AI Glossary
Self-Hosted AI
Running AI models on your own infrastructure (on-premise servers or private cloud) rather than using third-party APIs. This gives full control over data privacy, latency, and costs.
Understanding Self-Hosted AI
Self-hosted AI means running AI models on infrastructure you control — your own servers, a private cloud, or a dedicated cluster. This approach gives you complete control over data privacy (nothing leaves your network), predictable costs at scale, and the ability to customize every aspect of the deployment.
Open-source models like Llama, Mistral, and Phi make self-hosting increasingly viable. A $10,000-20,000 GPU server can run models that deliver 80-90% of GPT-4's quality for many business tasks, with zero per-query API costs.
Self-hosting makes the most sense when you process high volumes (millions of queries/month), handle highly sensitive data, need guaranteed uptime, or want to customize model behavior at a level beyond what API providers allow.
Self-Hosted AI in Canada
Canadian data sovereignty requirements in healthcare, finance, and government often make self-hosted AI the only compliant option, especially for systems processing personal information.
Related Services
Frequently Asked Questions
Initial hardware investment is $10,000-50,000 for GPU servers. At high volumes (1M+ queries/month), self-hosting typically costs 5-10x less than API services. At low volumes, APIs are more economical.
Llama 3 (Meta), Mistral, and Phi (Microsoft) are leading options. Llama 3 offers the best balance of quality and efficiency. Mistral excels at European languages. All have commercial-friendly licenses.
See Self-Hosted AI in Action
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