No OpenAI. No Claude.No External APIs.
Keep your data fully private and run AI models on your infrastructure with complete control.
API costs scale with every token. Your infrastructure bill stays flat.
An illustrative cost comparison for a mid-size organisation running Llama 3.3 70B on its own EKS cluster versus paying per token to Claude or GPT-4o. At volume, the numbers diverge quickly.
Monthly cost vs token volume
Llama 3.3 70B Instruct
- Parameters
- 70B
- Context
- 128K tokens
- Comparable to
- GPT-4o · Claude 3.5
- License
- Commercial ✓
1× g5.48xlarge · EKS
- GPUs
- 8× A10G (24 GB ea.)
- Total VRAM
- 192 GB
- On-demand
- $12,195/mo
- Reserved 1-yr
- $7,805/mo
8,000-Employee Org
- Active users/day
- 1,500 (19%)
- Tokens/user/day
- ~30,000
- Monthly total
- ~1.35B tokens
- Token ratio
- 75% in / 25% out
| Volume | Claude 3.5 | GPT-4o | On-Demandflat | Reserved 1-yrflat |
|---|---|---|---|---|
| 500M | $3,000 | $2,188 | $12,195 | $7,805 |
| 1B | $6,000 | $4,375 | $12,195 | $7,805 |
| 1.35B | $8,100 | $5,906 | $12,195 | $7,805 |
| 2Best. load | $12,000 | $8,750 | $12,195 | $7,805 |
| 3B | $18,000 | $13,125 | $12,195 | $7,805 |
| 5B | $30,000 | $21,875 | $12,195 | $7,805 |
Every AI API call sends your data across a border you don't control.
Your data may be processed in another jurisdiction, handled under third-party policies, and subject to logging, retention, or transfer rules you don’t control. For regulated environments, that introduces real compliance, audit, and governance risk. The answer is:
Clustra Deploy
End-to-end deployment of production-grade AI inference inside your infrastructure. From model selection to serving configuration, we handle the full stack so your team ships AI without building plumbing.
Clustra Profile
Deep performance profiling for your AI workloads. We benchmark throughput, latency, and GPU utilisation across your hardware and models, then tune the stack to hit your production targets.
Clustra Monitor
Continuous observability for your private AI infrastructure. Real-time metrics, alerting, and compliance reporting — so you always know what your models are doing and can prove it to your regulator.
We deploy AI inside your walls.
No external API calls. No third-party data processing. We install and operate a full AI inference stack directly inside your Kubernetes cluster, VPC, or on-premise environment — so your sensitive data never crosses a network boundary you don't own.
Local and VPC deployment
We deploy production-grade AI inference inside your Kubernetes environment — EKS, AKS, GKE, or bare metal. Your cluster. Your network. Your data never leaves.
Hardware-agnostic by design
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.
AI agents inside your perimeter
Autonomous agents for document processing, internal search, workflow automation, and decision support — running entirely within your security boundary. No external API calls.
Open model support
We deploy any open-weight model: Llama, Mistral, DeepSeek, Qwen, Jais, ALLAM, Phi, Gemma. You choose the model. We make it run at production scale inside your environment.
Built for industries where data cannot leave.
Finance, government, healthcare, defence, and energy all operate under strict data residency and sovereignty requirements. We build AI infrastructure purpose-fit for each sector — meeting the specific compliance, security, and operational standards your regulator demands.
Same models. Better performance. Inside your infrastructure.
Local deployment is not a compromise on capability. With the right inference stack, regulated organisations can achieve strong performance while keeping AI inside their own environment.
Your data stays yours. Your AI should too.
Whether you are evaluating sovereign AI for the first time or ready to deploy next month, we will meet you where you are.
You will speak directly with an engineer. Not a sales team.