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Digma

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Coding & Development

Introduction

Uncover code regressions and anomalies from your IDE.

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Digma Product Information

Digma Preemptive Observability Analysis (POA) is a pre-production observability solution that identifies performance, scalability, and reliability issues before they impact production. It analyzes code changes and runtime behavior to highlight affected components, suggests AI-driven fixes, and helps teams shift left toward more reliable software. Designed to complement traditional APMs, Digma focuses on preventing incidents, optimizing performance early, and enabling faster, safer deployments without requiring code changes or external data sharing.

How Digma Works

  1. Preemptive Analysis on code changes: Digma analyzes every code change and pull request to identify potential performance regressions, bottlenecks, and affected components.
  2. OpenTelemetry-friendly integration: It plugs into existing observability stacks (OTEL-compliant) without forcing code modifications.
  3. POA engine insights: The Preemptive Observability Analysis engine detects scalability and performance issues in pre-production and pinpoints the exact lines of code implicated.
  4. AI-driven suggestions: AI proposes fix ideas for inefficient queries, bottlenecks, and risky changes to accelerate remediation.
  5. Sandbox for exploration: A no-install, no-deployment demo environment lets teams explore POA benefits before adopting it in their workflow.
  6. Preventive focus, not just monitoring: By identifying patterns early, Digma helps prevent production incidents and aligns with team SLIs.

Key Benefits

  • Shift-left observability: catch issues early in development and PR stages.
  • Faster debugging: navigate from detected problems to the exact source code.
  • AI-assisted remediation: automated suggestions for performance improvements.
  • No code changes required: works with existing observability setups; OTEL compliant.
  • Cost efficiency: identifies inefficient patterns that drive higher resource usage, reducing infra costs.
  • Local data control: no exposure of data to public AI models; data stays within your environment.
  • Complementary to APMs: enhances, not replaces, traditional monitoring by preventing issues before they reach production.
  • Suitable for teams of all sizes: beneficial for developers, engineers, and managers alike.

Use Cases

  • Detect potential performance regressions introduced by code changes during review.
  • Identify scalability bottlenecks before large-scale deployments.
  • Obtain AI-generated fix suggestions to optimize queries and code paths.
  • Explore observability benefits in a safe sandbox before full adoption.
  • Reduce incident likelihood and time-to-resolution by preemptively addressing hotspots.

Safety and Privacy Considerations

  • No public AI models are used; data remains within your organization.
  • Integrates with existing OTEL-based pipelines without requiring middleware or code changes.
  • Focuses on pre-production and development-stage issues to minimize risk to customers.

Core Features

  • Preemptive Observability Analysis engine to identify performance and scalability issues in pre-production
  • Analysis of every code change and pull request to highlight affected components
  • OpenTelemetry (OTEL) compliant integration with no mandatory code changes
  • AI-driven fix suggestions for inefficient queries and bottlenecks
  • Visualization and navigation from issues to the exact source code
  • Digma Sandbox: a no-deploy, no-install demo environment to explore POA benefits
  • Prevention-focused approach that reduces production incidents and firefighting
  • Cost-aware insights to optimize resource usage and infrastructure footprint
  • Anonymous, local data processing with no sharing of observability data to external services
  • Complementary to traditional monitoring and APMs, extending shift-left capabilities

How It Works (Concise)

  • Analyze code changes and runtime patterns to detect potential performance regressions and bottlenecks.
  • Identify affected components and code areas before merging PRs.
  • Provide AI-generated remediation ideas and optimization opportunities.
  • Allow teams to validate insights in a sandbox before integrating into their workflow.

Technical Details

  • OTEL-compliant integration
  • Local data processing, no external model access
  • Designed to work alongside existing monitoring stacks to enhance early detection and prevention

Pricing / Availability

  • Provided as a tool to help teams prevent issues and optimize performance during development and PR processes. (Refer to official pricing and trials for current options.)