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Top Leading Key Players are:

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Global Docker Monitoring market is segmented based by type, application and region.

Based on Type, the market has been segmented into:

by Component (Solutions and Services {Managed Services and Professional Services}), Deployment Type (Cloud and On-Premise)

Based on application, the market has been segmented into:

by Industry Vertical (BFSI, IT & Telecom, Healthcare, Retail, Manufacturing, and Others)

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Top Players – VMware (US), Microsoft (US), IBM (US), Cisco (US), BMC Software (US), New Relic (US), Splunk (US), ServiceNow (US), Dynatrace (US), Datadog (US), Google (US), Micro Focus (UK), Oracle (US), Broadcom (US), AWS (US), Flexera (US), ScienceLogic (US), Puppet (US), HPE (US), Turbonomic (US), Apptio (US), Chef (US), Densify (US), Riverbed (US) etc.

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North America Country (United States, Canada)
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Imagine this familiar scenario: A new incident hits production; your team is getting paged and a growing number of people are getting on the conference bridge or joining the Slack channel to troubleshoot. A lot of chatter takes place. Everyone is trying to figure out what went wrong.

But almost immediately, it feels like there are a lot more people on this than are necessary. If I had to throw a number out, maybe 50% more people than necessary. There’s way too much noise and not enough clear information about the root cause of the incident. Getting so many people involved is not exactly efficient and inflates the time required to resolve this incident.

As an incident first responder, my top priority is always to understand what went wrong and where, so I try to involve the right people. At the beginning, it’s not always easy to immediately answer these questions, so we bring in more people thinking we’ll collaborate and mitigate the incident quickly. But it never happens that way. In the long run, it ends up decreasing the team’s productivity and effectiveness.

In this blog, I’ll tell you how to address incidents and resolve production issues faster with a feature in our Applied Intelligence product, and I’ll share some of the details about how it works. Within the Incident Intelligence capability is a feature called suggested responders, and it augments every new incident by identifying in real time the most relevant team members to help resolve it. Applying machine learning (ML) to analyze your past interactions, New Relic identifies the most relevant users in your organization who can help resolve an incident.

If you’re an Incident Intelligence customer, suggested responders is automatically available with no configuration or setup needed.

Machine learning powered

When you opt in to Incident Intelligence, New Relic automatically trains an ML model to analyze your account’s historic incident data and overall New Relic user interaction. As soon as the model training is completed-which happens as more and more users interact with the system-future incidents will be augmented in real time with those people best suited to help resolve them. The responders list is displayed both in the New Relic issue page and within the issue notification payload, so you can see it directly from where you usually respond to incidents.

For each suggestion, you can provide feedback in the form of a thumbs up/down vote, so that the model could become even more accurate.

How the machine learning model works

The suggested responders model uses past violations, combined with user analytics data, to predict the most likely responders for new violations. The model consists of three phases: supervisedpattern recognition,label spreading, and a recommender engine.

Supervised pattern recognition: This step predicts, based on how users interacted with the New Relic platform in the past, whether they closed the violation. It curates a labeled dataset that includes only violations for which we know for certain the user who closed them. We then train a binary classifier that treats the users’ actions as features and whether they closed the violations as the target. Suggested responders uses this trained model in the next step.

Label spreading: The purpose of this step is to increase the coverage of our labeled dataset by augmenting it with violations for which we do not know the user who closed it. We construct an unlabeled dataset that links each violation to the user actions that occurred while the violations were still open. We then run the unlabeled dataset through the trained model. The output of this stage is a responders table that assigns, for each violation-user pair, the likelihood that the user closed the violation. This table is used in the next step to identify the users who are most likely to resolve a certain violation. We update the table periodically, so that it will reflect the most recent violations.

Recommender engine: The third and last step of the model uses the responders table generated in the previous stage to suggest responders to new violations. When a new violation occurs, the model computes a similarity score between that violation and the violations that appear in the responders table. The similarity score is based on many fields, such as the type of product, target id, policy id, condition id, golden signal, and components of the violation at hand. We then use the similarity score as a weight to compute a weighted score for each user across all the violations in the table. The weighted score can be interpreted as the (predicted) level of involvement a user had with similar violations in the past. The model returns the users who received the highest weighted score. These are the suggested responders.

