Proprietary LLM

Benched.ai Editorial Team

A proprietary LLM is owned by a vendor who offers access via SaaS APIs but does not release weights or training data.

Pros and Cons

Aspect	Proprietary	Open-source
Performance	State-of-the-art	Varies
Transparency	Limited	Full
Cost model	Pay-per-use	GPU cost
Customization	Prompt only (usually)	Full fine-tune

Risk Mitigation

Evaluate vendor SLAs and data-use policies.
Plan for model regressions after silent updates.
Negotiate pricing tiers for predictable spend.

Current Trends (2025)

Feature flags allow opt-in to model version updates.
Confidential compute enclaves reassure on data privacy.
Some vendors offer weight escrow for continuity insurance.

Implementation Tips

Version API calls with model=vendor-model@2025-06-15.
Fallback to cheaper tier when latency budget breached.
Track token usage client-side to detect billing errors.