DevOps, MLOps and LLMOps
One operations layer for your software, models and LLMs
Complete DevOps for your applications and infrastructure, plus the MLOps and LLMOps that machine learning and LLM systems need on top: CI/CD, Kubernetes, infrastructure as code, observability, evaluation, tracing, cost ceilings and drift monitoring, on AWS, Google Cloud, Azure or your own hardware.
Technology we use for this work
The problem
Systems degrade quietly
Deployments depend on one person, infrastructure drifts from what anyone wrote down, and alerts fire without an owner. AI systems add another layer: prompts change, providers update models, data drifts and costs creep.
Without pipelines, monitoring, evaluation and budgets, the first sign of trouble is an outage, a customer complaint or an invoice. We put the operating controls in place so reliability, quality, cost and failures are visible to engineering and to finance.
What we build
What does our DevOps, MLOps and LLMOps service include?
CI/CD, Kubernetes, observability, model deployment, LLM tracing and cost control for software, ML and LLM systems.
CI/CD pipelines
GitHub Actions, GitLab CI or Jenkins with automated gates on every push: linting, type checks, tests, security scans and end-to-end runs, then GitOps deployment with Argo CD.
Infrastructure as code
Terraform, Pulumi and Ansible for reproducible environments from development to production, reviewed and versioned like application code.
Containers and Kubernetes
Docker images, Kubernetes clusters and Helm charts with autoscaling and rollback, on EKS, GKE, AKS or self-hosted clusters.
Observability and alerting
Metrics, logs and traces with Prometheus, Grafana, OpenTelemetry, Datadog and Sentry, with alerts routed to an owner and a runbook.
Security and secrets
Secrets in HashiCorp Vault or your cloud's secret manager, container scanning with Trivy and least-privilege access for every service.
Model lifecycle and serving
Experiments and a model registry in MLflow, pipelines in Kubeflow, serving with BentoML, shadow and canary releases, and drift monitoring with Evidently that triggers retraining.
LLM observability
Every model call traced in Langfuse, LangSmith or Arize, linked to the prompt version, the input and the cost, with prompts versioned outside the codebase.
Evaluation pipelines
Regression suites with DeepEval and Ragas that run on every prompt or model change and block releases that lower quality.
Cost control
Per-run model cost tracking, budget ceilings on model spend and cloud resources tagged per service, reported in terms finance can use.
Multi-cloud and regulated deployment
AWS, Google Cloud, Azure and on-premise, including deployments that cannot leave a given jurisdiction.
Proof
Where have we delivered this?
Book recommendations at scale, with a self-hosted LLM
A hybrid recommendation engine over more than 10 million titles and a goal-based recommendation chatbot on a self-hosted Llama 3 model.
Result: 60% higher click-through on recommendations
Read the case studyAn AI compliance platform for Saudi PDPL, live with banks
A platform that reviews client documents against the Saudi Personal Data Protection Law, scores the gaps and drafts compliant replacements, now in production with banks and finance companies.
Result: 95% content fidelity on AI-drafted documents
Read the case studyConversational BI for teams with no data team
Connect a spreadsheet or database and come back to a dashboard that already means something, then reshape it by asking in plain language.
Result: 100% of model calls routed through one traced gateway
Read the case studyContext
Industries and related services
Industries
Related services
FAQ
DevOps, MLOps and LLMOps: common questions
Do you provide DevOps for systems that are not AI?
Yes. We set up CI/CD, infrastructure as code, Kubernetes, observability and security for any application. MLOps and LLMOps sit on the same foundation, so one team owns the whole path from commit to production.
What does a production AI platform need that a prototype does not?
Evaluation on every change, tracing of every model call, cost tracking and ceilings, versioned prompts and models with rollback, monitoring for drift, and a runbook a team that did not build it can follow.
How do you measure quality over time?
With a regression set agreed at the start, run automatically on every change, plus sampled review of live traffic. A release that lowers the score is blocked.
Can you hand the system over to our team?
Yes, and we plan for it from the first phase. Code, infrastructure and documentation live in your accounts from day one, and the operate phase includes training your owners.
Start with a two to three week assessment.
Send us the problem and the constraints. You get a written architecture, a risk register and a costed roadmap, and you keep them whatever you decide next.