7 Best Synthetic Monitoring Tools for Engineering Teams

A checkout flow breaks in the middle of the night, but nobody notices until a customer files a support ticket the next morning. By then, the damage is done: lost revenue, a support queue backing up, and an engineering team scrambling to reconstruct what happened overnight. Synthetic monitoring tools close that gap by running automated scripts that mimic real users or API calls on a schedule.  These checks range from simple uptime monitoring to multi-step transactions, such as logging in and completing a purchase.

The category splits into two real approaches. Some tools are built for code-native testing workflows where you write test scripts, run them in CI/CD, and get fast feedback close to your codebase. Others are full-stack platforms where a failing synthetic check sits next to the trace, deploy, and infrastructure event that explains why it failed. Neither approach is wrong, but they solve different problems, and choosing incorrectly means either drowning in false alarms or spending your on-call rotation guessing at root cause.

This guide compares seven popular synthetic monitoring tools, breaks down what separates a useful tool from a checklist of monitor types, and shows how to match a tool to the stack you already run.

The best synthetic monitoring tools for engineering teams

Before comparing vendors, it helps to agree on what "good" looks like, especially since downtime isn't cheap. ITIC's 2024 Hourly Cost of Downtime survey found that hourly downtime costs exceed $300,000 for more than 90% of mid-size and large enterprises, and 41% of enterprises report hourly costs of $1 million or more. That's the cost proactive monitoring avoids, which is why the criteria below matter more than any vendor's feature list.

Use these four criteria to evaluate any synthetic monitoring tool:

• Monitoring types supported: Does the tool cover basic uptime checks (HTTP monitors and ping monitors), browser tests, API monitoring, and multi-step transaction monitoring that mimic real user flows?
• Correlation with backend telemetry: Does a failed check link to the APM trace, log, or infrastructure event that explains it?
• Monitoring-as-code and CI/CD support: Can you write, version, and run test scripts as part of your deployment pipeline?
• Pricing model: Is the tool priced per check and per location, or is it bundled into a broader observability platform?

Here's how seven commonly evaluated synthetic monitoring tools stack up.

New Relic

New Relic runs synthetic monitoring, including uptime checks, browser tests, API monitoring, and multi-step transaction monitoring, as part of its broader observability platform rather than as an add-on. Every synthetic check ties into the same entity map as your APM, infrastructure, and real user monitoring data, so a failed check leads straight to the trace or infrastructure event behind it.

Key features and benefits:

• Global check locations with device and browser simulation for consistent browser tests
• Synthetic checks correlate directly with APM, logs, and infrastructure telemetry for faster root-cause analysis
• Built-in alerting that routes into New Relic's AI-assisted incident workflows, including PagerDuty integrations, to help lower MTTR

The trade-off: Monitoring-as-code support is less mature than a developer-first tool like Checkly, so teams managing every check as version-controlled test scripts should expect more manual setup.

Best for: Teams running (or planning) full-stack observability that want a synthetic failure to point at the business outcome it affects, like a page-load regression tied to a drop in conversions, rather than a plain red-or-green status check. See how New Relic's synthetic monitoring fits into the rest of the platform.

Datadog

Datadog offers synthetic monitoring, including uptime, API, and browser tests, as one module inside a much larger observability suite spanning APM, infrastructure monitoring, log management, and more.

Key features and benefits:

• Broad monitor types, including HTTP monitors, API tests, and multi-step browser tests
• Strong integration with Datadog's own APM and infrastructure monitoring

The trade-off: Datadog sells its telemetry types as separate products, so synthetics, APM, and infrastructure monitoring often carry their own line items and usage limits, making total cost harder to predict as check volume grows. Teams often raise this when they compare New Relic to Datadog on total cost of ownership.

Best for: Teams already deep in the Datadog ecosystem that want synthetics to plug into dashboards and alerts they've already built.

Dynatrace

Dynatrace built its reputation on automated root-cause analysis, and its synthetic monitoring inherits that strength. Its AI engine analyzes failed checks alongside topology data to point at a likely cause instead of leaving an engineer to trace it by hand.

Key features and benefits:

• Automated root-cause analysis that correlates synthetic failures with application and infrastructure topology
• Broad monitor types, including browser tests, API monitoring, and DNS monitoring
• Strong support for large, complex enterprise environments

The trade-off: Dynatrace's depth comes with a steeper learning curve and pricing that tends to favor larger enterprise budgets.

Best for: Large enterprises that need automated root-cause analysis across a sprawling service topology and have the budget to use it.

Checkly

Checkly is a developer-first synthetic monitoring tool built around monitoring-as-code. You write checks as Playwright scripts that live in your codebase, run in CI/CD, and version like any other code.

Key features and benefits:

• A native monitoring-as-code workflow: write an API test or browser test in JavaScript or TypeScript and manage it through git
• Strong support for multi-step user flows alongside standard API monitoring
• Runs checks in CI/CD pipelines, so a bad deploy can fail a synthetic test before it reaches production

The trade-off: Checkly doesn't correlate a failing check with APM traces or infrastructure metrics on its own, so many teams run it alongside a broader platform rather than in place of one.

Best for: Teams whose top priority is code-native, CI/CD-embedded testing that behaves like any other automated test in their pipeline.

Site24x7

Site24x7, from Zoho, is a broad, budget-friendly monitoring suite that covers uptime monitoring, server monitoring, network monitoring, and application monitoring in one.

Key features and benefits:

• A wide range of monitor types, including HTTP monitors, DNS monitoring, uptime monitoring, and server monitoring
• A lower cost of entry than most full-stack observability platforms

The trade-off: APM and root-cause analysis capabilities are less mature than dedicated observability platforms, and the interface shows its age.

