Your data never leaves your infrastructure.
We deploy production-grade LLM infrastructure inside your own data centre, private cloud, or VPC. Open-weight models. Full control over data handling. No external AI API dependency in the serving path.
The problem is regulatory, not technical
Enterprises in regulated markets want to use AI. The models exist. The hardware exists. But sending sensitive data to foreign-hosted APIs creates real legal exposure.
GDPR Articles 44–49 — European Union
Restricts transfer of personal data outside the EU without adequate safeguards. Schrems II invalidated Privacy Shield, making cloud AI transfers to US-hosted providers legally complex. On-premise deployment within the EU eliminates the transfer question entirely — no SCCs, no adequacy decisions required.
HIPAA — United States
Requires administrative, physical, and technical safeguards for protected health information (PHI). Every API call to a cloud AI provider creates a potential PHI exposure. On-premise deployment within a covered entity's environment means PHI never leaves the entity's control.
PDPL Article 29 — Saudi Arabia
Explicitly restricts cross-border transfer of personal data. Government and regulated-sector data must remain within the Kingdom unless specific exemptions are met. On-premise deployment satisfies Article 29 by architecture, not by paperwork.
PDPA — Singapore & Thailand
Restricts cross-border transfer of personal data without consent or adequate protection. MAS Technology Risk Management guidelines add further controls for financial services. Local deployment eliminates the transfer entirely.
DPDP Act — India
India's Digital Personal Data Protection Act restricts certain cross-border data flows and empowers the government to notify restricted jurisdictions. On-premise deployment within Indian infrastructure satisfies data localisation provisions by default.
Audit & liability risk — every jurisdiction
Regulated industries — banking, government, healthcare, energy — face audit risk and potential liability if they use foreign-hosted AI on sensitive data. This applies in every major jurisdiction. The risk isn't theoretical.
What we actually do
Three things. We do them well.
On-premise inference, fully isolated
We deploy NVIDIA Dynamo + SGLang + vLLM inside your own infrastructure — your data centre, your private cloud, your VPC on EKS, AKS, GKE, or bare metal. You control where the system runs, how it is connected, and how it is governed.
Hardware-agnostic, vendor-neutral
We deploy across NVIDIA, AMD Instinct, Intel Gaudi, and AWS Trainium/Inferentia. You are never locked to one hardware vendor. As silicon improves, your stack moves with it.
Multi-language model support, Arabic-first expertise
We deploy any open-weight model — Llama, Mistral, DeepSeek, Qwen, Jais (G42/MBZUAI), ALLAM (SDAIA). Our deepest language specialisation is Arabic, built for the world’s strictest regulatory environments, but the same infrastructure supports any language and any model.
Hands-on deployment and ongoing operations
We are building Clustra as a hands-on deployment and operations partner for regulated organisations. We do not want to be a slide deck or a software login — we want to help customers deploy, tune, and operate the stack in real environments.
How we approach performance
Performance matters in sovereign AI, but it depends on model choice, prompt shape, concurrency, hardware, and workload design. We benchmark in the customer environment instead of publishing numbers that may not match your stack.
| Metric | Baseline | Clustra Stack | Improvement |
|---|---|---|---|
| Serving architectureModel routing, batching, prefill/decode separation | Default deployment choices | Stack tuned for workload shape | Often more efficient |
| GPU efficiencyScheduling, cache strategy, concurrency tuning | Capacity left underused | Measured and tuned | Better utilization potential |
| Structured outputsJSON schemas, regex constraints, validation paths | Application-level retries | Constraint-aware generation patterns | More reliable workflows |
| Deployment economicsParticularly relevant for recurring sensitive workloads | Opaque usage pricing | Infrastructure you control | Clearer cost planning |
| Compliance postureArchitecture supports data residency requirements | Third-party processing risk | AI stays inside approved environments | Lower transfer exposure |
We prefer customer-specific benchmarking to headline numbers. Results vary materially by model, prompt design, hardware, and concurrency profile.
Deployment patterns we are built for
Three common deployment environments for sovereign AI. We describe them at the sector level because the architectural pattern matters more than inflated customer-name signaling.
Government / Public Sector
National infrastructureOn-premise deployment pattern for citizen services and document workflows where identity data must remain in-country.
