vLLM is easy to deploy. Keeping it fast is the hard part.
Getting started with vLLM is simple. Keeping it stable, compliant and cost-efficient at scale is not - model extensions, crashes, scaling behaviour and drifting SLOs all land on your team. We run that layer with you.
Simple to start. Difficult to keep stable.
Getting vLLM running takes an afternoon. Keeping it stable, compliant and cost-efficient at scale is a different job — managing model extensions, handling crashes and downtime, holding latency under load, and proving compliance when someone asks.
We take that layer on: an inference engine that stays reliable, stays compliant, and stays tuned to the workloads you actually serve.
Five things we run with you.
Maintain & scale reliably
As the application scales, so do the risks — crashes, downtime and scaling failures. We keep the deployment stable as load grows.
Meeting and holding SLOs
Low latency, high throughput and predictable behaviour, tracked against the objectives you have committed to.
Optimisation that scales
Suboptimal configuration quietly drains millions at scale. Every workload and agent behaves differently, and the configuration should follow.
Model support & extension
Fine-tuning, adapting for new tasks, or integrating emerging models — full lifecycle support rather than a one-off setup.
Making vLLM compliant
Data handling, audit trails and governance standards, met by the deployment rather than bolted on afterwards.
What we bring.
Optimised deployments
- Cloud (AWS, GCP, Azure) or on-prem setup
- Configurations tuned for model size, batch pattern and latency goal
- Integration with Kubernetes, Ray, Triton and existing serving infrastructure
Performance optimisation
- Memory and throughput tuning
- Profiling and benchmarking on custom workloads
- GPU scheduling and scaling strategies
Custom integrations
- API gateways and enterprise authentication
- Monitoring and observability with Prometheus and Grafana
- CI/CD for model rollout and versioning
Enterprise support
- SLAs for uptime and response
- 24/7 troubleshooting assistance
- Continuous updates as vLLM evolves
Three situations this fits.
Why Bud
We work across LLM infrastructure and deployment, open-source model serving, hardware-aware optimisation, and MLOps for AI systems. The aim is not just to keep vLLM running, but to run it the way the teams who do this best run it.
Book a free consultation.
We will assess your setup and show you where the throughput and cost are going.