AI Vendor Evaluation Template for AI Products

Evaluate 2-4 vendors for any significant AI procurement decision. Fewer than 2 gives you no comparison point. More than 4 creates diminishing returns and delays the decision. Start with 3: the market leader, a strong challenger, and an option that excels on your highest-priority dimension (e.g., the cheapest option if cost is your primary constraint).

Two to four weeks is typical. You need enough time to test with representative data (not just demo examples), measure latency under realistic load, and have multiple team members review output quality. Shorter POCs risk missing edge cases. Longer POCs risk analysis paralysis. The [AI Eval Scorecard](/tools/ai-eval-scorecard) provides a structured approach for evaluation testing.

Using multiple vendors is common and often optimal. Use the best vendor for each use case rather than forcing one vendor to handle everything. The risk is increased operational complexity. Mitigate this by standardizing your AI abstraction layer so swapping vendors requires changing a configuration, not rewriting your integration.

Come prepared with: your projected monthly volume (tokens or requests), a competitive quote from another vendor, a willingness to commit to a minimum term (annual contracts get better rates), and clarity on which features you need (enterprise features vs standard tier). Volume commits above $10K/month typically earn meaningful discounts.

Vendor lock-in from fine-tuned models. If you fine-tune a model on a specific vendor's platform, that fine-tuned model usually cannot be exported or used elsewhere. Before fine-tuning, confirm the vendor's model portability policy. If the model is locked, ensure the ROI justifies the switching cost risk. The [AI PM Handbook](/ai-guide) covers strategies for managing vendor dependencies in AI products.