Keywords AIObservability and debugging platform for shipping reliable LLM-powered applications faster.

4.8 (4)

Reviewed by Daniel Nikulshyn·Updated May 2026

Developer Tools LLM Observability Monitoring Debugging Analytics Evaluation API

Overview

Keywords AI is a developer platform for monitoring, debugging, and improving AI applications built on large language models. It centralizes logs, traces, and metrics so teams can see how their prompts, models, and agents behave in production. The tool helps engineers catch regressions, latency spikes, and quality issues before users do. By providing structured visibility into requests, responses, and costs, it shortens the feedback loop between experimentation and deployment. It is aimed at teams that want to treat LLM features with the same rigor as the rest of their stack, combining evaluation, alerting, and analytics in one workspace.

Key features

Request and response logging
Tracing for multi-step LLM workflows
Prompt and model performance analytics
Cost and token usage tracking
Evaluation and alerting tools
SDKs for popular LLM providers

Pricing

Model: $7
Category: AI Infrastructure & MLOps
Rating: 4.8 / 5 (4)

Use cases

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.

Pros & Cons

Pros

Unified view of LLM logs and traces
Helps debug production AI issues quickly
Tracks latency, cost, and quality metrics
Integrates with common LLM providers

Cons

Most useful for teams already running LLMs in production
Requires instrumentation of existing code
Smaller ecosystem than general-purpose APM tools

Reviews

4.8

Average from 4 ratings.

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.