Service
Model deployment & MLOps
We turn models into services your applications can use. That includes deployment, load testing, monitoring and repeatable releases, with attention to response times, reliability and operating costs.
- 0 errors
- at 25 concurrent users on one GPU, in a recent project
- Lower cost
- through batching, quantisation and right sized GPUs
- Traceable
- every live model tied to its code and data
Where we can help
- The model works on a laptop but falls over under load.
- Your GPU bill grows faster than your users.
- Releases are manual and nobody knows which model is live.
- Quality drifts and you find out from customers.
What you get
Model serving
Triton, TEI or FastAPI services with batching and autoscaling.
CI/CD for models
Automated tests, evaluation gates and repeatable releases.
Monitoring
Latency, errors, cost and quality on one dashboard.
Cost tuning
Quantisation, batching and right sized hardware.
How we run it
From assessment to delivery.
Week 1
Audit
We measure what you have today: latency, errors, cost per request and how releases happen.
Week 1 to 3
Package and serve
Models move into proper serving with batching, health checks and autoscaling.
Week 2 to 4
Automate releases
Tests and quality gates run on every change. Every live model can be traced to its code and data.
Ongoing
Monitor and tune
Dashboards for speed, errors, cost and quality, with alerts before users notice problems.
Where to start
Inference health check
1 to 2 weeks, fixed price
We load test your current setup, find the bottlenecks and give you a prioritised plan to make it faster and cheaper.
- Load test and latency report
- Cost per request breakdown
- Bottleneck analysis
- Prioritised fix plan
When we would advise against it.
If one of these applies, we will tell you in the first call.
- Your volume is low. A managed API costs less than your own GPUs.
- There is no working model yet. Start with a proof of concept.
- Your cloud provider already offers the serving setup you need.
Typical projects
Common applications.
- Scaling an inference API before a launch
- Moving from a hosted API to self hosted models
- Kubernetes or Docker setups for ML teams
- Speeding up video processing with CUDA
Tools we use
Tools that fit your project.
We choose tools around your existing systems, data requirements and operating costs.
Docker · Kubernetes · NVIDIA Triton · Hugging Face TEI · GitHub Actions · Prometheus
Questions about this service.
Do we need Kubernetes?
Not always. Many teams do better with a simpler Docker setup. We pick what your team can run with confidence.
Can you work in our cloud account?
Yes. We work in AWS, Google Cloud, Azure or your own servers, and leave everything documented for your team.
Is this only for large models?
No. The same practices help classic ML models, vision models and LLMs alike.
What would you like to build?
Tell us where your project stands. If AI is not worth it for you, we will say so.