Choose the right AI deployment strategy for your data and compliance needs
Deploy open-source models within your own infrastructure where data never leaves your control.
Regulated industries, sensitive data, high-volume usage, compliance-critical applications
Use cloud-based AI services through APIs with per-token pricing and managed infrastructure.
Non-sensitive applications, prototyping, low volume, general-purpose use cases
Consider these factors when making your decision.
PHI, PII, financial data, or trade secrets require private deployment
Enterprise security policies may prohibit external data processing
High-volume usage often makes private deployment more cost-effective
Domain-specific fine-tuning requires private model access
Public APIs are faster to start; private deployment takes planning
For regulated industries or sensitive data, private LLM deployment is often the only compliant option. Start with a clear assessment of what data will touch the AI system. If any sensitive data is involved, or if you need audit trails for compliance, private deployment is the safer choice. Many organizations use a hybrid approach: public APIs for general tasks, private deployment for sensitive workloads.
Initial setup costs more, but. for any document type, private deployment often saves money. Public API costs are per-token and can grow significantly with usage. A typical enterprise processing millions of tokens monthly often sees 50-70% cost reduction with private deployment.
For most business applications, yes. Models like Llama 3, Mistral, and others perform comparably to proprietary models. For domain-specific tasks, fine-tuned private models often outperform general-purpose public APIs.
Major providers offer enterprise agreements with BAAs and compliance certifications. However, your data still leaves your environment. For strict compliance requirements, auditors often prefer seeing data stay internal with private deployment.
We can help you evaluate your options and make the right choice for your organization.