Skip to main content

Introduction to BitDive

What is BitDive?​

BitDive is a distributed tracing and runtime observability platform specifically designed for Java, Spring, and Kotlin applications. It captures real runtime behavior including method execution, parameters, return values, SQL/REST calls, and distributes traces across services. Designed for developers, testers, SDETs, and DevOps, BitDive automates performance analysis, regression validation, and AI-powered fixes.

Unlike traditional profiling tools that require complex setup or cause performance overhead, BitDive works with a simple Maven dependency and minimal configuration. No code changes, no JVM restarts, no complex instrumentation.

Why BitDive?​

The Problem: Traditional monitoring tools show you what happened, but not why. AI development tools can see your code, but not how it actually behaves in production. This creates a gap between static code analysis and runtime behavior, leading to time wasted debugging issues without proper context, AI suggestions that miss the mark, and difficulty validating that fixes actually work.

The Solution: BitDive bridges this gap by providing complete runtime intelligence through its unique architecture. The platform combines distributed tracing with runtime observability, delivering enriched trace context to both humans and AI tools via the Model Context Protocol (MCP). Learn more about how BitDive compares to traditional profilers.

Architecture Overview​

BitDive's architecture consists of five core components working together seamlessly. The BitDive Agent is added as a Maven dependency with no code changes required, supporting Java, Spring Boot, and Kotlin applications. The Trace Backend collects and indexes traces in real time, while the Model Context Protocol (MCP) delivers enriched trace context to IDEs and LLMs via SSE-based protocol. GitLab Integration allows navigation from trace to source code using REST API v4, and the Replay Engine re-executes method chains from production traces in controlled environments.

For a detailed overview of the complete system design, see our System Architecture guide.

How BitDive Works​

Simple Setup​

Add the BitDive agent as a Maven dependency to your Java, Spring, or Kotlin application, include a simple YAML configuration file in your resources, and start your application. BitDive automatically begins capturing runtime data with minimal overhead.

Get started with our Quick Start Guide or learn about advanced configuration options.

Complete Observability Loop​

BitDive enables a complete workflow from problem detection through validated solutions. When errors or slowdowns occur in runtime, the platform captures method-level detail for root cause analysis via UI or MCP to AI. This leads to AI-suggested or human patches, followed by replay and validation with identical input to verify the fix, and auto test generation based on real trace paths.

Real-World Impact​

Consider this actual case: A trace revealed excessive SQL calls from FetchType.EAGER, causing performance issues. The fix involved switching to LAZY + @EntityGraph. Using BitDive's replay capability, the team verified the solution with a new trace, resulting in 99.6% reduction in SQL volume and 86% latency improvement.

Key Capabilities​

Distributed Tracing captures full call chains across services with method parameters, results, exceptions, and timings, visualized in an interactive Service Map with performance indicators including call count, average latency, and error rates. Explore our guides on Distributed Tracing, Method-Level Tracing, and Service Maps.

Runtime Observability monitors method-level performance over time without sampling - all invocations are tracked with minimal overhead, providing aggregated views by class, method, and package through the Performance Explorer interface. Learn more about Performance Explorer, JVM Metrics, and HeatMap Dashboard.

AI Integration supercharges development tools like Cursor IDE, ChatGPT, and Copilot through MCP APIs including findTraceAll, findTraceForMethod, and findTraceForMethodBetweenTime, enabling code lookup, root cause analysis, and automatic code suggestions based on real runtime behavior. See our MCP Integration Guide and learn how to analyze services with Cursor.

Live Context Replay captures traces with full runtime context including database and REST calls, then re-executes them in test/stage environments to compare behavior and validate changes. This enables regression validation, safe refactoring, and fault reproduction.

Visual Analytics includes HeatMaps that visualize performance pressure per service with color-coding for high latency, frequent errors, and long database/REST operations, plus drill-down interfaces for finding bottlenecks and navigating from service to method to line. Explore the HeatMap Dashboard and Service Map features.

Who Benefits from BitDive?​

Java, Spring, and Kotlin developers gain understanding of production issues with full context, while AI-Enhanced Development Teams can give their tools the runtime intelligence they need for accurate suggestions. DevOps Engineers detect and resolve performance degradations faster, Test Engineers automate regression testing with real production scenarios, and Engineering Leaders improve application reliability and development velocity.

What Makes BitDive Different?​

BitDive works out of the box with no manual instrumentation required for Java, Spring Boot, and Kotlin applications. It captures full call and context data, not just spans or metrics, making it ideal for monoliths, microservices, legacy and modern stacks alike. The platform integrates tightly with IDEs and CI/CD pipelines, enabling automatic replay of key scenarios post-deployment with GitLab UI/CLI integration for developer feedback.

The platform is production-safe with minimal overhead designed for continuous operation, and includes robust security features. Replay functionality is only enabled on test/staging environments, callId access requires authentication, sensitive data is redacted in MCP by default, and the system supports encrypted channels via HTTPS/SSE.

For a detailed comparison with traditional tools, read our BitDive vs Traditional Profilers analysis.

AI and LLM Applications​

BitDive enables advanced AI applications including root cause analysis and RCA summaries, predictive SLA and latency warnings, smart code review and test generation, onboarding assistance for new engineers, digital twin capabilities for load testing, and automated error reproduction with suggested fixes.

Next Steps​

Ready to supercharge your Java, Spring, or Kotlin application observability and AI development workflow?

👉 Get Started - Set up BitDive in minutes
👉 Deploy Infrastructure - Deploy the backend platform
👉 AI Integration - Connect with AI development tools
👉 Watch Demo - See BitDive in action

Explore Key Features:

Transform your development experience with runtime intelligence that both you and your AI tools can understand.