.//Blog
Research findings, product updates, and technical deep-dives from the Trainly team.
We tested five providers trying to ship sub-100ms LLM-as-judge scoring. None of them got us there. Here are the numbers we measured, where the time actually goes, and why beating Galileo's Luna-2 is a research project rather than a config tweak.
Most teams ship AI features and never look back. Here are the blind spots we see in every pipeline: hidden cost concentration, wrong-model routing, silent quality drift, and semantic anomalies your logs won't catch.
A breakdown of the leading LLM observability platforms for agent debugging, tracing, evaluation, and real-time guardrails. We compare Trainly, LangSmith, Langfuse, Helicone, Braintrust, Arize Phoenix, and Datadog.
We published research on behavioral reliability in LLM systems. Here is what we found, why it matters, and how we built it into Trainly. Achieving a 97.5/100 reliability score through behavioral contracts, deterministic validators, and DPO fine-tuning.