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Home/Design services/AI inference
Model serving sized for your SLA, not a vendor default
Batching, autoscaling, quantisation and caching designed around the latency and throughput numbers you promised your users.
What you get
Designed for your environment
Batching and quantisation
Serving configured around your model family. INT8 or FP16 where the accuracy budget allows.
Autoscaling
Scale replicas on request latency and GPU utilisation, not just CPU load.
Latency SLA monitoring
P50, P95 and P99 tracked per model. Alerts before your users notice.
Multi-model inference
Multiple model versions behind a single endpoint, with traffic-split rollout.
Deliverables
What lands at the end of the engagement
Working environment
Built, commissioned and tested against the design.
Runbooks
Day-one operations and common failure procedures.
Evidence pack
Configuration, test results and controls for your audit team.
Named team
The engineers who built it, reachable afterwards.
FAQ
Common questions
Do you support vLLM and TensorRT?
Yes. We configure the serving stack that matches your model and hardware. vLLM, TensorRT-LLM and Triton are all used depending on the workload.
Can we run on our own GPUs?
Yes. The design is hardware-agnostic. We specify whatever meets the SLA.
Next step
Scope this service today.
Fixed price, defined scope, handed over working.