How to use suggested responders to route incident notifications

Now that you know how suggested responders works, you can start using it in new ways and further increase your response efficiency and resolve production issues faster. One way to do that is by configuring a pathway logic that, for instance, routes the alerts notification to a specific channel whenever it predicts certain users as suggested responders for a given incident. This lets you ensure that the right people will always be notified for the right incidents.

Get started now and resolve production issues faster

To get up and running with suggested responders, all you need to do is to opt in to Incident Intelligence and start ingesting your violations. The model trains itself and gives you suggestions about 24 hours after opt-in. The more you engage, the more recommendations will increase. For instance, if you interact with New Relic One entities or define more alert conditions and policies in your account, the model will increase recommendations.

The views expressed on this blog are those of the author and do not necessarily reflect the views of New Relic. Any solutions offered by the author are environment-specific and not part of the commercial solutions or support offered by New Relic. Please join us exclusively at the Explorer’s Hub (discuss.newrelic.com) for questions and support related to this blog post. This blog may contain links to content on third-party sites. By providing such links, New Relic does not adopt, guarantee, approve or endorse the information, views or products available on such sites.

Overview

New Relic introduced New Relic Edge with Infinite Tracing, a fully managed, cloud-native distributed tracing solution built to help software engineering teams rapidly diagnose issues and latency across complex, microservice-based environments. The offering complements New Relic’s existing head-based distributed tracing capabilities, giving teams the flexibility to choose the tracing model that best fits their workloads.

The Problem: Observability in Microservice Architectures

The shift from monolithic application architectures to microservices has brought significant benefits — including greater team autonomy, faster deployment cycles, and better scalability. However, it has also introduced substantial operational complexity.

Modern systems can involve hundreds or even thousands of individual microservices, each with its own dependencies, frameworks, and deployment mechanisms. When a latency spike or error occurs, tracing the root cause across so many interconnected services becomes extremely difficult without the right tooling.

Many organizations rely on tail-based distributed tracing to address this challenge. Unlike head-based tracing — where a sampling decision is made at the start of a request — tail-based tracing evaluates the complete trace after it finishes, allowing teams to retain traces that are actually interesting (slow, erroneous, or anomalous) rather than sampling blindly up front.

The catch with existing tail-based solutions is that they traditionally require teams to deploy, manage, and scale their own on-premises tracing infrastructure. This creates operational overhead, increases costs for data transmission and storage, and pulls engineering effort away from product development.

What Is New Relic Edge with Infinite Tracing?

New Relic Edge with Infinite Tracing is designed to remove that operational burden by delivering tail-based distributed tracing as a fully managed service. Key characteristics include:

• Tail-based sampling: Observes 100% of a workload’s trace data before deciding which traces to forward, ensuring that high-value traces (errors, slow requests) are never dropped.
• Fully managed infrastructure: New Relic operates the tracing edge nodes — teams do not need to deploy, patch, or scale any additional software themselves.
• Cloud-native co-location: The edge nodes are located in the same cloud provider and region as the customer’s workloads, reducing data egress latency and costs associated with sending large volumes of trace data across regions or to on-premises collectors.
• Intelligent forwarding: After evaluating all trace data at the edge, only the most relevant traces are forwarded to New Relic’s core platform for storage and visualization.
• Visualization in New Relic One: Forwarded traces are surfaced in New Relic’s standard tracing UI, enabling teams to correlate distributed traces with metrics, logs, and other telemetry.

Head-Based vs. Tail-Based Tracing: Choosing the Right Model

New Relic Edge with Infinite Tracing does not replace the existing head-based distributed tracing option — it complements it. The two models serve different use cases:

Teams can select the model that aligns with their environment’s complexity, data volume, and observability requirements.

