# How to Evaluate Data and AI Observability Tools

DevFeed: [How to Evaluate Data and AI Observability Tools](<https://devfeed.tech/articles/best-data-observability-tools-with-rfp-template-82968.md>)

Original publisher: [Read original article](<https://montecarlo.ai/blog-best-data-observability-tools-with-rfp>)

Author: Michael Segner

Published: 2025-05-09T18:03:48Z

Content type: article

Language: en

Sources: [Monte Carlo](<https://devfeed.tech/sources/monte-carlo.md>)

Topics: [data observability](<https://devfeed.tech/topics/data-observability.md>), [LLM observability](<https://devfeed.tech/topics/llm-observability.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>), [Production Engineering](<https://devfeed.tech/topics/production-engineering.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-observability](<https://devfeed.tech/tags/ai-observability.md>), [airflow](<https://devfeed.tech/tags/airflow.md>), [allows](<https://devfeed.tech/tags/allows.md>), [data-observability](<https://devfeed.tech/tags/data-observability.md>), [data-observability-tools](<https://devfeed.tech/tags/data-observability-tools.md>), [observability](<https://devfeed.tech/tags/observability.md>)

## AI overview

The article outlines criteria for evaluating data and AI observability tools, including security, scalability, setup time, integrations, anomaly detection, root-cause analysis, alerting, operational reporting, and agent monitoring. It also offers an RFP template for organizations comparing solutions.

## Source excerpt

Monte Carlo is rated #1, but we recognize there are alternatives. Here's how the experts evaluate data observability tools.