- Built for environments where sensitive data must remain under direct government control
- Structured generation patterns can support auditable workflow outputs
- Can be designed for disconnected or tightly controlled network environments
Banking / Fintech
Regulated financial environmentPrivate deployment pattern for financial document processing, internal search, and workflow automation under strict audit expectations.
- Can support structured output requirements for downstream systems
- Designed for teams that need tighter control over model behavior and data handling
- Useful when external AI APIs create unacceptable regulatory or vendor risk
Energy / Petrochemicals
Edge deploymentLocal deployment pattern for edge or air-gapped industrial sites where cloud connectivity is limited or unavailable.
- Supports local inference in environments with strict OT and network controls
- Can separate batch and real-time workloads based on operational needs
- Useful for safety, maintenance, and document workflows near the asset
How we compare
There are several ways to get AI running inside a regulated environment. Each has tradeoffs. Here is an honest comparison.
Cloud AI APIs
OpenAI, Azure AI, Google Vertex, AWS Bedrock
Fast prototyping. Non-sensitive workloads where data residency is not a constraint.
External AI APIs can create data transfer, logging, vendor dependency, and review challenges for sensitive workloads. They are usually a poor fit for air-gapped or strict data residency environments.
Global System Integrators
Large consulting firms and regional SIs
Organisations that need a single vendor for strategy, implementation, and change management across the entire business.
Often slower, more expensive, and broader in scope than teams need when the immediate problem is getting a private AI stack running reliably inside a regulated environment.
Private AI Platforms
Self-service deployment platforms
Teams with strong internal ML engineering who want a UI layer on top of open-source tooling.
Platform products still require internal ownership for architecture, operations, and performance tuning. They are not the same as having a specialist team accountable for deployment execution.
DIY Open Source
Internal teams assembling vLLM, Ollama, HuggingFace
Research teams and well-funded platform engineering groups with 6+ months of runway and ML serving experience.
The stack is free, but the integration, tuning, governance, and operational burden still sit with your team. That is often the most expensive part of the decision.
Clustra AI
We sit in a specific position: hands-on sovereign AI deployment for regulated industries. We deploy, tune, and operate the full inference stack inside your environment. We are not a platform you log into, not a consultancy that writes a report and leaves, and not a cloud API you send data to. If your data cannot leave your infrastructure, and you need production AI running in weeks rather than quarters, that is what we do.
Compliance — what we can actually claim
No invented certifications. No logo walls. Here is the practical link between common regulatory requirements and local deployment architecture.
GDPR Articles 44–49 — Cross-border transfer restrictions (EU)
GDPR restricts transfer of personal data to countries without an adequate level of data protection. For some organisations, local deployment within the EU can materially simplify the transfer analysis because processing remains inside the customer-controlled environment.
HIPAA — Protected health information (United States)
HIPAA's Security Rule requires covered entities and business associates to implement safeguards for protected health information. For healthcare organisations that want to reduce external processing exposure, local deployment can simplify architecture and vendor-risk decisions.
PDPL Article 29 — Cross-border transfer restrictions (Saudi Arabia)
Saudi Arabia's Personal Data Protection Law restricts transferring personal data outside the Kingdom. Local deployment can support architectures designed to keep regulated data within Saudi-controlled infrastructure.
PDPA — Cross-border data transfer (Singapore, Thailand)
The Personal Data Protection Act restricts cross-border transfer of personal data without consent or a comparable standard of protection in the receiving country. For regulated sectors, local deployment can reduce transfer complexity and support tighter operational control.
DPDP Act — Data localisation (India)
India's Digital Personal Data Protection Act empowers the government to restrict cross-border data flows to notified jurisdictions. Local deployment in Indian infrastructure can help organisations prepare for stricter data-handling expectations.
NDMO Data Classification Framework (Saudi Arabia)
The National Data Management Office requires government data to be classified and handled according to residency requirements. For public sector workloads, local deployment can align better with residency and access-control expectations than external AI APIs.
UAE PDPL — Personal data transfer restrictions
The UAE's data protection law restricts cross-border transfer of personal data without adequate safeguards. Local VPC or on-premise deployment can support architectures built to keep sensitive data within UAE-controlled environments.
We are intentionally conservative about proof claims at this stage. As we build a reference base, this section should evolve into published case studies, benchmark notes, and implementation learnings.
See it on your hardware
Two ways to start. Both lead to a conversation with an engineer, not a sales rep.