Log10

Scale expert LLM evaluation with automated real-time error detection.

4.6 (5)
Daniel NikulshynAnmeldt av Daniel Nikulshyn·Oppdatert mai 2026

Oversikt

Log10 is a platform built to help teams improve the accuracy and reliability of large language model applications. It combines automated error detection with workflows that scale human expert review, making it easier to identify hallucinations, regressions, and quality issues as they happen in production. The platform logs LLM calls, surfaces problematic outputs, and trains custom auto-evaluators that learn from expert feedback. This lets engineering and domain teams continuously monitor model behavior, refine prompts, and ship more trustworthy AI features without manually inspecting every response.

Nøkkelfunksjoner

  • LLM call logging and tracing
  • Automated error and hallucination detection
  • Expert feedback collection workflows
  • Custom AI-powered evaluators
  • Prompt management and versioning
  • Production analytics dashboards

Brukstilfeller

Detect Hallucinations in Production LLMs

Automatically surface inaccurate or low-quality model outputs in real time, allowing teams to catch hallucinations and regressions before they impact end users.

Train Custom Auto-Evaluators

Collect expert feedback on LLM responses and use it to build AI-powered evaluators that scale domain-specific quality checks without manual review of every output.

Iterate and Debug Prompts

Use call logging, versioning, and analytics dashboards to compare prompt variations, diagnose failures, and refine LLM behavior over time.

Monitor LLM Reliability at Scale

Track production analytics and error trends across LLM applications, helping engineering teams maintain trustworthy AI features as usage grows.

Fordeler og ulemper

Fordeler

  • Real-time monitoring of LLM outputs
  • Custom auto-evaluators trained on expert feedback
  • Reduces manual review workload
  • Supports prompt iteration and debugging

Ulemper

  • Primarily aimed at technical teams
  • Value depends on quality of expert labeling
  • May be overkill for small-scale projects

Anmeldelser

4.6

Gjennomsnitt fra 5 vurderinger.

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K

Kwame Mensah

Does the job

Pretty happy overall. Automated error and hallucination detection just works and custom auto-evaluators trained on expert feedback. but no dealbreakers — I'd recommend it to a friend without hesitating.

P

Pierre Dubois

Use it every day

Honestly didn't expect to like it this much. Automated error and hallucination detection is exactly what I needed, and custom auto-evaluators trained on expert feedback. but I reach for it almost every day now and it just clicks.

E

Esther Adeyemi

Compared a few options

Evaluated this against two competitors. Where it wins: lLM call logging and tracing and real-time monitoring of LLM outputs. Where it lags: may be overkill for small-scale projects. On balance the feature set — especially automated error and hallucination detection — justifies the 4 stars for our use case.

C

Carlos Mendoza

Years in this space

I've evaluated a lot of these over the years. What stands out here is prompt management and versioning — handled better than most — and real-time monitoring of LLM outputs. May be overkill for small-scale projects is my one real gripe. Worth the time if this is your use case.

O

Omar Haddad

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on automated error and hallucination detection, and reduces manual review workload caught me off guard. Value depends on quality of expert labeling is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Spørsmål

Ingen spørsmål ennå — still det første.

Still et spørsmål

Alternativer til Large Language Models (LLMs)