Every LLM eval platform, running the same app

We cover this category at two depths, and we label every page so you always know which one you are reading.

Hands-on tested (12 platforms)

We instrument a single identical LLM application - the same traces, the same eval set, the same volume - across every platform, then compare what each one captures, how it scores, what it costs at realistic span volumes, and how much work self-hosting actually takes. Same app, same day, published methodology. These pages carry a Hands-on tested badge.

Researched (61 platforms)

The category is far larger than any team can instrument at once, and a tool nobody has written up honestly is still worth knowing about. These pages are built from primary sources - vendor pricing pages, documentation, licenses, funding records and acquisition filings - and we state plainly when we could not verify something rather than filling the gap. They carry a Researched badge and no performance numbers we did not measure ourselves.

Researched entries get promoted to tested over time. The distinction exists so that a directory listing is never mistaken for a benchmark.

What to look for

• Corporate status - is the vendor alive, acquired, or quietly in maintenance mode
• The real billing unit - spans, traces, seats, events or GB ingested change the bill by orders of magnitude
• License terms - MIT, Apache 2.0 and AGPL 3.0 have very different commercial consequences
• Self-hosting - whether it genuinely exists, and what is gated behind the cloud tier
• Pricing - what it actually costs at your volume, not the headline number

Categories we cover

• Observability & Tracing - Trace, log and monitor LLM applications in production.
• Eval Frameworks - Libraries for scoring model and agent output.
• Prompt Management - Version, test and deploy prompts.
• Guardrails & Safety - Runtime validation, PII and injection defence.
• LLM Gateways - Routing, caching and cost control proxies.
• Agent Evaluation - Tools built specifically for multi-step agent traces.

Our recommendation

There's no single best tool - it depends on team size, stack, and budget. Browse the guides for detailed recommendations by team type.