Capacity and traffic planning
Concurrency modelling, autoscaling policy and burst handling based on measured traffic, not averages.
All services
Data & Platform
Cloud & Inference Infrastructure
Capacity, latency and cost decided deliberately.
- 30–60%
- Typical inference spend reduction
- Per-team
- Cost attribution and budgets
- Residency
- Enforced by architecture
The problem we solve
AI workloads break the assumptions cloud estates were sized against. We design inference infrastructure around your traffic shape and residency rules, then run the FinOps discipline that keeps the bill explainable.
What the work includes
Private model hosting
Open-weight models served in your VPC or on-premise with isolation, quotas and hardened endpoints.
Edge and hybrid inference
On-device and on-site inference where latency, connectivity or privacy makes cloud calls impractical.
AI FinOps
Token and GPU attribution per team, budget alerts, committed-spend planning and unit-cost reporting.
Reliability engineering
Multi-region failover, provider fallback and degradation modes that keep the product usable.
How the engagement runs
A sequence you can plan a quarter around.
- 01
Measure
Traffic profiling, current spend breakdown and constraint capture.
- 02
Design
Architecture options with cost, latency and residency trade-offs made explicit.
- 03
Implement
Build, migration and FinOps reporting handed to your platform team.
Common questions
Before you commit.
- Self-host or API?
- It depends on volume, residency and how much operations capacity you have. We model both and show the crossover point.
- Can you cut costs without a migration?
- Usually yes — routing, caching and prompt hygiene typically deliver the first tranche of savings.
Related practices
Data Foundations
Pipelines, lineage, quality gates and feature stores so every model runs on data you can defend in an audit.
ViewMLOps & AI Platform
CI/CD for models, observability, drift detection, rollback discipline and a self-service platform for your teams.
ViewLegacy Modernisation
AI-assisted code understanding, migration and re-platforming for systems nobody wants to touch.
ViewTalk through cloud & inference infrastructure.
A 45-minute briefing with the people who would run the work — scope, timeline and a straight answer on whether it is the right next step.