Key Benefits for DevOps and SRE Teams

• Faster root cause analysis: Because tail-based sampling retains traces for slow and erroneous requests, engineers can investigate the exact transactions that caused incidents rather than hoping a sampled trace captured the problem.
• Reduced toil: No on-premises tracing collectors to deploy, monitor, or scale. The managed service handles capacity automatically.
• Lower data costs: By filtering at the edge before forwarding to New Relic’s core platform, teams avoid paying to transmit and store every raw trace span.
• Incident resolution before customer impact: Faster diagnosis means teams can detect and resolve latency issues and errors earlier in the incident lifecycle.

Availability

At the time of announcement (May 2020), New Relic Edge with Infinite Tracing was available for customers hosted in AWS US-East-1 (N. Virginia), with additional region support planned. Customers should consult the New Relic documentation for current regional availability, as this will have expanded since the initial release.

How Infinite Tracing Fits Into a Modern Observability Stack

Distributed tracing is one pillar of the broader observability model alongside metrics and logs. Infinite Tracing specifically addresses the tracing pillar’s hardest challenge at scale: retaining the signal (error and latency traces) while discarding the noise (successful, fast, representative traces), without requiring engineering teams to build and maintain the infrastructure to do so.

For organizations running complex microservice platforms on AWS — or planning to expand to additional cloud regions — New Relic Edge with Infinite Tracing offers a managed path to full-fidelity distributed tracing without the operational overhead of self-hosted alternatives.

Original announcement sourced from Help Net Security, May 2020. Quote attributed to Andrew Tunall, Senior Director of Product Management at New Relic.

New Relic AI: AIOps Reaches General Availability

New Relic, a leading cloud-based observability company, announced the general availability of New Relic AI, its AIOps (Artificial Intelligence for IT Operations) platform. The product is positioned as a core pillar of the broader New Relic One observability platform, extending its capabilities beyond traditional Application Performance Monitoring (APM) and infrastructure monitoring into AI-driven incident intelligence.

Background: The SignifAI Acquisition

The foundation of New Relic AI traces back to February 2019, when New Relic acquired SignifAI, a Silicon Valley-based startup headquartered in Sunnyvale, California, with additional offices in Tel Aviv. SignifAI specialized in applying AI and machine learning to IT operations — precisely the capability New Relic needed to enter the AIOps space. The acquisition allowed New Relic to integrate intelligent, ML-powered operations directly into its observability stack.

What Is New Relic AI?

New Relic AI is described as an open incident correlation and intelligence solution that is source-agnostic and data-agnostic. It integrates deeply with the New Relic One platform and is designed to help IT and DevOps teams detect anomalies, correlate incidents, and reduce the volume of noisy alerts that routinely overwhelm operations teams.

Key capabilities include:

• Anomaly detection: Evaluates telemetry data from disparate sources to surface outliers in patterns before they become critical incidents.
• Incident correlation: Automatically groups related alerts and events to reduce duplicate notifications and alert fatigue.
• Multi-signal ingestion: Ingests and analyzes alerts, logs, metrics, and deployment events from multiple data types simultaneously.
• Contextual insights: Provides an intelligent summary of each incident alongside a live view of ingested data, enabling faster diagnosis and prioritization.
• User feedback tuning: Allows operators to tune correlation rules based on feedback, improving accuracy over time.

The Role of NRDB (New Relic Database)

New Relic AI is powered by NRDB (New Relic Database), a purpose-built, distributed time-series database designed to store telemetry data — metrics, events, logs, and traces — from a wide variety of sources in a uniform format. NRDB is engineered for high-performance querying, making it well-suited for the real-time analysis that AIOps requires. Machine learning algorithms run against data stored in NRDB to identify patterns, detect anomalies, and derive context-based insights.

Reducing Alert Fatigue: What New Relic Claims

Alert fatigue is a well-documented problem in DevOps and IT operations: too many alerts, too many false positives, and too little context lead to slower incident response and missed issues. New Relic AI addresses this by correlating alerts across data sources to filter signal from noise automatically.

