Azure AI Foundry and Self-Hosted LLMs, With Cost Discipline
We help Azure shops control AI costs. Foundry deployment in your tenant, spot GPU labs for sustained-throughput workloads, and a FinOps overlay across both. The same cost discipline we apply to Azure infrastructure, applied to AI.
AI Foundry, Deployed Properly
Azure AI Foundry inside your tenant, on your security boundary, in the right region for your workload and budget. Region choice and SKU selection set the cost ceiling for everything that follows, so we get this part right first.
Foundry Deployment in Your Tenant
Deploy Azure AI Foundry behind private endpoints with RBAC, content filtering, Key Vault integration, and monitoring on day one. Same security boundary as the rest of your Azure estate.
Region & Region-Pricing Guidance
Foundry pricing varies sharply between regions and SKUs. We map your model and quota requirements to the cheapest region that still meets your latency and data-residency needs. Usually 20-40% under the default choice.
Multi-Model Routing
Route expensive prompts to capable models, simple ones to cheaper models. GPT-5 family, Mistral, Llama, DeepSeek — all run on Azure compute, all under one RBAC and one bill. Anthropic Claude available too, but routes to Anthropic infrastructure (worth knowing if data residency is your driver).
Self-Hosted Spot GPU Lab on Azure
Batch inference, fine-tuning, embeddings, evals. Once a workload is sustained and high-volume, managed-API pricing breaks down fast. A spot GPU pool with an OpenAI-compatible LLM server in front is often 5-10x cheaper at the same throughput, and the data stays inside your VNET.
GPU Right-Sizing
A100 vs T4 vs L4 vs MI300X. We benchmark against your actual workload and pick the right SKU instead of the biggest one. Often the cheaper card wins.
Spot GPU Lab on Azure
Run sustained-throughput workloads (batch inference, fine-tuning, evals, embeddings) on Azure Spot GPU VMs for up to 90% discount. Checkpoint-based pipelines that handle eviction gracefully.
Self-Hosted LLM Stack
vLLM, llama.cpp, or Ollama on your spot GPU pool, fronted by an OpenAI-compatible API. Useful when token volume makes managed APIs painful, or when you need data to stay in your VNET.
Not always the right answer. Spiky, low-volume workloads usually belong on managed APIs. Steady, high-volume workloads usually don't. Our break-even analysis tells you which side of the line you're on before you commit.
FinOps Overlay for AI Workloads
The same blueprints approach as our Azure FinOps service, applied to AI. Break-even analysis, scheduling, pricing gotchas, and the dashboards that catch the £4,000 agentic-loop bill before month-end.
Break-Even Analysis
At what monthly token volume does self-hosting beat managed APIs? We model managed-API pricing vs spot GPU vs reserved GPU and show you the crossover point before you commit to either path.
Working-Hours Scheduling
Dev and staging GPU instances left running 24/7 are the new "forgotten VMs". Auto-shutdown policies, scale-to-zero where possible, and on-demand resume. Easy 50-70% saving on non-prod.
Pricing Gotcha Audit
Per-token pricing differences between regions, hidden Foundry hosting fees, PTU vs PAYG break-even, egress on RAG pipelines, log ingestion volume from agentic workflows. The bills that surprise people.
Token Consumption Dashboard
Which teams, which models, how many tokens, what cost, by week. Anomaly alerts when an agentic loop goes wrong and racks up £4,000 overnight.
Sustained-Throughput Routing
Steady, high-volume workloads belong on reserved or self-hosted infrastructure. Spiky, low-volume workloads belong on PAYG. We split your traffic correctly.
Regional Arbitrage
Latency-insensitive AI workloads routed to cheaper Azure regions. UK South vs East US2 vs Sweden Central pricing differences are real, and Foundry SKU availability varies between them too.
Why Us, Not an AI Specialist?
AI specialists optimise the model. We optimise the infrastructure and the bill.
| AI Specialists | Caleta |
|---|---|
| Focuses on the model and the use case | Focuses on the infrastructure and the bill |
| Demo-driven proof-of-concept first | Production deployment in your tenant, on your security boundary |
| "We'll figure out cost later" | Cost model on day one. Break-even and routing decisions made up front |
| Managed APIs only, vendor lock-in baked in | Managed APIs, self-hosted spot GPU, or hybrid. Whichever the maths supports |
| Separate AI silo bolted onto the org | AI deployed as part of your existing Azure infrastructure with FinOps overlay |
How We Engage
Start with a focused review. No long pitch, no obligation.
Book a 30-Min Review
We talk through your current AI plans, workloads, and Azure environment.
We Audit
Read-only access to your tenant. Foundry config, GPU SKUs, region choice, token spend, governance gaps. 3-5 working days.
Receive the Report
Cost projections, region recommendations, break-even analysis, gotcha list, and a 90-day roadmap.
Build or Hand Over
Implement yourself with the report, or engage us for the deployment and FinOps overlay.
Works With Our Other Services
Azure FinOps
AI cost work usually surfaces broader Azure overspend. Our FinOps service tackles waste across the whole estate.
Smart Hands
Deploying GPU hardware in a Slough data centre? Our Smart Hands team provides same-day installation and support across 38+ facilities.
Data Shuttle
Moving large training datasets or model artefacts between cloud and on-prem? Data Shuttle transfers terabytes in hours, not weeks.
AI & Infrastructure Insights
Practical guidance on AI infrastructure and cost discipline
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We help Azure shops control AI costs
Get in touch for a 30-minute review. No obligation, no long pitch. Just a focused conversation about your AI plans and where the costs are likely to land.