HomePrivate LLM vs Public AI APIs

    Private LLM vs Public AI APIs

    Choose the right AI deployment strategy for your data and compliance needs

    Private LLM Deployment

    Deploy open-source models within your own infrastructure where data never leaves your control.

    Advantages

    • Complete data privacy and control
    • Meets compliance requirements (HIPAA, SOC 2, PCI DSS)
    • No data used for third-party training
    • Predictable costs at scale
    • Customizable and fine-tunable
    • Works in air-gapped environments

    Considerations

    • Requires infrastructure investment
    • Needs GPU resources and expertise
    • Model updates require management
    • Initial setup takes longer
    • May need fine-tuning for best results

    Best For:

    Regulated industries, sensitive data, high-volume usage, compliance-critical applications

    Commercial AI APIs

    Use cloud-based AI services through APIs with per-token pricing and managed infrastructure.

    Advantages

    • Instant access to latest models
    • No infrastructure to manage
    • Continuously improving capabilities
    • Lower initial investment
    • Simple integration via API
    • Broad feature set

    Considerations

    • Data sent to third-party servers
    • May not meet compliance requirements
    • Per-token costs add up at scale
    • Rate limits and availability dependencies
    • Limited customization options
    • Data potentially used for training

    Best For:

    Non-sensitive applications, prototyping, low volume, general-purpose use cases

    Key Decision Factors

    Consider these factors when making your decision.

    Data Sensitivity

    PHI, PII, financial data, or trade secrets require private deployment

    Compliance Requirements

    HIPAA, SOC 2, PCI DSS audits may prohibit external data processing

    Usage Volume

    High-volume usage often makes private deployment more cost-effective

    Customization Needs

    Domain-specific fine-tuning requires private model access

    Speed to Market

    Public APIs are faster to start; private deployment takes planning

    Our Recommendation

    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.

    Frequently Asked Questions

    Is private LLM deployment more expensive?

    Initial setup costs more, but at scale, 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.

    Can private LLMs match public API quality?

    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.

    What about compliance with public AI 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.

    Need Help Deciding?

    We can help you evaluate your options and make the right choice for your organization.