According to New Relic, early access customers reported seeing automatic reductions in alert noise of up to 50 percent through incident correlation. This figure is a vendor-reported claim from early access users and has not been independently verified.

Integrations: Works With Your Existing Stack

A notable differentiator for New Relic AI is its broad integration ecosystem. Rather than requiring teams to rip and replace existing tooling, the platform is designed to augment and connect with commonly used monitoring, alerting, and collaboration tools. Supported integrations at launch included:

• Monitoring & observability: Splunk, Prometheus, Grafana, Amazon CloudWatch, Google Cloud’s Stackdriver (now Google Cloud Operations Suite)
• Incident management & on-call: PagerDuty, OpsGenie, VictorOps
• IT service management: ServiceNow
• Collaboration: Slack

By integrating with both public cloud monitoring platforms and third-party tooling, New Relic AI positions itself as a correlating intelligence layer that sits above existing data sources rather than replacing them.

New Relic’s Broader Strategic Shift

New Relic began as an Application Performance Monitoring (APM) company but has progressively expanded its scope. The consolidation of APM and infrastructure monitoring into the unified New Relic One platform was a pivotal step, and New Relic AI represents the next layer — bringing machine learning and AI-driven intelligence to the data already flowing through that platform.

The company’s stated goal is to move beyond reactive monitoring toward proactive, AI-assisted operations — a requirement increasingly driven by the complexity of cloud-native, hybrid, and multi-cloud environments.

The AIOps Market Consolidation

New Relic’s move into AIOps mirrors a broader consolidation trend across the industry. Around the same period, several notable acquisitions reshaped the AIOps and observability landscape:

• Splunk acquired SignalFx
• LogicMonitor acquired Unomaly
• ServiceNow acquired Loom Systems
• VMware acquired Voyance
• New Relic acquired SignifAI

This pattern reflects an industry-wide recognition that observability data alone is not enough — operations teams need intelligent correlation and context to act quickly at scale. AIOps has emerged as the discipline that bridges raw telemetry with actionable incident intelligence.

Summary

New Relic AI’s general availability marks a significant step in New Relic’s evolution from an APM vendor to a full-stack observability and AIOps provider. For DevOps and IT operations teams managing complex, distributed systems, the platform offers a way to centralize alert correlation, reduce noise, and surface actionable insights faster — without abandoning existing monitoring tooling. As multi-cloud and cloud-native architectures continue to grow in complexity, AI-assisted operations platforms like New Relic AI are likely to become a standard part of the enterprise DevOps toolkit.

What Is APM Automation?

Application Performance Management (APM) refers to the monitoring, detection, and diagnosis of performance issues within software applications. APM automation extends this discipline by integrating continuous monitoring pipelines, alerting, and remediation workflows directly into CI/CD and DevOps toolchains — reducing the manual effort required to keep production systems healthy.

As organisations move workloads to hybrid and multi-cloud environments, and as microservices architectures multiply the number of observable components, automated APM tooling has become a strategic investment rather than an optional add-on.

Major APM Vendors in the Market

The following vendors are consistently recognised as significant players in the APM and observability space:

• AppDynamics (Cisco) – Full-stack observability with deep application topology mapping and business transaction monitoring.
• New Relic – Cloud-native, consumption-based observability platform covering metrics, events, logs, and traces (MELT).
• Dynatrace – AI-powered (Davis AI) automated root-cause analysis and automated discovery of cloud-native environments.
• Microsoft Corporation – Azure Monitor and Application Insights provide native APM capabilities tightly integrated with the Azure ecosystem.
• CA Technologies (now part of Broadcom) – Enterprise APM and AIOps offerings targeting large-scale, on-premises and hybrid environments.
• BMC Software – TrueSight Operations Management and Helix platforms for enterprise IT operations and AIOps.
• IBM Corporation – Instana (real-time APM) and Watson AIOps for automated anomaly detection at enterprise scale.
• Oracle Corporation – Oracle Application Performance Monitoring Cloud Service integrated with Oracle Cloud Infrastructure.
• Dell Technologies – Infrastructure performance monitoring solutions that complement partner APM tooling.
• AppNeta – Network path and application delivery monitoring with a focus on end-user experience and SaaS performance.
• Riverbed Technology – Network performance management and WAN optimisation with integrated application visibility.

