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Digma AI

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Introduction

Continuous feedback for developers

Added on: Feb 12, 2026

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Digma AI Product Information

Digma AI Overview

Digma is an AI-powered continuous feedback platform for developers that analyzes runtime data from your application and surfaces performance issues, errors, and code quality problems directly in your IDE as you work. Rather than waiting for problems to be reported by users or discovered in monitorin...

This product stands out with features such as:

•IDE Integration: See runtime performance data directly in your development environment
•Continuous Feedback: Ongoing analysis of how your code performs in production
•Performance Issue Detection: Automatically identify slow code paths and bottlenecks
•Error Tracking: Surface errors and exceptions linked to specific code locations
•Code Quality Insights: Identify code that frequently causes production issues
•Team Visibility: Share performance insights across the development team
•Multiple Languages: Supports Java, Python, and other major languages
•OpenTelemetry Integration: Works with standard observability data formats

How to Use Digma Ai

Get started in a few simple steps

Connect Your Application

Install the Digma IDE plugin and connect it to your application's observability data using OpenTelemetry. Digma begins analyzing runtime behavior and linking it to your source code.

See Runtime Insights in IDE

As you open files and work on code, Digma displays performance metrics, error rates, and quality insights directly in your editor alongside the code they relate to.

Act on Continuous Feedback

Use the insights to identify and fix performance issues before they escalate. Digma surfaces problems proactively rather than waiting for production incidents or user complaints.

Digma AI's Core Features in Detail

Powerful features from Digma AI

Runtime Context in the Editor

The gap between writing code and understanding its production behavior is a significant source of technical debt. Digma brings runtime data into the editor so developers have performance context while they are writing and modifying code

Proactive vs Reactive

Most teams discover performance problems after they have already impacted users. Digma's continuous feedback surfaces issues before they reach that stage

Code-to-Production Link

Connecting specific lines of code to their production performance metrics gives developers a much clearer understanding of what their changes actually do to system behavior

Developer-First Observability

Traditional observability tools are designed for operations teams. Digma surfaces the same data in a format and location designed specifically for developers

Digma AI Use Cases

Discover how Digma AI can benefit different users

Development Teams Caring About Performance

Engineering teams that prioritize performance and want developers to understand the production impact of their code use Digma to make runtime data a standard part of the development workflow

Teams Reducing Technical Debt

Organizations working to reduce accumulated technical debt use Digma to identify the specific code that causes the most production problems and prioritize improvement work

DevOps-Oriented Development Teams

Teams practicing DevOps principles use Digma to bring operational feedback into the development process rather than keeping dev and ops concerns separated

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