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Gantry

Observability for LLM apps in production.

★★★★★ 4.5 600 saves Starting price: Custom pricing

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Gantry is an observability platform for LLM applications, helping teams log, evaluate and improve model behavior in production. It tracks quality, drift and user feedback on predictions.

AI teams use Gantry to catch regressions, compare model versions and keep assistants and agents performing reliably after launch.

In-depth review

Observability for LLM apps in production. Gantry competes in the AI Model Training category, where our editors have reviewed 294 tools so far. It has been bookmarked by 600 readers, making it one of the most discussed options in its class. This in-depth review breaks down exactly where Gantry shines, where it falls short, and who should choose it.

In real-world use, Gantry's clearest strengths are LLM production monitoring; Evaluation workflows; Drift detection. These advantages matter most for Monitoring LLMs|Evaluating outputs|Detecting drift|Comparing versions|Improving quality — the exact workload Gantry was designed around. Readers who prioritise these qualities consistently rank it among their favourites in AI Model Training.

No tool is perfect — before you commit, weigh LLM-focused, not general ML; Custom pricing for scale. Gantry pricing starts at Custom pricing. Always confirm the latest plans on the official website, because prices change often. With no free tier available, compare the alternatives carefully before spending. Gantry is a solid, well-reviewed choice in AI Model Training and deserves a place on your shortlist.

How Gantry compares

Gantry is rated 4.5 out of 5, below the AI Model Training category average of 4.5 across 294 rated tools. Its closest alternatives are Entry Point AI, Roboflow, Labelbox. Popular alternatives include Entry Point AI, Roboflow, Labelbox. Compare their pricing, ratings, and reviews before you decide.

Pricing

Gantry pricing starts at Custom pricing. Always confirm the latest plans on the official website, because prices change often.