Should I use an LLM proxy to avoid vendor lock-in?

Yes—if you expect to switch providers, use multiple model vendors, or want centralized control, an LLM proxy can be a good way to reduce vendor lock-in.

When an LLM proxy helps

Use one if you want:

• Provider abstraction: swap OpenAI / Anthropic / Google / open-source backends with less code change
• Centralized governance: logging, auth, rate limits, cost tracking, prompt management
• Fallbacks and routing: send requests to different models based on task, latency, or cost
• Easier experimentation: A/B test models without rewriting your app

When it may not be worth it

A proxy can add:

• Latency
• Extra complexity
• Another dependency to maintain
• Potentially less access to provider-specific features if you abstract too hard

If you’re using only one vendor and don’t expect to change soon, the proxy may be overkill.

Important caveat

A proxy reduces technical lock-in, but not necessarily business lock-in:

• Your prompts, evals, tooling, and workflows may still become model-specific
• Provider-specific features like structured output, function calling, multimodal input, or fine-tuning can still create coupling
• Costs and quotas can still tie you to a particular ecosystem

Best practice

A good middle ground is:

• Use a thin provider abstraction layer in your app
• Keep prompts and model configs versioned and portable
• Avoid relying on nonportable features unless you truly need them
• Add evals so you can compare models objectively

Rule of thumb

• Yes: if you’re building a product, expect scale, or want flexibility
• Maybe not: if it’s a small/internal project with one model and low switching risk

If you want, I can also give you a decision checklist for whether a proxy is worth it in your specific case.