Your cloud, built and run with AI.
We design, build and operate your infrastructure. AI drafts and watches; our engineers approve every change.
- Fixed price
- Your cloud, your Git
- Based in Ireland
Built on
AWS
Google Cloud
Azure
Kubernetes
Terraform
Argo CD
GitHub Actions
Prometheus
Grafana
OpenTelemetry
vLLM
Claude
OpenAI
Llama
Cloudflare
PostgreSQLFrom design to implementation, and everything after
We design it with you, build it, run it, automate it and keep improving it, with AI beside our engineers at every step.
Cloud and infrastructure, run with AI
Our core. AWS, Google Cloud and Azure, Kubernetes and infrastructure as code, built by engineers and operated with AI in the loop.
- Terraform and Kubernetes written, reviewed and kept in Git
- Cost and capacity advice from your real usage
- Runbooks that fix routine incidents on their own
AI observability
Metrics, logs and traces with AI reading them first, so your team hears about a problem before your customers do.
- Anomaly detection on the signals that matter
- Alert noise grouped into real incidents
- Root-cause hints and plain summaries on call
AI service design
Services and architectures designed with AI as a working partner: options, failure modes and costs explored before anyone writes code.
- From requirements to architecture in days
- Reviewed for security, cost and scale
- Documentation your team can own
Automation
Everything that can run without a person does: CI/CD and GitOps for your software, and AI that takes repetitive work off your team.
- Pipelines from commit to production
- Support, documents and reporting handled by AI
- People in the loop where it matters
Continuous improvement
Launch is the start. We keep measuring cost, speed, reliability and security, and improve them month after month.
- Cloud bills reviewed and cut where they can be
- Performance and reliability tracked against goals
- Security and upgrades kept current
AI platforms and LLM infrastructure
Open models on your own GPUs or hosted models behind one gateway, served, scaled and watched like any production service.
- vLLM, Ollama and TGI on Kubernetes GPUs
- Retrieval (RAG) on your own data
- Tokens, latency and cost tracked per request
The models, and the infrastructure that serves them
Open models on your own GPUs, hosted models behind an API, or both behind one gateway. We choose by quality, cost, privacy and latency, then run it like any other production service.
Models we work with
Open weights you can run in your own cloud account, and the leading hosted models.
We serve, scale and watch them
Inference servers, GPUs, retrieval and monitoring: what decides whether AI is fast, affordable and safe.
The stack we build on
Docker
Helm
Amazon ECS
Pulumi
Packer
Vault
Ansible
Puppet
Linux
NGINXCI/CD and GitOps
From commit to production without a ticket queue.
GitLab CI
JenkinsObservability
Metrics, logs and traces, with AI reading them first.
Datadog
Sentry
MySQL
MongoDB
Redis
ElasticsearchFrom idea to a system that keeps getting better
Idea
We find where infrastructure, AI and automation save real time or money, and agree how success is measured.
Design
Architecture, data and the human checkpoints, explored with AI and reviewed by engineers.
Build
Infrastructure as code and working software, with tests and evaluations from day one.
Run
Deployed with CI/CD to your cloud, secured, and watched by AI observability.
Automate
Routine operations and repetitive business work handed to pipelines, runbooks and AI.
Improve
Cost, speed, reliability and quality reviewed and improved, month after month.
Why teams choose us
Infrastructure engineers first
We are an infrastructure company that uses AI, not an AI agency guessing at production. Every change is engineered and reviewed.
Fixed price, free first call
A free consultation, then a fixed-price proposal with a clear scope, so you know the cost before work starts.
Yours, not ours
Everything lives in your cloud account and your Git: code, dashboards, runbooks and documentation. No lock-in.
We run what we sell
Our own products run on the same cloud, AI and automation we build for clients.
Our own products, on the same cloud we build for you
Four live services, built, deployed and watched with the same infrastructure, automation and AI we offer.
EasyEnvTechnical hiring in real, production-like environments.See how we built it →
GP FinderWhich GPs in Ireland are taking new patients.See how we built it →
FairPriceWhat things really cost in Ireland, compared every morning.See how we built it →
TechHomeSmart technology installed properly in Irish homes.See how we built it →Latest 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.Frequently asked
What does DoneDeploy do?
We design, build and run cloud infrastructure for software companies, with AI in the loop: AI-assisted service design, infrastructure as code on AWS, Google Cloud and Azure, Kubernetes, CI/CD, AI observability, and AI that automates business processes.
How do you use AI in infrastructure and cloud work?
AI drafts and reviews Terraform and Kubernetes changes, reads metrics and logs to spot anomalies and group alerts into incidents, recommends cost and capacity changes from real usage, and runs automated runbooks. Engineers stay in charge of every change that reaches production.
What is AI observability?
Observability with AI on top: the usual metrics, logs and traces, plus models that detect unusual behaviour, cut alert noise, suggest likely root causes and summarise incidents in plain language. For LLM applications we also track tokens, latency, cost and answer quality.
Can you run open models like Llama, Mistral or Qwen on our own servers?
Yes. We deploy open models with vLLM, Ollama or TGI on Kubernetes GPU nodes in your cloud account or on your own hardware, so data never leaves your environment, and route to hosted models such as GPT, Claude or Gemini where they are the better fit.
How do we start using AI in our company?
Start with the work, not the tool: we look at where your team loses time, pick one or two processes where AI clearly helps, run a short pilot on your own data with clear success measures, and set simple rules on data and tools before rolling it out. Our Getting started with AI service covers exactly this.
What should we automate first?
The work that is frequent, repetitive and rule-based: deployments and environment setup on the engineering side, and on the business side things like triaging requests, extracting data from documents and preparing reports. We map the process first, remove the steps that should not exist, then automate what is left.
How can AI improve our business processes?
AI is good at reading, sorting, summarising and drafting: support requests, documents, emails, reports and logs. Put into a process with people checking the important decisions, it removes hours of manual work while keeping quality and an audit trail. We measure the process before and after so the improvement is real.
Do you work with startups and small teams?
Yes, most of our clients are small and mid-size teams. We work on fixed-price packages with a free first consultation, so you know the cost before work starts.
Where are you based?
DoneDeploy is based in Ireland and works with teams across Ireland, the UK and Europe, remotely.
