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What is Orbit AI?

Orbit AI provides observability for AI applications by collecting and analysing real runtime data at the feature level. Rather than treating AI systems as black boxes, it lets you see what your models are actually doing in production, monitor their behaviour, and identify issues before they affect users. The tool is designed for teams building AI features into their applications who need visibility into model performance, data quality, and user interactions. It works by instrumenting your AI code to capture detailed runtime information, then presenting this data in a way that helps you spot patterns, debug problems, and optimise performance. Offer at no cost, Orbit AI aims to make AI observability accessible to smaller teams and those experimenting with AI integration without requiring significant investment upfront.

Key Features

Real runtime data collection

captures what your AI models are actually doing in production, not just theoretical performance

Feature-level monitoring

tracks behaviour at the individual feature level rather than application-wide metrics

Production visibility

lets you see model inputs, outputs, and performance characteristics from real user interactions

Data quality tracking

helps identify issues with the data flowing through your AI systems

Performance analysis

reveals how your models behave across different scenarios and user segments

Pros & Cons

Advantages

  • Free pricing makes it accessible for teams without dedicated observability budgets
  • Focuses specifically on AI systems rather than generic application monitoring
  • Shows real production behaviour rather than relying on test data or simulations
  • Feature-level granularity helps pinpoint exactly where problems are occurring

Limitations

  • Requires code instrumentation, which means additional setup and maintenance for your development team
  • Free tier may have limitations on data retention, volume, or advanced analysis features that aren't publicly detailed

Use Cases

Monitoring chatbot responses to detect when model quality degrades in production

Tracking model performance across different user demographics to spot bias or fairness issues

Debugging unexpected model behaviour by reviewing actual inputs and outputs from real users

Optimising feature performance by analysing which variations work best in practice

Identifying data quality problems before they cause widespread model failures