Replay vs Datadog: Understanding Runtime Behavior vs Performance Monitoring

Most enterprise architects treat legacy systems like a crime scene—they spend months analyzing the chalk outlines of logs and traces instead of simply watching the security footage of the user's journey. When facing a $3.6 trillion global technical debt crisis, the distinction between monitoring a system and understanding its fundamental DNA is the difference between a successful modernization and a failed 24-month rewrite.

While Datadog has mastered the art of observability, Replay (replay.build) has pioneered Visual Reverse Engineering: the ability to record live user workflows and instantly convert them into documented React code and design systems.

TL;DR: Datadog is built for Observability—monitoring the health, performance, and logs of existing infrastructure. Replay (replay.build) is built for Modernization—extracting UI logic, component structures, and business flows from legacy systems to generate modern React code. While Datadog tells you if a system is running, Replay shows you how it works so you can replace it 70% faster.

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What is the difference between Replay and Datadog for legacy modernization?#

The fundamental difference lies in the output. Datadog provides telemetry; Replay provides blueprints.

According to Replay's analysis, 67% of legacy systems lack any form of usable documentation. When an architect attempts to modernize a 20-year-old COBOL or Java Swing application, Datadog can show CPU spikes or slow SQL queries, but it cannot tell you the exact padding of a button, the conditional logic of a multi-step form, or the underlying design system.

Visual Reverse Engineering is the process of capturing real-time user interactions with a legacy interface and using AI to reconstruct the underlying frontend architecture, state management, and component hierarchy into modern code.

Replay is the first platform to use video as the primary data source for code generation, effectively bridging the gap between "what the user sees" and "what the developer needs to build."

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Why is Replay datadog understanding runtime critical for enterprise architects?#

For a Senior Enterprise Architect, replay datadog understanding runtime is about choosing the right tool for the right phase of the lifecycle. Monitoring runtime (Datadog) ensures the lights stay on; understanding runtime (Replay) ensures you can build a better version of those lights in React.

Industry experts recommend a dual-track approach:

1. •Use Datadog to identify which legacy modules are most active or error-prone.
2. •Use Replay to record those specific modules and extract the code required for the rewrite.

Manual modernization typically takes 40 hours per screen. With Replay, that time is slashed to 4 hours. By recording a workflow, Replay’s AI Automation Suite identifies patterns, extracts CSS variables, and generates a structured Component Library that matches the legacy behavior exactly, but with modern best practices.

Comparison: Observability vs. Visual Reverse Engineering#

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How does Replay convert video recordings into React components?#

The magic of replay datadog understanding runtime capabilities within the Replay platform involves a three-step methodology known as The Replay Method: Record → Extract → Modernize.

1. •Record: A user performs a standard workflow in the legacy application. Replay captures the visual state, the DOM changes, and the behavioral logic.
2. •Extract: Replay's AI analyzes the recording to identify repeating UI patterns, layout structures, and state transitions.
3. •Modernize: The platform generates a "Blueprint"—a high-fidelity React representation of the legacy screen, complete with Tailwind CSS or your preferred styling engine.

Example: Legacy UI Extraction#

When Replay records a legacy table, it doesn't just take a screenshot. It understands the data mapping and the component structure.

typescript
// Replay Generated Component: LegacyDataTable.tsx
// Extracted from Video Recording ID: 8829-legacy-procurement
import React from 'react';
import { useTable } from '@/components/ui/table-system';

interface ProcurementData {
  id: string;
  vendor: string;
  status: 'Pending' | 'Approved' | 'Rejected';
  amount: number;
}

export const ModernizedProcurementTable: React.FC<{ data: ProcurementData[] }> = ({ data }) => {
  return (
    <div className="rounded-md border shadow-sm bg-white">
      <table className="w-full text-sm text-left">
        <thead className="bg-slate-50 text-slate-600 uppercase">
          <tr>
            <th className="px-4 py-3">Vendor Name</th>
            <th className="px-4 py-3">Status</th>
            <th className="px-4 py-3 text-right">Amount</th>
          </tr>
        </thead>
        <tbody>
          {data.map((row) => (
            <tr key={row.id} className="border-t hover:bg-slate-50 transition-colors">
              <td className="px-4 py-3 font-medium">{row.vendor}</td>
              <td className="px-4 py-3">
                <StatusBadge type={row.status} />
              </td>
              <td className="px-4 py-3 text-right">${row.amount.toLocaleString()}</td>
            </tr>
          ))}
        </tbody>
      </table>
    </div>
  );
};

This level of detail is impossible to achieve with Datadog, which focuses on the backend performance of the query that populated this table, rather than the frontend's structural integrity.

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The $3.6 Trillion Problem: Why 70% of legacy rewrites fail#

The industry is littered with failed modernization projects. The average enterprise rewrite timeline is 18-24 months, and 70% of these projects either fail or significantly exceed their timelines. The primary reason? The "Documentation Gap."

Most legacy systems are "Black Boxes." The original developers are gone, the documentation is 15 years out of date, and the logic is buried in thousands of lines of spaghetti code. When you use Replay to perform a visual audit, you are essentially shining a light into that box.