APM Tool Types

APM automation tools are broadly classified by the primary telemetry source they leverage:

App Metrics-Based APM

These tools collect and analyse time-series metrics — response times, error rates, throughput, saturation — to establish baselines and trigger alerts when thresholds are breached. Solutions in this category often integrate with Prometheus, Grafana, or vendor-native dashboards.

Code-Based (Instrumentation) APM

Code-based APM agents instrument application runtimes (JVM, .NET CLR, Node.js, Python, etc.) to capture distributed traces, transaction profiles, and method-level performance data. This approach provides the deepest visibility into bottlenecks but requires agent deployment and maintenance.

Network-Based APM

Network-based tools analyse packet flows, latency between services, and network path quality to correlate infrastructure behaviour with application performance degradation — particularly valuable for diagnosing issues in distributed or WAN-connected architectures.

Key Industry Verticals Driving Adoption

APM tooling is adopted across virtually every sector, but demand is especially strong in industries where application downtime or degraded performance has direct financial or safety consequences:

• Banking, Financial Services & Insurance (BFSI) – High transaction volumes, regulatory compliance requirements, and zero-tolerance for downtime make APM critical.
• IT and Telecom – Complex service meshes and high customer expectations for availability drive continuous monitoring investment.
• Logistics – Real-time tracking and order management platforms require consistent performance across peak demand periods.
• Retail & eCommerce – Conversion rates are directly tied to page load times; APM supports performance budgeting and A/B testing pipelines.
• Healthcare – Clinical and patient-facing applications must meet strict uptime and data-integrity standards.
• Manufacturing – Industrial IoT and ERP integrations require reliable middleware monitoring.
• Chemicals, Oil & Mining – SCADA and operational technology (OT) monitoring increasingly intersects with IT APM tooling.

Market Growth Drivers

Several structural trends are accelerating investment in APM automation tools:

• Cloud-native and microservices adoption – Containerised, service-mesh architectures produce orders-of-magnitude more telemetry data than monolithic applications, making manual monitoring impractical.
• DevOps and SRE maturity – Organisations formalising Site Reliability Engineering (SRE) practices embed APM into deployment pipelines and error-budget tracking.
• AIOps integration – Machine-learning-driven anomaly detection reduces mean time to detect (MTTD) and mean time to resolve (MTTR) by surfacing correlated signals across thousands of metrics.
• Shift-left observability – Development teams are integrating performance testing and synthetic monitoring earlier in the software delivery lifecycle.
• Regulatory and SLA pressure – Formal SLA commitments in regulated industries mandate quantifiable uptime and performance reporting.

Market Challenges and Restraints

• Agent overhead and complexity – Deep code instrumentation can introduce CPU and memory overhead in resource-constrained environments.
• Data volume and cost – High-cardinality telemetry at scale can result in significant storage and ingestion costs, particularly on consumption-based SaaS platforms.
• Skill gaps – Effectively interpreting distributed traces and configuring alerting policies requires specialised expertise that many teams are still developing.
• Vendor lock-in – Proprietary agents and data formats can make switching APM vendors or consolidating tooling difficult.
• Security and data privacy – APM agents with access to request payloads must be configured carefully to avoid capturing sensitive personal data.

Evaluating APM Tools: What to Look For

When assessing APM platforms for your environment, consider the following criteria:

1. Auto-discovery and auto-instrumentation – Can the tool detect new services and containers without manual configuration?
2. Distributed tracing support – Does it implement OpenTelemetry (OTel) standards for vendor-neutral trace propagation?
3. Alerting and anomaly detection – Are alerts threshold-based, ML-driven, or both? How noisy are they in practice?
4. Integration ecosystem – Does it connect to your existing incident management (PagerDuty, Opsgenie), CI/CD (Jenkins, GitHub Actions), and log management (Splunk, Elastic) tools?
5. Pricing model – Host-based, per-user, or consumption-based pricing will affect TCO significantly depending on your scale.
6. On-premises vs. SaaS deployment – Regulated industries may require on-premises or private-cloud deployment options.

