Learn / How to deploy a fine-tuned LLM in production
How to deploy a fine-tuned LLM in production
Deploying a fine-tuned LLM means taking a model you've adapted to your own data and serving it in production behind a reliable, low-latency API. The main decisions are where to run it (hosted, VPC, or on-prem), how to expose it (ideally an OpenAI-compatible API), and how to keep latency and throughput acceptable under real load.
The steps
- Fine-tune or train the model on your own data until it meets your quality bar.
- Choose a deployment target: hosted, your own VPC, on-prem, or self-hosted, based on your control, cost, and compliance needs.
- Serve the model behind an OpenAI-compatible API so existing clients and agent frameworks can call it without rewrites.
- Load-test for latency and throughput at realistic concurrency, not just a single request.
- Monitor quality, latency, and cost, and scale capacity as traffic grows.
What to watch for
The most common surprise is performance under concurrency: a model that feels fast for one user can slow down sharply when many users arrive at once. Watch time-to-first-token and per-token latency at your real load, keep an eye on GPU memory for long contexts, and weigh cost against the ownership and compliance benefits of running on your own infrastructure.
How Netra helps
Netra Runtime supports fine-tuning and training on your own data, then deploying that model on infrastructure you own — on-prem, in a VPC, self-hosted, or hosted — through an OpenAI-compatible API. That covers the whole path from a customized model to a production endpoint. Learn what Netra Runtime is or see pricing.
Frequently asked questions
Where can I deploy a fine-tuned model?
On Netra Runtime you can deploy a fine-tuned model on-prem, in your own VPC, self-hosted, or hosted, so you keep ownership of the model and the data it runs on.
Can I serve a fine-tuned model behind an OpenAI-compatible API?
Yes. Netra Runtime exposes an OpenAI-compatible API, so a fine-tuned model can be called by existing OpenAI-style clients with only a base URL and key change.
Do I keep ownership of my fine-tuned model?
Yes. Netra Runtime is built around owning your models and infrastructure, including models you fine-tune or train on your own data.