Put AI features into production with predictable cost and control
A prototype that calls a model is easy; a production AI service that is fast, affordable and safe with your data is not. DoneDeploy helps you choose between hosted models and self-hosted open models, then designs and builds the serving, retrieval and monitoring around them. You get AI features that run reliably, with costs you can see and data that stays where you decide.
From design to implementation, and everything after:
What goes wrong without it
Costs that surprise you
Token or GPU bills grow faster than usage because nobody measured cost per feature.
Data in the wrong place
Sensitive data is sent to third-party APIs without anyone deciding whether that is acceptable.
Prototype in production
A demo became a product without retries, limits, evaluation or monitoring.
What we do
- Compare hosted models such as Claude, GPT and Gemini with open models such as Llama, Mistral and Qwen for your use case
- Serve open models on GPUs with vLLM, TGI or Ollama on Kubernetes when self-hosting makes sense
- Build retrieval with pgvector and your existing data
- Add caching, rate limits, fallbacks between models and evaluation tests
- Monitor latency, quality, token use and cost per feature with OpenTelemetry
- Document where data goes and who can access it
What you get
- An architecture and model choice document with the trade-offs
- Serving and retrieval infrastructure as code in your Git
- Dashboards for latency, errors and cost
- An evaluation suite and runbooks for model updates
From first call to continuous improvement
Free first consultation
We learn what the AI feature should do, what data it needs and your constraints.
Design
We compare models and hosting options against your quality, cost and data needs.
Fixed-price proposal
We agree the architecture, scope and price.
Build
Engineers build serving, retrieval, evaluation and monitoring, and test with your real cases.
Improve after launch
We review quality, cost and new models regularly and suggest changes.
Claude
Llama
Qwen
vLLM
Kubernetes
NVIDIA GPUs
pgvector
OpenTelemetryRelated articles
From Idea to AI Feature in Six WeeksHow an AI project runs end to end in six weeks: discovery, design, prototype, infrastructure, rollout, then measuring and improving.
CI/CD for Models and PromptsEvaluation gates, model registries, canary releases and rollback: applying CI/CD discipline to prompts and models in LLM applications.
Fine-Tuning or Prompting? A Decision Guide for Small TeamsWhen prompting, RAG or fine-tuning is the right way to adapt a language model, and what each one costs a small engineering team.Often done together
Frequently asked
Should we self-host a model or use an API?
APIs are quicker to start with and need no GPUs; self-hosting gives more control over data and can be cheaper at steady, high volume. We work out which fits your case.
Can our data stay in Ireland or the EU?
Yes, by choosing EU regions for hosted services or self-hosting open models in your own cloud account. We document where data flows.
How do we know the AI gives good answers?
We build an evaluation set from your real cases and run it on every change of model, prompt or data.
Tell us what you run and what you want to change. We reply personally, and the first consultation is free.
Let's talk »
