This article draws on publicly available information about the synthetic monitoring tools market and is intended to help DevOps and SRE practitioners understand the vendor landscape, key use cases, and evaluation criteria. The original source is a market research teaser published in 2020 by Marketsandresearch.biz via primefeed.in; specific figures, forecasts, and rankings from that report have not been independently verified and are not reproduced here.
What Is Synthetic Monitoring?
Synthetic monitoring (also called active monitoring) simulates user interactions with an application or API using scripted transactions, rather than waiting for real users to encounter problems. Scripts are executed on a schedule from geographically distributed probe locations, and the results — response time, availability, error codes, content correctness — are reported continuously.
Because synthetic checks run whether or not anyone is actually using the system, they are especially valuable for:
- Detecting outages and degradations before users do
- Measuring baseline performance across regions and network paths
- Validating SLAs and SLOs continuously
- Testing new deployments in staging and production (shift-right testing)
- Monitoring APIs and third-party dependencies that cannot be instrumented directly
Synthetic Monitoring vs. Real User Monitoring (RUM)
Synthetic monitoring complements, rather than replaces, Real User Monitoring. The two approaches differ in important ways:
| Dimension | Synthetic Monitoring | Real User Monitoring (RUM) |
|---|---|---|
| Data source | Scripted probes | Actual user sessions |
| Coverage when traffic is low | Continuous — traffic-independent | No data without users |
| Geographic control | Explicit probe locations | Wherever users happen to be |
| Depth of user behaviour | Predefined flows only | Reflects real navigation patterns |
| Alert lead time | Can detect before any user is affected | Reactive — requires traffic to trigger alerts |
Production observability strategies typically combine both: synthetic checks for proactive detection and SLA reporting, RUM for understanding true user experience across diverse devices and networks.
Key Vendors in the Synthetic Monitoring Space
The 2020 market research teaser identified the following companies as notable players in the global synthetic monitoring tools space. Brief context is provided for each based on publicly known product positioning at the time of writing.
Broadcom (formerly CA Technologies)
Broadcom acquired CA Technologies in 2018. The CA Application Performance Management (APM) suite — including synthetic monitoring capabilities — became part of Broadcom’s enterprise software portfolio. It targets large enterprises with complex mainframe and distributed environments.
AppDynamics (Cisco)
AppDynamics, acquired by Cisco, provides full-stack observability. Its Browser Synthetic Monitoring feature allows teams to run scripted browser journeys from global probe points and correlate results with back-end APM traces, giving end-to-end visibility from the simulated user to the database query.
Dynatrace
Dynatrace offers HTTP monitors and browser click-path monitors via its Synthetic Monitoring module. A key differentiator is its AI engine (Davis), which automatically correlates synthetic alert events with infrastructure anomalies, reducing mean time to identify root cause (MTTI).
New Relic
New Relic Synthetics supports simple ping monitors, scripted API monitors, and scripted browser monitors (Selenium WebDriver-based). Results are integrated into New Relic’s unified observability platform alongside metrics, traces, and logs, enabling correlation across signals in a single query interface (NRQL).
Splunk / Splunk Infrastructure Monitoring (formerly SignalFx)
Splunk’s observability portfolio includes synthetic monitoring capabilities through its Splunk Observability Cloud. Splunk’s strength lies in combining synthetic data with log analytics, providing teams that already invest heavily in Splunk for security and operations with an integrated monitoring path.
IBM
IBM Instana and the broader IBM Watson AIOps platform include synthetic monitoring capabilities. IBM’s offering targets enterprise clients who are deeply integrated with IBM infrastructure and cloud services.
Microsoft (Azure Monitor)
Azure Monitor Application Insights includes availability tests — URL ping tests and multi-step web tests — that function as lightweight synthetic monitors. For Azure-hosted workloads, this provides a native, low-friction entry point into synthetic monitoring without adopting an additional tool.
Riverbed (Aternity)
Riverbed’s APM and end-user experience monitoring products include synthetic testing, with a particular focus on enterprise WAN environments and branch office performance — use cases relevant to organisations with distributed, non-cloud-native infrastructure.
