Top 5 Alternatives to Datadog Synthetic Tests for Recorder + Code Testing

Introduction and Context

Synthetic testing has its roots in early uptime checks and scriptable web monitors that verified critical pages, APIs, and user journeys. Over time, teams moved from simple “is it up?” pings to robust, browser-driven end‑to‑end checks. In parallel, tools like Selenium made it practical to automate real browser interactions, while modern DevOps and CI/CD practices turned synthetic testing into a continuous, code‑driven discipline.

Datadog Synthetic Tests emerged in this evolution as part of an observability‑first platform. Its appeal lies in unifying browser and API checks with metrics, logs, traces, and real user monitoring. By combining a recorder‑style experience with code‑level customization, Datadog helped teams build reliable uptime and functional checks that plug directly into CI/CD, SLOs, and alerting.

Why it became popular:

• It integrates with the broader Datadog ecosystem, giving teams a “single pane of glass” for infrastructure, application performance, and synthetic monitoring.

• It supports both browser and API tests, which covers a wide range of web and backend reliability checks.

• It aligns with modern workflows: Teams can trigger tests from CI/CD, run pre‑production checks, and wire results into incident response.

• It supports a recorder plus code approach, enabling faster authoring with the flexibility to customize.

As adoption grew, teams praised Datadog for its broad capabilities and modern integrations. At the same time, some organizations reached a point where they needed deeper specialization—especially in mobile, protocol‑level performance, or low/no‑code authoring with AI assistance. Others reassessed costs, test maintenance overhead, and the fit with their specific tooling stack. That’s why many are now evaluating focused alternatives that better match particular needs.

Top Alternatives Covered in This Guide

Here are the top 5 alternatives to Datadog Synthetic Tests for recorder + code testing and adjacent use cases:

• LoadRunner

• Mabl

• Repeato

• TestCafe Studio

• Waldo

Why Look for Datadog Synthetic Tests Alternatives?

Datadog Synthetic Tests is capable and widely used, but teams often explore alternatives for specific reasons:

• Native mobile coverage: If you need first‑class automation for iOS and Android apps (not just mobile web), a mobile‑first tool can be a better fit.

• Deep performance and protocol testing: Large‑scale load, stress, and protocol‑level testing often require specialized performance tooling.

• Authoring style and maintenance: Some teams prefer strongly guided, low/no‑code authoring with self‑healing to combat flakiness and reduce upkeep.

• Cost and scale: Synthetic checks at scale—particularly cross‑environment and cross‑region—can become expensive; alternative licensing models may suit budgets better.

• Ownership and environment constraints: If you need to run everything on‑premises or within tight network boundaries, a tool with flexible deployment may be preferable.

• Reporting and collaboration needs: Some teams want richer visual validation, change detection, or collaborative IDE experiences that streamline debugging and triage.

• Ecosystem alignment: If your CI/CD, test data management, or device lab standards are already set, you may want a tool that slots into your existing stack with minimal friction.

Deep Dive into the Alternatives

LoadRunner

What it is and who built it: LoadRunner, originally created by Mercury and later owned by Micro Focus (now OpenText), is a mature, enterprise‑grade performance and load testing suite. It’s designed for web, API, and protocol‑level testing across a wide variety of technologies. While it’s not positioned as a pure “recorder + code” browser E2E tool, it does include recording capabilities and supports scripting in proprietary and standard languages to simulate complex workloads.

What makes it different: LoadRunner focuses on performance and scalability at the protocol and system levels. If your primary goal is to stress services and measure system behavior under load, it is specifically engineered for that.

Core strengths:

• Scalable load generation: Simulate large volumes of users and traffic patterns across regions to measure response times, throughput, and resource utilization.

• Broad protocol support: Go beyond basic HTTP/S—cover legacy, enterprise, and protocol‑rich systems.

• Tight integration with monitoring: Pair performance tests with system metrics and APM for correlation and analysis.

• Detailed analysis and reporting: Rich diagnostics and bottleneck identification backed by decades of performance engineering practice.

• Enterprise‑grade ecosystem: Role‑based controls, test asset management, and integration options suitable for large organizations.

How it compares to Datadog Synthetic Tests:

• Focus: Datadog Synthetic Tests emphasizes functional reliability and uptime with browser/API checks, integrated with observability. LoadRunner emphasizes performance and capacity validation at scale.

