Evaluating AI Service Vendors Beyond Your LLM Provider
Most engineering teams spend weeks evaluating LLM providers—benchmarking latency, testing accuracy, negotiating pricing. Then they pick an observability tool, a guardrail vendor, and an embedding provider in an afternoon, on the basis of a well-designed landing page and a favorable blog post. The asymmetry is backwards. Your LLM provider is probably a well-capitalized company with stable APIs. The niche vendors surrounding it often are not.
The AI service ecosystem has exploded into dozens of categories: guardrail vendors, embedding providers, observability and tracing tools, fine-tuning platforms, evaluation frameworks. Each category has ten startups competing for the same enterprise budgets. Some will be acquired. More will shut down. A few will pivot and deprecate your critical workflow with a 90-day notice email. Building on this ecosystem without rigorous evaluation is a form of technical debt that doesn't show up in your backlog until it's already a production incident.
