AI · Infrastructure
Build the runway before you fly the model.
GPU planning, serving topology, latency budgets, and observability — engineered before traffic, not after the first incident.
Overview
AI infrastructure is where most production AI projects quietly fail. We design forcapacity, cost, and failure modesup front — so the system holds up the day the demo turns into a deployment.
What it is
The plumbing under every model that ships.
AI infrastructure is the runtime substrate that turns a model checkpoint into a service: GPUs and the schedulers that share them, routers that pin sessions and cache state, replicas that scale with load, and the observability that tells you why p99 doubled at 14:32.
Done well, it is invisible. Done poorly, every model launch starts a new firefight. We build the boring, durable layer underneath — so the interesting work above it can ship without drama.
Workflow
Inference serving, end to end.
- Client sends a request to the API gateway (auth and quota).
- The router selects the model and tenant pool.
- Inference replicas autoscale and pull weights from the GPU pool with KV-cache.
- A parallel cache stores prompts and embeddings, short-circuiting hot paths.
- Every node emits logs, traces, and metrics into the observability bus.
Deliverables
What you walk away with.
- Capacity model: GPU class, memory, replicas, and headroom mapped to traffic forecasts.
- Serving topology: gateway, router, and replica pool with explicit latency and cost budgets.
- Observability: structured logs, distributed traces, and per-route latency/throughput SLOs.
- Failure-mode runbook: cold-start, cache miss, model-load failure, and circuit-break behavior.
- Cost dashboard: per-request, per-tenant, and per-model unit economics, refreshed nightly.
Pitfalls
How we don't do it.
- Sizing a cluster from a vendor calculator instead of measured token throughput.
- Treating inference as stateless when KV-cache and warm replicas dominate p99.
- Shipping without a backpressure or shed-load policy — the first spike becomes an outage.
- Logging prompts to plaintext stores without retention, redaction, or tenant isolation.
Engagement
How we work with you.
01
Discover
Workload shape, SLOs, and the cost ceiling that matters to the business.
02
Architect
Topology, capacity model, and the failure modes you accept by design.
03
Build
Gateway, router, replicas, and observability — wired into your existing platform.
04
Operate
On-call playbooks, capacity reviews, and a tuning loop against real traffic.
Ready to design for the day after launch?
Tell us your traffic shape and your cost ceiling. We'll come back with a topology, a capacity model, and a list of the failure modes you should plan for.
Related