Time to first production agent
- In-house platform team
- 6–18 months hiring and building
- Azure OpenAI / Databricks
- Strong model access; governance integration varies by service
- cloudstrata AI Foundry
- Weeks on managed runtime
AI platform engineering as a service
70+ specialists · Linz HQ · Remote across Europe
Build, deploy, and scale production AIProduction AI without a platform org
Deploy governed AI agents in weeks — without hiring a platform team. cloudstrata combines strategic cloud advisory with AI Foundry, our managed platform: multi-tenant runtime, retrieval pipelines, usage metering, and EU-hosted infrastructure.Governed AI in weeks — without a platform team. Multi-tenant runtime, retrieval, and metering. Linz · Europe.
Insight·OpenAI Reasoning Models: What o1 and o3 Mean for Enterprise AIProduct·AI Foundry – production AI runtime for governed agentsCustomer·Unternehmensdaten.at – legal data platform in productionInsight·ChatGPT API and Developer Platform: What's New for BuildersProduct·AI Foundry – governed agents, RAG, and metering built inInsight·RAG for Enterprise: Building Production-Ready Retrieval SystemsInsight·OpenAI Reasoning Models: What o1 and o3 Mean for Enterprise AIProduct·AI Foundry – production AI runtime for governed agentsCustomer·Unternehmensdaten.at – legal data platform in productionInsight·ChatGPT API and Developer Platform: What's New for BuildersProduct·AI Foundry – governed agents, RAG, and metering built inInsight·RAG for Enterprise: Building Production-Ready Retrieval Systems
WHAT WE DO
Cloud advisory, custom software, and operated AI platforms
Your roadmap should not depend on building an internal platform team. Under AI platform engineering as a service, we own the architecture, integrations, and operated AI Foundry surface so prompts, retrieval, observability, and billing stay aligned—from pilot through scale. Learn more about our services
WHO WE SERVE
Clients that trust us.
If your roadmap calls for agents, grounded knowledge, or customer-facing copilots — but your headcount plan has no room for a dedicated platform team — we built our offering for that constraint.
Learn more about our clients
Selected clients
Organizations we have delivered projects for
SOLUTION
AI Foundry — the managed runtime behind our AI platform engineering as a service.
AI Foundry
cloudstrata AI Foundry
AI Foundry bridges pilot demos and a dedicated platform team: multi-tenant workspaces, model routing, retrieval pipelines, evaluation hooks, and usage-based billing — ready when procurement asks real questions.
Your teams iterate on prompts and datasets; we operate the shared platform — identity boundaries, audit trails, and spend tied to outcomes — with central governance on a single platform.
- Start with concrete use cases and clear success criteria — expand when results and governance are in place
- Agents and integrations share tenancy, audit trails, and usage limits — for stable, auditable operations
- Model and retrieval costs reported alongside business KPIs — finance and engineering read the same numbers
- Managed SaaS posture with usage metering and quotas for predictable compute costs
- Role-based access, secrets management, and tenant isolation sized for regulated industries
- Roadmaps align procurement, security, and engineering — one shared plan, not conflicting priorities
PLATFORM
What procurement and security teams evaluate.
Multi-tenant agents, retrieval, metering, and governance on one managed surface — the same architecture we detail on the AI Foundry page.
Explore full platformBUILD VS. BUY
Comparing in-house build, hyperscaler APIs, and managed AI Foundry
Side-by-side for procurement and engineering leads comparing an in-house platform team, hyperscaler APIs, and cloudstrata's managed runtime.
| Criterion | In-house platform team | Azure OpenAI / Databricks | cloudstrata AI Foundry |
|---|---|---|---|
| Time to first production agent | 6–18 months hiring and building | Strong model access; governance integration varies by service | Weeks on managed runtime |
| Governance & audit trails | You design and operate everything | Varies by service; often stitched together | Approvals, RBAC, and audit built in |
| Retrieval pipelines (RAG) | Custom build per use case | Bring your own orchestration | Managed connectors and citations |
| Cost visibility | Internal FinOps to build | Usage spread across invoices | Metering per agent and workspace |
| EU data residency | Your infrastructure choice | Region-dependent configuration | EU-hosted by default |
Governance & audit trails
- In-house platform team
- You design and operate everything
- Azure OpenAI / Databricks
- Varies by service; often stitched together
- cloudstrata AI Foundry
- Approvals, RBAC, and audit built in
Retrieval pipelines (RAG)
- In-house platform team
- Custom build per use case
- Azure OpenAI / Databricks
- Bring your own orchestration
- cloudstrata AI Foundry
- Managed connectors and citations
Cost visibility
- In-house platform team
- Internal FinOps to build
- Azure OpenAI / Databricks
- Usage spread across invoices
- cloudstrata AI Foundry
- Metering per agent and workspace
EU data residency
- In-house platform team
- Your infrastructure choice
- Azure OpenAI / Databricks
- Region-dependent configuration
- cloudstrata AI Foundry
- EU-hosted by default
HOW THE ENGAGEMENT RUNS
From discovery to production – with clear milestones.
Each sprint refines assumptions about retrieval quality, latency requirements, and cost — with concrete, measurable criteria.
1
Discovery Meeting
We learn about your goals, challenges, and technical landscape. No obligation – just an open exchange.
2
Solution Presentation
We propose architectures tying embeddings stores, orchestration, observability, and procurement dashboards — grounded in implementable infrastructure and operational requirements.
3
Proposal & Planning
Clear scope, timeline, and commercial terms. We align on milestones before we start.
4
Implementation
Engineers pair-program integrations, CI/CD lanes, and LLMOps safeguards – with demos rooted in logged prompts and retrieval citations.
5
Support & Iteration
We stay with you: launch support, optimization, and continuous improvement.
FIELD NOTES
Practical perspectives on AI platform engineering.
Essays on retrieval economics, inference posture, and structured rollout planning — with a focus on operations and measurement.
AI
OpenAI Reasoning Models: What o1 and o3 Mean for Enterprise AI
OpenAI's reasoning models (o1, o3) represent a leap in chain-of-thought capabilities. Learn how enterprises can leverage these models for complex problem-solving, code generation, and strategic decision support.
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AI
ChatGPT API and Developer Platform: What's New for Builders
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AI
RAG for Enterprise: Building Production-Ready Retrieval Systems
Retrieval Augmented Generation (RAG) is essential for grounding LLMs in your data. Learn best practices for building scalable, accurate RAG pipelines in enterprise environments.
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- KubernetesKubernetes and AI Workloads: Best Practices for 2026March 11, 2026
- CloudAzure OpenAI Service: Enterprise Deployment PatternsMarch 10, 2026
Cloud Platform Status
CONTACT
Get in touch
Tell us about your use case — we'll respond with a tailored next step.
We aim to reply within one business day.