BMC Software
BMC TrueSight Operations Management includes synthetic transaction monitoring as part of its broader IT operations and AIOps platform, targeting enterprises running mixed on-premises and cloud environments.
Dell / Hewlett Packard Enterprise
Dell and HPE appear in enterprise monitoring market analyses primarily through their infrastructure management products and partnerships, rather than as standalone synthetic monitoring tool vendors.
Market Segments: Enterprise Size
The synthetic monitoring market is typically segmented by organisation size:
Large Enterprises
Large enterprises tend to require synthetic monitoring that integrates with existing APM, ITSM, and SIEM platforms. They often have strict data residency requirements, need private probe nodes (agents installed within corporate networks or specific cloud regions), and need role-based access control across large teams. Vendors like Dynatrace, AppDynamics, and Broadcom have historically targeted this segment.
Small and Medium-Sized Enterprises (SMEs)
SMEs typically prioritise ease of setup, consumption-based pricing, and out-of-the-box dashboards over deep customisation. SaaS-first platforms such as New Relic (which moved to a usage-based pricing model) and cloud-native tools lower the barrier to entry for this segment.
Regional Adoption Factors
Synthetic monitoring adoption varies by region, influenced by digital infrastructure maturity, regulatory requirements, and cloud adoption rates. Key regional considerations include:
- North America: Highest concentration of SaaS vendors and early adopters; strong demand from financial services, e-commerce, and SaaS companies with aggressive SLA requirements.
- Europe: GDPR and data sovereignty concerns influence tool selection; on-premises and private probe deployments are more common than in North America.
- Asia-Pacific: Rapid cloud adoption in markets such as India, Japan, and Southeast Asia drives demand; latency testing across diverse regional network conditions is particularly valuable.
- Middle East and Africa / South America: Growth markets where internet infrastructure variability makes synthetic baseline testing especially useful for validating performance from specific geographies.
Key Evaluation Criteria for DevOps Teams
When evaluating synthetic monitoring tools, consider the following dimensions:
- Probe network coverage: Does the vendor operate probe nodes in the specific regions and cloud availability zones that matter to your users?
- Script flexibility: Can you write Selenium/WebDriver-based scripts for complex multi-step user journeys, or are you limited to URL ping tests?
- Private locations: Can you run synthetic checks against internal or non-public endpoints by deploying a private agent?
- Integration with your observability stack: Does the tool correlate synthetic failures with traces, logs, and infrastructure metrics from your existing platforms?
- Alerting and on-call integration: Does it integrate with PagerDuty, Opsgenie, Slack, or your existing incident management workflow?
- Pricing model: Per-monitor, per-execution, or seat-based? Understand cost at scale before committing.
- API and CI/CD integration: Can synthetic monitors be created, updated, and triggered via API or CLI as part of your deployment pipeline?
Synthetic Monitoring in a Modern DevOps Workflow
Synthetic monitoring is most effective when it is treated as code and integrated into the software delivery lifecycle, not bolted on as an afterthought:
- Pre-deployment: Run synthetic checks against staging environments as a release gate in your CI/CD pipeline.
- Canary and blue/green deployments: Compare synthetic performance metrics between old and new versions before shifting traffic.
- Post-deployment verification: Trigger synthetic checks immediately after a deployment to confirm critical user journeys are healthy before closing the change window.
- Continuous SLA reporting: Use scheduled synthetic checks to generate the uptime and latency data that backs customer-facing SLA commitments.
Summary
Synthetic monitoring tools fill a critical gap in the observability stack by providing proactive, traffic-independent insight into application availability and performance. The vendor landscape includes mature enterprise platforms (Dynatrace, AppDynamics, Broadcom/CA, BMC) alongside cloud-native and SaaS-friendly alternatives (New Relic, Azure Monitor, Splunk Observability). Selecting the right tool depends on your organisation’s size, existing toolchain, geographic requirements, and how deeply you want to embed synthetic testing into your CI/CD pipeline.