• Authoring approach: Datadog offers recorder + code for web and API checks. LoadRunner uses record/replay and scripting to model virtual users and protocols, often requiring performance expertise.

• When to choose which: If your mission is to validate peak loads, stress scenarios, and scalability across complex protocols, LoadRunner is the better fit. For continuous functional checks, SLAs/SLOs, and observability‑first workflows, Datadog stands out.

Standout benefits:

• Purpose‑built for high‑scale performance testing.

• Extensive protocol support beyond browser and REST.

Mabl

What it is and who built it: Mabl is a commercial, SaaS‑first end‑to‑end testing platform designed for web and API automation. It blends low‑code authoring with AI‑assisted self‑healing, aimed at reducing flakiness and maintenance overhead. It’s built for product QA and engineering teams who want a modern, cloud‑native testing experience.

What makes it different: Mabl emphasizes low‑code creation of stable E2E tests with automatic waits, intelligent element selection, and self‑healing when the UI changes. It’s designed to speed up authoring while keeping tests resilient.

Core strengths:

• Low‑code authoring with AI‑assisted self‑healing to reduce flaky tests and maintenance.

• Unified UI and API testing, including data‑driven flows and assertions.

• Cloud‑native execution with parallel runs across browsers and environments.

• Built‑in change detection and visual checks for UI regressions.

• CI/CD integration for pre‑merge, pre‑release, and scheduled runs.

• Team‑friendly collaboration features and result triage.

How it compares to Datadog Synthetic Tests:

• Focus: Both support web and API checks, but Mabl leans into test creation, maintainability, and stability for product quality. Datadog synthesizes tests with broader observability and alerting as part of a monitoring stack.

• Authoring: Datadog offers recorder + code in a monitoring context; Mabl’s low‑code approach plus self‑healing often leads to faster authoring and lower flakiness for complex UI.

• Reporting and triage: Mabl includes change detection and visual diffs; Datadog ties more directly to SLOs, incident workflows, and infra/app telemetry.

• When to choose which: Pick Mabl if your main pain point is test creation/maintenance and you want an all‑in‑one E2E platform for web and API testing. Stick with Datadog if tight coupling with observability, SLOs, and synthetic uptime monitoring is your priority.

Standout benefits:

• Self‑healing reduces the day‑two burden of maintaining UI tests.

• Low‑code flows make it easier for non‑specialists to contribute tests.

Repeato

What it is and who built it: Repeato is a commercial, mobile‑first UI testing tool for iOS and Android. It uses computer vision to interact with application screens, aiming to remain resilient to UI changes and reduce reliance on brittle selectors. It supports CI/CD integrations and is built for teams focused on mobile app quality.

What makes it different: Repeato is purpose‑built for native mobile apps and relies on computer vision for robust element identification, which can be more stable across UI updates than tightly coupled DOM/XPath strategies.

Core strengths:

• Mobile‑first coverage for iOS and Android apps, including native and hybrid scenarios.

• Computer vision‑based interactions that can be more resilient to UI changes.

• Codeless authoring that lowers the barrier for mobile QA and product teams.

• CI/CD integration to run tests on schedule or in pipelines.

• Designed for stability on real devices and emulators/simulators.

How it compares to Datadog Synthetic Tests:

• Platform focus: Datadog Synthetic Tests primarily targets web and API checks. Repeato targets native mobile apps—filling a gap when you need first‑class mobile coverage.

• Authoring: Datadog offers recorder + code in a browser/API context, while Repeato focuses on codeless, computer vision‑driven authoring for mobile UI.

• When to choose which: Choose Repeato if mobile app quality and stability are central to your product and you need robust, code‑optional workflows for iOS/Android. Use Datadog when your priority is web/API reliability with observability‑backed monitoring and alerting.

Standout benefits:

• Computer vision approach aims to reduce flakiness in mobile UI tests.

• Streamlines mobile‑specific pipelines where web‑centric tools fall short.

TestCafe Studio

What it is and who built it: TestCafe Studio is the commercial, codeless IDE variant of the popular open‑source TestCafe framework from DevExpress. It targets web E2E testing and is known for running tests without relying on WebDriver, which can simplify setup and improve stability. TestCafe Studio adds a visual test recorder and a desktop IDE to the core framework.

What makes it different: Rather than controlling browsers through WebDriver, TestCafe runs tests directly in the browser context using a Node.js engine. This architecture often results in fewer configuration headaches and more stable execution, especially for modern SPAs.

