Point of view
AI & Generative AI
MLOps & LLMOps
Pipelines, evaluation harnesses, monitoring and cost controls that keep models accurate and affordable over time.
Overview
A model that works on launch day can degrade quietly as data, users and upstream systems change. MLOps and LLMOps provide the engineering discipline to keep AI solutions accurate, secure and affordable once they are in production.
For machine learning, we put in place versioned data and features, reproducible training pipelines, a model registry, automated deployment and monitoring for performance and drift. For generative AI, we add prompt and configuration versioning, evaluation harnesses that run on every change, tracing of model and tool calls, and guardrails for safety and data protection.
Cost is treated as a first-class metric. We instrument token usage and infrastructure spend, then apply caching, model routing and right-sizing so costs stay predictable as adoption grows. These practices are designed to fit your existing DevOps tooling and platform team, so AI is released and operated like any other well-run software.
What you receive
- 01MLOps and LLMOps reference architecture and standards
- 02CI/CD pipelines for training, evaluation and deployment
- 03Model and prompt registry under version control
- 04Monitoring dashboards for quality, drift, latency and cost
- 05Runbooks and on-call guidance for AI incidents
Technologies we work with
- MLflow
- Databricks
- Amazon SageMaker
- Azure Machine Learning
- Kubernetes
- GitHub Actions
- LangSmith
- Langfuse
We are technology-agnostic: tools are chosen to fit your requirements and existing investments.
Questions we often hear
Do we need a dedicated MLOps platform?
Not always. Many organizations get most of the benefit from the cloud and data platforms they already run, plus a few targeted tools for evaluation and tracing. We recommend the lightest setup that meets your scale and control requirements.
How do you keep generative AI costs under control?
We measure cost per request and per user, then apply techniques such as prompt optimization, response caching, routing simpler requests to smaller models and usage budgets. Cost dashboards make spend visible to the teams that drive it.
Insights
Related thinking
Let’s build what’s next, together.
Tell us about your goals. We’ll connect you with the right experts, usually within one business day.