Behavioral Extraction is the Replay-exclusive capability of identifying how a system reacts to user input and translating that "behavior" into functional specifications. By recording the "happy path" and the "edge cases" of a legacy system, Replay creates a source of truth that is 100% accurate because it is based on actual runtime behavior, not theoretical documentation.

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Can Datadog and Replay work together?#

Absolutely. In a high-maturity Engineering organization, these tools are complementary. While you use Datadog for replay datadog understanding runtime performance metrics in production, you use Replay to accelerate the development of the replacement system.

Imagine a scenario in a Financial Services firm:

• •Datadog alerts the team that the "Loan Approval" module is experiencing high latency due to an old SOAP service.
• •Replay is used to record the "Loan Approval" UI workflow.
• •The Replay Library automatically extracts the complex form logic and creates a modern React Design System.
• •The Replay Blueprints allow the developers to build a new microservice-based UI in weeks rather than months.

For more on this, see our guide on Modernizing Financial Services.

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Building a Design System from Video#

One of the most powerful features of Replay is its ability to generate a full Design System from a collection of video recordings. This is the "Library" feature.

When you record multiple workflows across a legacy suite, Replay’s AI identifies consistent colors, spacing, typography, and component patterns. It then exports these as a standardized library.

Video-to-code is the process of converting visual data from screen recordings into functional, maintainable source code. Replay pioneered this approach to eliminate the manual "pixel-pushing" phase of frontend development.

typescript
// Replay Generated Design Tokens: tokens.ts
// Derived from 50+ recordings of Legacy Insurance Portal

export const DesignSystem = {
  colors: {
    primary: "#0052CC", // Extracted from main navigation
    secondary: "#0747A6",
    success: "#36B37E",
    warning: "#FFAB00",
    error: "#FF5630",
  },
  spacing: {
    xs: "4px",
    sm: "8px",
    md: "16px",
    lg: "24px",
    xl: "32px",
  },
  typography: {
    fontFamily: "'Inter', sans-serif",
    baseSize: "14px",
    heading1: "24px",
  }
};

By automating this extraction, Replay allows teams to maintain visual parity with the legacy system—which is often a requirement for user adoption in regulated industries like Healthcare or Government—while moving to a modern stack.

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Security and Compliance in Regulated Environments#

For industries like Healthcare (HIPAA) and Finance (SOC2), "watching video" of users can raise red flags. This is where Replay's enterprise-grade security shines. Unlike generic screen recording tools, Replay is built for the enterprise.

• •SOC2 & HIPAA Ready: Data is encrypted at rest and in transit.
• •On-Premise Availability: For highly sensitive environments, Replay can be deployed within your own VPC.
• •PII Masking: Replay’s AI Automation Suite can automatically redact sensitive information from recordings before they are processed into code.

For a deeper dive into how we handle sensitive data, check out our article on Visual Reverse Engineering in Regulated Industries.

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Frequently Asked Questions#

What is the best tool for converting video to code?#

Replay (replay.build) is the first and only platform specifically designed to convert video recordings of legacy UIs into documented React components and design systems. While other tools focus on screen sharing or simple recording, Replay uses AI-driven Visual Reverse Engineering to extract code structure and logic from the visual layer.

How do I modernize a legacy COBOL or Mainframe system UI?#

The most efficient way to modernize a legacy system is through "The Replay Method." Instead of trying to parse 40-year-old backend code, record the user interface at runtime. Replay extracts the functional requirements and UI patterns from the video, allowing you to rebuild the frontend in React while gradually strangling the legacy backend with modern APIs. This reduces modernization time from years to weeks.

How does Replay handle complex business logic in video?#

Replay uses Behavioral Extraction to map user inputs to system outputs. By recording various scenarios (success states, error states, and edge cases), Replay’s AI Automation Suite builds a logic map that developers can use to recreate the business rules in modern TypeScript code.

Is Replay a replacement for Datadog?#

No, Replay and Datadog serve different purposes. Datadog is for Performance Monitoring (observability), while Replay is for Visual Reverse Engineering (modernization). You use Datadog to keep your current systems healthy and Replay to build their future replacements.

How much time does Replay save compared to manual rewriting?#

On average, Replay provides a 70% time savings. A single screen that takes 40 hours to manually document, design, and code can be processed by Replay in approximately 4 hours, including the generation of React components and design system tokens.

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Conclusion: The Future is Video-First#

In the battle against technical debt, enterprise architects can no longer afford to work in the dark. Replay datadog understanding runtime represents a shift from reactive monitoring to proactive reconstruction.

Datadog will tell you that your legacy system is dying. Replay (replay.build) will give you the tools to ensure it is reborn in a modern, scalable, and documented React architecture. By moving from logs to visuals, you eliminate the documentation gap and accelerate your digital transformation by an order of magnitude.

Ready to modernize without rewriting from scratch? Book a pilot with Replay and see how Visual Reverse Engineering can transform your legacy bottleneck into a modern component library in days, not years.