Best for: Smaller teams or budget-constrained IT departments that want broad monitoring coverage without paying for a full observability platform.

Pingdom

Pingdom, from SolarWinds, is one of the most recognized names in uptime monitoring, known for being fast to set up and easy to read.

Key features and benefits:

• Simple, reliable uptime monitoring and page speed checks from multiple global locations
• A real user monitoring add-on for teams that want passive monitoring alongside active monitoring

The trade-off: Pingdom is built for straightforward uptime and page speed checks, not multi-step transaction monitoring or deep API monitoring. Teams that need to test full user flows, like a login-to-purchase sequence, typically outgrow it.

Best for: Teams that mainly need dependable uptime monitoring and basic performance testing without much configuration.

Catchpoint

Catchpoint focuses on network-level visibility, with strong DNS monitoring, CDN monitoring, and last-mile testing across ISPs and geographies.

Key features and benefits:

• Deep network monitoring, including DNS monitoring and CDN performance monitoring, isolates whether an issue is application-side or network-side
• Offers synthetic transaction monitoring from a wide range of global vantage points, which is useful for reachability testing on specific ISPs
• Strong reporting for service level agreements tied to network performance

The trade-off: Since Catchpoint was designed for teams with real network complexity, like global media or financial services companies, smaller teams may find it's more than they need in cost and setup time.

Best for: Enterprises where network performance, CDN behavior, and DNS resolution are frequent sources of incidents alongside application code.

Vendor comparisons also surface tools like Zabbix (open-source server monitoring) or Better Stack (uptime monitoring paired with incident management), but neither is built as a dedicated synthetic monitoring tool with browser tests and multi-step transaction monitoring like the options above.

What to look for in a synthetic monitoring tool

The features above matter less as a checklist and more as answers to these questions: Will this tool catch a real incident before your customers do, and will it tell you enough to fix it fast? Three criteria decide that in practice.

Global check locations and device coverage

Running synthetic tests from a single location tells you a page is reachable to you, not to the users spread across the regions you actually serve. Look for a broad set of check locations and the ability to simulate different devices, browsers, and connection speeds, since latency and response times differ between a user near your servers and one on mobile data across the world.

Reachability matters too: confirming a domain resolves and a CDN endpoint responds from multiple regions catches DNS and network issues before they become support tickets. Pairing these active monitoring checks with real user monitoring, a form of passive monitoring, rounds out the picture. Synthetic tests catch a broken flow before anyone hits it, while real user monitoring shows how it performs for actual visitors.

Correlation with APM and infrastructure data

A failed synthetic check is a symptom, not a diagnosis. What matters is what happens in the minute after it fires. Can your team see the APM trace, the spike in error rates, or the infrastructure event (a bad deploy, a maxed-out database connection pool) behind it, in one tool, without pivoting across three dashboards? Or does the alert stop at "check failed," leaving someone to reconstruct the timeline by hand?

That's the difference between synthetic monitoring as a standalone signal and synthetic monitoring as part of a broader application performance monitoring and observability platform strategy. Teams starting from default cloud-native tooling like AWS CloudWatch should see how a dedicated APM and synthetics platform compares to CloudWatch before scaling synthetic checks across a growing service footprint.

Response times matter too. A B2B site that loads in one second can convert up to three times more visitors than one that takes five seconds, according to a Portent study cited by HubSpot. A tool that only reports "the page loaded," without breaking down DNS resolution, server response, or asset loading, won't tell you which part of that response time to fix.

CI/CD and monitoring-as-code support

If your team treats infrastructure as code, ask whether synthetic checks get the same treatment. Monitoring-as-code means the test scripts behind your synthetic tests live in your repo, get reviewed in pull requests, and run automatically in CI/CD, the same way unit tests or load testing scripts do. That matters most for teams doing frequent deployments that want a broken user flow caught in staging instead of production.

Not every team needs this level of integration. If the priority is business-level visibility, such as whether the checkout flow is up or a service level agreement is being met, a platform-native feature with simpler setup serves better than a fully code-native tool.

Standalone tool or platform feature: what actually matters

Teams already running full-stack observability generally get more value adding synthetics to that platform than standing up a separate tool. A failing check lands next to the trace, deploy, and infrastructure event that explains it, which shortens the investigation instead of starting it from zero. This keeps alerting and incident management in a single workflow, routed through a tool like PagerDuty, instead of forcing you to triage two separate alert streams.

Teams whose primary need is code-native, CI/CD-embedded testing, not full-stack correlation, are often better served by a developer-first tool like Checkly, sometimes running it alongside a broader platform rather than replacing one with the other. Checkly catches regressions in the deploy pipeline, while a platform like New Relic or Datadog handles ongoing proactive monitoring and root-cause analysis for production traffic.

Synthetic checks are also one piece of a bigger picture. Once a check confirms a user flow is broken, session replay shows what an actual user saw, and a broader digital experience monitoring strategy ties synthetic tests, real user monitoring, and session data together instead of treating them as individual dashboards.

Choosing the right fit for your stack

Every comparison in this guide comes down to one decision: proactive detection in isolation or proactive detection correlated with root cause. A standalone synthetic tool answers "Is it up?" A full-stack platform answers "Is it up, and if not, why, and what should we do about it?" Both are valid starting points, but only one tends to reduce mean time to resolution as your stack and service level agreements grow increasingly complex, much like how engineering teams lower MTTR by connecting monitoring data.

If your team is evaluating synthetic monitoring tools as part of a broader move toward proactive monitoring and lower MTTR, test that correlation on your own applications rather than taking a vendor's word for it. Request a New Relic demo to see synthetic checks, APM, and infrastructure data in one view.