Best AI Observability Tools for Gemini 2.0
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Compare the Top AI Observability Tools that integrate with Gemini 2.0 as of September 2025
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This a list of AI Observability tools that integrate with Gemini 2.0. Use the filters on the left to add additional filters for products that have integrations with Gemini 2.0. View the products that work with Gemini 2.0 in the table below.
What are AI Observability Tools for Gemini 2.0?
AI observability tools provide deep insights into the behavior, performance, and reliability of AI models in production environments. They monitor model outputs, data inputs, and system metrics to detect anomalies, biases, or drifts that could impact decision-making accuracy. These tools enable data scientists and engineers to trace errors back to their root causes through explainability and lineage features. Many platforms offer real-time alerts and dashboards to help teams proactively manage AI lifecycle health. By using AI observability tools, organizations can ensure their AI systems remain trustworthy, compliant, and continuously optimized. Compare and read user reviews of the best AI Observability tools for Gemini 2.0 currently available using the table below. This list is updated regularly.
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1
Langtrace
Langtrace
Langtrace is an open source observability tool that collects and analyzes traces and metrics to help you improve your LLM apps. Langtrace ensures the highest level of security. Our cloud platform is SOC 2 Type II certified, ensuring top-tier protection for your data. Supports popular LLMs, frameworks, and vector databases. Langtrace can be self-hosted and supports OpenTelemetry standard traces, which can be ingested by any observability tool of your choice, resulting in no vendor lock-in. Get visibility and insights into your entire ML pipeline, whether it is a RAG or a fine-tuned model with traces and logs that cut across the framework, vectorDB, and LLM requests. Annotate and create golden datasets with traced LLM interactions, and use them to continuously test and enhance your AI applications. Langtrace includes built-in heuristic, statistical, and model-based evaluations to support this process.Starting Price: Free -
2
Galileo
Galileo
Models can be opaque in understanding what data they didn’t perform well on and why. Galileo provides a host of tools for ML teams to inspect and find ML data errors 10x faster. Galileo sifts through your unlabeled data to automatically identify error patterns and data gaps in your model. We get it - ML experimentation is messy. It needs a lot of data and model changes across many runs. Track and compare your runs in one place and quickly share reports with your team. Galileo has been built to integrate with your ML ecosystem. Send a fixed dataset to your data store to retrain, send mislabeled data to your labelers, share a collaborative report, and a lot more! Galileo is purpose-built for ML teams to build better quality models, faster.
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