measured

Quality parity on evals, cost per 1k requests, p95 latency.

Related

More use cases.

Retail & E-commerce Product content & imagery at scale Generate descriptions, attributes and lifestyle images for thousands of SKUs. Image & VisionFine-tuningLLM Apps & Copilots Open → Manufacturing & Logistics Visual defect detection on the line Detect defects consistently from camera images at production speed. Image & VisionMLOps / LLMOps Open → Legal & Professional Services Contract review & clause comparison Compare incoming contracts against your playbook in minutes. RAG & SearchFine-tuningEvals & Guardrails Open →

Next step

Let's talk about your AI system.

A free 30-minute call with an Engagement Lead or AI Architect. You'll leave with a clearer view of options, risks and cost, whether or not we work together. Your case doesn't need to fit any box on this site; just tell us what you're facing.

Book a free call Find your starting point

Keep exploring

There’s more to see.

Up next · How we work Start small. Prove value. Scale. Three engagement models, a measure-first approach and a 3-click starting-point finder. About Hands-on engineering, not slideware Who we are and the principles we work by. Blog Notes from the field In-depth articles on architecture, production lessons and research. Services Everything between the idea and the SLA Agents, MCP servers, RAG, fine-tuning, voice, vision and governance, 19 hands-on capabilities. Platforms & Cloud The heavy lifting behind AI Data & AI platforms, landing zones, accelerators and migrations between cloud AI providers.