Checkly vs Datadog vs Dynatrace vs New Relic vs Elastic
A software release changes how new customers create their first workspace. The application remains online, the database responds, and the engineering dashboard looks normal. But the final onboarding step no longer saves. A prospect who cannot reach that first useful moment may leave before opening a support ticket.
Synthetic monitoring gives a SaaS team a way to check that journey on a schedule. The buying decision extends beyond whether a platform can automate a browser. Someone must maintain the check, interpret failures, and pay for the coverage as the product grows.
Checkly, Datadog, Dynatrace, New Relic, and Elastic offer different starting points. A team with developers maintaining browser tests should weigh code ownership heavily. A company with an established monitoring platform should examine how easily a failed journey reaches the people already investigating incidents. The most useful shortlist follows those operating conditions.
What the monitoring service needs to prove
A synthetic check executes a defined request or sequence of actions and evaluates the result. For a SaaS application, that might mean signing into a dedicated account, creating a test workspace, and confirming that the workspace appears after a page refresh.
The last assertion matters. A script that finishes clicking through a form has not necessarily established that the application saved anything. Buyers should define the business outcome before discussing recording tools or dashboards.
These checks cover the paths, accounts, and locations that a team configures. They cannot establish that every customer has a good experience. Real-user telemetry and application diagnostics remain useful for understanding failures outside those controlled scenarios. Elastic’s synthetic monitoring documentation explains the distinction between lightweight endpoint checks and browser journeys.
For a broader review of the five products, StackBriefly’s synthetic monitoring comparison covers their capabilities and tradeoffs. The buying question here is how each approach fits the people who will operate it after the initial setup.
The quick difference
| Tool | Team fit | What to prove in a trial |
|---|---|---|
| Checkly | Developers who maintain Playwright tests | A product change and its monitor can be reviewed together |
| Datadog | Teams already using Datadog for incident investigation | A failed browser step leads to useful application evidence |
| Dynatrace | Operations teams covering public and internal applications | The proposed setup reaches the required networks and workflows |
| New Relic | Teams comfortable with Selenium scripting | A representative journey works within the supported runtime |
| Elastic | Teams operating Elastic Observability | Monitor management and execution fit the existing deployment |
These are starting points for evaluation, not performance rankings.
Checkly for teams that maintain tests with application code
Checkly’s synthetic monitoring product supports Playwright browser checks, API checks, and monitoring definitions managed in code. It also provides evidence such as screenshots and traces to help investigate failures.
That approach deserves attention when engineers already review tests alongside product changes. For example, an onboarding redesign can include both the updated application and the assertions that confirm a new workspace exists. Keeping those changes in the same review process makes ownership explicit.
The tradeoff is developer time. Reusing a test does not automatically make it suitable for production. A test written for a disposable development database may create unwanted records when scheduled against live software. During a trial, have the intended owner adapt one real test, including account setup and cleanup, and record the work required.
Datadog for teams connecting detection with investigation
Datadog’s Synthetic Monitoring includes a browser recorder and API testing. Its integrations with application performance monitoring and real-user monitoring can connect failed tests with other diagnostic information.
For an existing Datadog customer, the useful evaluation is a complete incident. Trigger a controlled failure in a test environment, let the alert reach the responsible engineer, and examine whether that person can identify the affected service using the available evidence. This reveals more than watching someone record a successful journey.
The tradeoff is dependency on the surrounding setup. Useful correlation requires the relevant instrumentation, configuration, and access. A browser test alone does not establish that the team has everything shown in a full-platform demonstration. Ask which capabilities the proposal includes and price the intended combination.
Dynatrace for applications across public and private networks
Dynatrace’s Synthetic Monitoring Classic documentation describes single-URL browser monitors, browser clickpaths, HTTP monitors, and execution from private locations. It also describes licensing through consumption of synthetic actions and requests.
This makes network placement an important part of evaluation. A SaaS company may need to check a public customer portal and a staff application that is accessible only internally. The proposal should identify how both will be reached and who will operate any private execution infrastructure.
The tradeoff is configuration and commercial detail. Confirm which Dynatrace experience is included; features documented under Classic should not be assumed to apply identically elsewhere. Ask the vendor to model a representative multistep journey, including its locations and schedule, against the applicable consumption rules.
New Relic for teams prepared to own browser scripts
New Relic’s scripted browser monitors use Selenium WebDriver. Scripts can navigate an application, perform actions, and check that expected elements are present.
For a team already using New Relic, this is a reasonable candidate when someone can maintain the browser scripts. Familiarity with JavaScript helps, but teams bringing tests from another framework should evaluate the actual conversion effort. Playwright and Selenium do not use interchangeable automation APIs.
The tradeoff is the script lifecycle. Login behavior, page structure, and runtime constraints can affect a scheduled journey. New Relic’s documentation specifies a three-minute maximum runtime for scripted browser monitors. Include the slowest realistic version of the proposed journey in the trial, rather than assuming that a short demonstration represents production behavior.
Elastic for teams with an established observability environment
Elastic Synthetics combines lightweight HTTP, TCP, and ICMP monitors with browser-based journeys. Its documentation describes management through the Synthetics interface or a Synthetics project, with execution on managed infrastructure or private locations.
For an Elastic team, the decision should include where monitor definitions belong. An operations group may prefer managing checks through the interface, while developers may want a project that follows their review process. Choose a clear owner for each monitor so changes do not become ambiguous.
The tradeoff is operational responsibility, particularly with private execution. Establish who maintains the runner and detects its failure. Also keep the scope clear: Elastic explicitly states that the Synthetics interface does not provide infrastructure or Kubernetes autodiscovery. Browser journey coverage and dynamic infrastructure monitoring address different requirements.
Compare the cost of the same coverage
Build a small coverage model before requesting a quote. Suppose a team schedules 20 journeys every five minutes from three locations. Over a 30-day month, that represents 518,400 scheduled executions: 20 multiplied by three locations, 12 runs per hour, 24 hours, and 30 days.
That is an illustrative workload, not a bill estimate. Vendors can count browser runs, actions, requests, retries, and other usage differently. Ask each supplier to translate exactly the same workload into its own charging model. Include the cost of any additional products needed for the proposed investigation workflow.
Then add the work inside the company. Estimate the time needed to maintain tests after releases, investigate unreliable checks, rotate credentials, and operate private runners. A lower subscription price may offer little advantage if the team cannot keep the monitors dependable.
Frequency should follow the consequence of a failure. A critical sign-in journey and an occasional settings change do not necessarily need identical schedules. Discuss which failures require fast notification before multiplying every check across every location.
Use a trial that exposes maintenance work
A useful trial should span at least one application change. The team should see how the monitoring setup behaves when the interface changes, when a test account loses access, and when the application returns an incorrect result.
Record whether the resulting alerts help distinguish a customer problem from a broken test. Measure the time needed to understand and repair the check. These observations provide a more practical basis for selection than the number of monitors created on the first day.
Before selecting a platform, confirm:
- A named person owns each critical journey and its test data.
- The journey checks a meaningful result without creating unintended customer activity.
- An alert contains enough evidence for the intended responder.
- The quotation reflects the planned schedule and execution locations.
Checkly is a useful starting point for a Playwright-oriented engineering team. Datadog deserves attention when its incident investigation workflow is already established. Dynatrace warrants evaluation where application coverage spans public and private networks. New Relic fits a shortlist built around Selenium scripting, while Elastic is relevant where its observability environment is already in use. Select the platform that the responsible team can keep accurate through normal product changes.