Core strengths:

• Simple setup with no WebDriver/Selenium dependency.

• Codeless recorder in the Studio IDE for quick authoring, plus the ability to export or augment with code.

• Cross‑browser support with good handling for async behavior and automatic waits.

• Parallel execution and CI/CD integrations for faster feedback cycles.

• Built‑in debugging features and readable error reporting.

How it compares to Datadog Synthetic Tests:

• Focus: Datadog is a monitoring‑centric SaaS for browser/API checks tied to observability. TestCafe Studio is a test authoring and execution tool primarily for development and QA workflows.

• Infrastructure: Datadog runs in the cloud with native dashboards and alerts; TestCafe Studio runs where you host it—locally or in your CI—giving you more control over environments.

• When to choose which: Pick TestCafe Studio if you want to own your E2E test stack, run tests in CI, and prefer a codeless recorder with the option to drop down to code. Choose Datadog if you need a managed synthetic monitoring layer with built‑in alerting and SLO integrations.

Standout benefits:

• Minimal setup and fewer moving parts compared to WebDriver‑based tools.

• Strong for teams that want to keep tests close to the codebase and pipeline.

Waldo

What it is and who built it: Waldo is a commercial, no‑code mobile UI testing platform for iOS and Android. It focuses on fast, recorder‑driven authoring and cloud execution so teams can test mobile apps at scale without managing device infrastructure.

What makes it different: Waldo’s no‑code approach and managed cloud runs make it easy to stand up robust mobile tests quickly. It’s designed for teams that want immediate value without the overhead of scripting or maintaining device farms.

Core strengths:

• No‑code recorder that accelerates mobile test creation for iOS and Android.

• Cloud device infrastructure for parallel, scalable execution.

• Visual verification and diffs for catching regressions in UI/UX.

• CI/CD integration to gate builds and run on every commit or release.

• Collaboration features for sharing results and triaging issues across teams.

How it compares to Datadog Synthetic Tests:

• Platform focus: Waldo is built specifically for native mobile app E2E testing; Datadog focuses on web and API synthetic monitoring.

• Authoring: Waldo is no‑code, while Datadog offers recorder + code primarily for web and API.

• When to choose which: Choose Waldo if your team’s testing needs center on mobile UI with minimal setup. Choose Datadog if you need observability‑driven web/API monitoring and alerting at scale.

Standout benefits:

• Rapid time to value for mobile teams thanks to no‑code authoring and managed infrastructure.

• Strong visual regression capabilities for mobile UI.

Things to Consider Before Choosing a Datadog Synthetic Tests Alternative

• Scope and platforms:

• Authoring model:

• Setup and environment management:

• Execution speed and scale:

• CI/CD integration:

• Debugging and triage:

• Reporting and analytics:

• Security and compliance:

• Ecosystem and community:

• Scalability and reliability:

• Cost and licensing:

Conclusion

Datadog Synthetic Tests remains a strong choice for teams that want web and API checks tied tightly into observability, SLOs, and incident response. Its recorder + code approach, CI/CD integrations, and consolidation with metrics, logs, and traces make it a compelling monitoring‑centric solution.

However, alternative tools can be a better fit depending on your specific goals:

• If you need to validate performance and scalability under heavy load across diverse protocols, LoadRunner is purpose‑built for that job.

• If your priority is low‑maintenance, stable web and API test authoring with self‑healing, Mabl’s low‑code approach can accelerate coverage and reduce flakiness.

• If native mobile apps are core to your product, Repeato and Waldo offer mobile‑first experiences—one emphasizing computer vision resilience (Repeato), the other prioritizing no‑code speed with cloud devices (Waldo).

• If you want to own your E2E stack for the web with a simpler setup and a codeless IDE, TestCafe Studio provides a practical path without WebDriver complexity.

In many teams, the best answer is a blend: keep Datadog Synthetic Tests for observability‑driven monitoring while adopting a specialized alternative for mobile, performance, or authoring efficiency. If you’re standardizing your stack, consider complementing your chosen tool with managed device clouds or cross‑browser grids to simplify infrastructure and accelerate feedback. Ultimately, map your testing requirements—platforms, scale, authoring preferences, and budget—against the strengths of each alternative to arrive at a solution that’s both reliable today and adaptable for tomorrow.