MLOps & LLMOps
MLOps and LLMOps consulting for teams running AI in production
Most teams do not have a model problem. They have an operations problem. The model is roughly 20% of the work, and the other 80% is serving, evaluation, observability, cost control and the failure modes that only appear under real traffic. That 80% is what I build.
Who this is for
- ▹You have a working prototype and no reliable path to production
- ▹Your AI works in demos but is slow, expensive or unpredictable with real users
- ▹You have no evaluation harness, so every deploy is a guess
- ▹Inference cost is growing faster than revenue
What the work covers
Model serving and infrastructure
Self-hosted serving with vLLM behind a stable, OpenAI-compatible contract so your application never couples to a specific model.
Evaluation and regression harnesses
Fixture-based regression suites, behavioural checks and adversarial probes that run before deploy, not after the incident.
Observability
Tracing, quality-drift and hallucination monitoring, p95 latency and cost-per-task, so you fix systems instead of guessing.
Cost and capacity
Real token budgets extracted from your codebase, build-vs-buy modelling and a defensible break-even point.
Evidence this works
- ✓Self-hosted inference measured at roughly 5.4 times cheaper than an external API at scale
- ✓Over 99% of traffic resolved in under 100ms on a safety-critical path
- ✓A 12-hour reporting process reduced to 45 minutes with multi-agent pipelines
How the engagement runs
- 1Discovery sprint: I map your data, serving stack, constraints and risks, and hand back an architecture plan with cost estimates you own.
- 2Build: iterative delivery with a working demo every week, evaluation and observability built in from day one.
- 3Handover: documentation your team can maintain, plus the harnesses that keep it safe to change.
Common questions
Do you work with existing infrastructure?
Yes. Most engagements start with an existing prototype or partially built stack. I integrate with your tools rather than replacing them, and hand over systems your team can run.
Can you reduce our LLM inference costs?
Usually. The first step is measuring real token budgets from your codebase rather than estimating. On a previous system that analysis showed self-hosting was about 5.4 times cheaper than an external API at our volume, with break-even under 2,000 monthly active users.
What does an MLOps engagement typically cover?
Serving and deployment, an evaluation harness, observability, cost modelling and a maintainable handover. The exact scope is set in a short paid discovery sprint before any build work.
Related work and demos
Working on something like this?
Tell me what you are building and where it is stuck. I reply within one business day.
Send a project inquiry