Keywords AIPlatforma za nadzorljivost i odabirnu diagnostiku za uspješno pospuškujući aplikacije s pogonjenim LLM-ovima brže.

4.8 (4)

Recenzirao Daniel Nikulshyn·Ažurirano svibanj 2026.

LLM Observability Analytics Evaluation API Debugging Developer Tools Monitoring

Pregled

Keywords AI je platforma za razvojnoj zajednice koja omogućava praćenje, testiranje i poboljšanje aplikacija za umjetnu inteligenciju koje se zasnivaju na velikim jezilkim modelima. Centralizira logove, tragove i metrike kako bi timovi mogli vidjeti kako se njihovi upite, modeli i agensti ponašaju u proizvodnji. Alat pomaže inženjercima da uhvatite regresije, pike brzine i pitanja o kakovosti prije nego što korisnici to učiniju. Omogućujući strukturnu upozorenost u zahteve, odgovore i troškove, skraćuje pouzdani vijek između experimentiranja i ugradnje. Njezina namjena je timovima koji žele tretirati karakteristike LLM istim strogošću kao i preostalog dijela njihove arhitekture, kombinirajući ocjenu, upozorenja i analitiku u jednom radnom prostoru.

Ključne značajke

Dnevničko i odgovorne zapise za zahtjeve i odgovore
Trazanje za višekorake protokole LLM-a
Analitika za prompte i performanse modela
Slijedanje troškova i upotrebe tokena
Vrste procjene i upozorenja
SDK-ovi za popularne LLM prvide

Cijene

Model: $7
Kategorija: AI Infrastructure & MLOps
Ocjena: 4.8 / 5 (4)

Slučajevi uporabe

Debug production LLM issues

Engineers use centralized logs and traces to quickly diagnose failed requests, latency spikes, or unexpected model outputs in live AI applications.

Track LLM cost and token usage

Teams monitor token consumption and spend across models and prompts to control costs and identify expensive workflows before they scale out of hand.

Evaluate prompt and model performance

Use built-in evaluation and analytics to compare prompts, models, and agent configurations, catching quality regressions before they reach end users.

Trace multi-step agent workflows

Visualize complex agent chains with structured tracing to understand how each step contributes to the final output and pinpoint failure points.

Prednosti i nedostaci

Prednosti

Jedinstveni pogled na logove i trase LLM-a
Pomože brzo debugirati AI probleme u proizvodnji
Pruža praćenje za odgađenosti, troškove i kakovosti

Nedostaci

Najviše korisno za timove koji već izvode LLM-ove u proizvodnji
Potrebovalo je instrumentiranje postojećeg koda
Mali ekosustav u usporedbi s općeprihvaćenom APM tehnikom
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Recenzije

4.8

Prosjek iz 4 ocjena.

Prijavi se za ostavljanje recenzije.

Yuki Mori

Nov 24, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is sDKs for popular LLM providers — handled better than most — and helps debug production AI issues quickly. Worth the time if this is your use case.

Sanjay Gupta

Nov 15, 2025

Solid for our team

We rolled this out across the team last quarter and helps debug production AI issues quickly. Tracing for multi-step LLM workflows fits neatly into how we already work, and sDKs for popular LLM providers removed a step we used to do by hand. Smaller ecosystem than general-purpose APM tools, which is the main caveat, but it has held up under daily use.

Tomáš Novák

Oct 3, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is evaluation and alerting tools — handled better than most — and tracks latency, cost, and quality metrics. Worth the time if this is your use case.

Hannah Goldberg

Jul 28, 2025

Compared a few options

Evaluated this against two competitors. Where it wins: tracing for multi-step LLM workflows and unified view of LLM logs and traces. Where it lags: most useful for teams already running LLMs in production. On balance the feature set — especially evaluation and alerting tools — justifies the 4 stars for our use case.