Services · AI
Run AI workloads in production without the 3 a.m. surprises.
GPU clusters, model serving, MLOps platforms, and LLM observability — built so your AI runs as boringly as the rest of your stack.
How we deliver this: AI handles the routine analysis (audits, IaC drafts, runbook scaffolds, alert triage). A senior engineer reviews every change before it touches your production. Consultancy speed at consultancy quality.
Read more →When you need this
GPU spend out of control
Always-on H100s burning $30k a month while utilization sits at 12%. We design Spot-friendly architectures, autoscaled inference, and right-sized fleets that match traffic to capacity.
Models that work in dev, fail in prod
Latency, batch sizing, KV-cache, and tokenizer mismatches kill production AI. We harden the serving layer (vLLM, TGI, Triton) and put SLOs on the metrics that actually matter — p99 latency, tokens/sec, and cost per million tokens.
No observability into what the model is doing
When a hallucination ships to a customer, "check the logs" doesn't help. LLM observability (Langfuse, Phoenix, Helicone) plus structured tracing turn black-box behaviour into something you can debug.
How it works
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Phase 01
AI workload assessment
We map your model and pipeline footprint — training jobs, inference endpoints, vector stores, RAG retrieval, agent runtimes — and produce a cost-and-reliability snapshot.
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Phase 02
Infrastructure design
GPU node groups (EKS/GKE/AKS), model-serving runtime selection, MLOps platform (Kubeflow, MLflow, Argo Workflows), feature store, and observability stack — sized to your model and traffic profile.
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Phase 03
Production cutover
Wave-based rollout. Canary releases, traffic shadowing, and rollback plans for each model. The goal is "this looks like every other production deploy" — not a separate AI track.
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Phase 04
Cost and reliability tuning
Once stable, we tune. Speculative decoding, prompt caching, dynamic batching, KV-cache reuse, Spot orchestration. Most clients see 40–60% inference cost reduction without an SLO regression.
What you get
- → GPU cluster architecture (EKS / GKE / AKS) with Terraform modules
- → Model-serving runtime configured (vLLM, TGI, or Triton) with autoscaling
- → MLOps platform (Kubeflow, MLflow, or Argo Workflows) with paved-path templates
- → LLM observability stack (Langfuse or Phoenix) wired to your tracing
- → Cost dashboards with per-model and per-tenant attribution
- → Runbooks for inference incidents and model rollback
What changes for you
Predictable inference cost
Per-token cost dashboards, Spot orchestration, and dynamic batching turn GPU spend from a quarterly surprise into a budget line item.
Production-grade reliability
SLOs on p99 latency, tokens/sec, and error rate — with the alerting and runbooks to act on them.
Fast iteration for ML teams
Self-service model deployment with paved-path templates means data scientists ship without filing platform tickets.
Observability you can act on
Trace requests end-to-end through retrieval, LLM call, and tool use. Hallucinations become a debuggable artefact, not a customer email.
Model lifecycle that scales
Registry, versioning, lineage, and rollback as part of the platform — not a Slack thread and a prayer.
Multi-cloud GPU flexibility
We design GPU strategies that work across AWS, GCP, Azure, and Lambda Labs — so you're not locked into the cloud whose GPU quota happens to be approved.
What clients say
"CloudWizz rebuilt our delivery pipeline in eight weeks. Deploys went from a Friday-night ritual to a non-event we ship four times a day."
Director of Engineering
Fintech, Series C · 2025-11
"They turned a CFO emergency into a board-ready story in 12 weeks. The dashboards alone changed how engineering thinks about cost."
VP Engineering
Series B SaaS · 2026-01
Frequently asked questions
Do you cover training infrastructure too, or just inference? +
Both. Training (distributed training on Kubeflow or Ray, checkpoint management, dataset pipelines) is a common engagement. Many clients start with inference because that's where the production pain is loudest.
vLLM, TGI, or Triton — which should we use? +
vLLM for high-throughput LLM inference (best continuous batching), TGI for HuggingFace-native deployments, Triton for multi-modal or non-LLM workloads. We pick based on your model and traffic profile, not by ideology.
How do you handle GPU cost optimization? +
Spot-first design where the workload tolerates interruptions, reserved capacity for steady inference baseload, dynamic batching, prompt caching, and per-model right-sizing. Typical savings are 40–60% versus a stock setup.
Do you support self-hosted vs. managed model APIs? +
Yes, both. Often the right architecture is a hybrid — self-hosted for steady high-volume workloads where unit economics matter, managed APIs for spiky or specialty models. We help you draw the line.
What about RAG and vector databases? +
We design and operate RAG infrastructure end-to-end — chunking, embedding, retrieval, re-ranking, eval. Common stacks include Pinecone, Weaviate, pgvector, and Qdrant. We're database-agnostic; the design follows the use case.
Can you help with LLM observability and evaluation? +
Yes — Langfuse, Phoenix, or Helicone for tracing; offline eval with Ragas or LangSmith. We wire it into your existing observability (Datadog, Grafana) so AI signals live next to system signals.
How do you handle prompt and model versioning? +
Prompts are code — they live in git, are versioned, and ship through CI. Models live in a registry (MLflow, Weights & Biases) with promotion gates. Rollback is a deploy, not a recovery.
Do you cover agent runtimes (LangChain, LlamaIndex, custom)? +
Yes. Agent observability, retry/timeout discipline, tool-call tracing, and cost attribution per agent run. Agents fail in interesting ways; the platform underneath has to be predictable.
What about compliance for AI in regulated industries? +
PHI, PCI, and confidential data handling for AI workloads is a frequent ask. Pattern is data-isolated VPCs, audit logging on every model call, and explicit consent/redaction pipelines before retrieval.
How long is a typical AI infrastructure engagement? +
8–14 weeks for the first wave (assessment + production-ready inference for one model family). Longer engagements layer training, multi-model serving, and cost optimization in subsequent waves.