The Road Ahead

The convergence of APM, infrastructure monitoring, log management, and real-user monitoring (RUM) into unified observability platforms — exemplified by the OpenTelemetry project’s growing adoption — is reshaping how organisations approach performance management. Vendors that offer full-stack coverage with AI-assisted triage and strong open-standards support are increasingly preferred over best-of-breed point solutions that require custom integration work.

For DevOps and platform engineering teams, the strategic priority is not simply purchasing an APM tool, but embedding observability as a first-class engineering practice: instrumenting applications at build time, defining SLOs before incidents occur, and automating remediation where confidence is high enough to act without human intervention.

What Is Website Monitoring Software?

Website monitoring software continuously checks the availability, performance, and correctness of web applications and APIs. Capabilities typically include uptime monitoring, synthetic transaction monitoring, real-user monitoring (RUM), alerting, and root-cause analysis. Enterprise-grade platforms extend these features into full application performance monitoring (APM), distributed tracing, and infrastructure observability.

Notable Vendors in the Space

The market includes a wide range of vendors, from focused uptime-monitoring tools to full-stack observability platforms. Two frequently cited enterprise players are:

• AppDynamics – A Cisco-owned APM and business observability platform that correlates application performance with business outcomes. It provides code-level diagnostics, infrastructure monitoring, and end-user monitoring across on-premises and cloud environments.
• New Relic – A cloud-native observability platform offering APM, infrastructure monitoring, browser monitoring, synthetic monitoring, and log management under a consumption-based pricing model.

Other established vendors in adjacent segments include Datadog, Dynatrace, Pingdom (now part of SolarWinds), Site24x7, Uptime Robot, and open-source stacks built around Prometheus, Grafana, and Alertmanager. The right choice depends on team size, existing cloud provider relationships, budget model (seat-based vs. consumption-based), and required depth of tracing and profiling.

Key Market Segments

By Monitoring Type

• Uptime / Availability Monitoring – Periodic HTTP checks from external probes to detect outages.
• Synthetic Monitoring – Scripted browser or API transactions run on a schedule to validate user journeys before real users are affected.
• Real-User Monitoring (RUM) – JavaScript agents injected into pages to capture actual user experience metrics such as Core Web Vitals, page load times, and error rates.
• Application Performance Monitoring (APM) – Deep instrumentation of application code, databases, and external service calls, often using distributed tracing (OpenTelemetry, Jaeger).
• Infrastructure Monitoring – Host, container, and Kubernetes metrics feeding dashboards and alerting pipelines.

By Deployment Model

• SaaS / Cloud-hosted – Most modern platforms are fully managed SaaS offerings, reducing operational overhead for the monitoring infrastructure itself.
• On-premises / Self-hosted – Required in regulated industries or air-gapped environments; often built on open-source tooling.
• Hybrid – Agents run on-premises while data is aggregated and visualized in a cloud control plane.

By Geography

Demand for website monitoring software spans all major regions. North America has historically been the largest adopter, driven by high cloud-application density and mature DevOps cultures. Europe is a significant market, with GDPR data-residency requirements influencing vendor selection. Asia-Pacific is a fast-growing segment, particularly in markets with large e-commerce and fintech ecosystems such as India, China, and Southeast Asia.

Market Drivers

• Cloud-native adoption – Microservices and serverless architectures increase the number of observable components and the need for distributed tracing.
• SRE and SLA accountability – Engineering teams are formally measured against error budgets and SLOs, driving demand for reliable monitoring data.
• E-commerce revenue sensitivity – Performance degradation directly correlates with cart abandonment and lost revenue, making monitoring a business-critical investment.
• Security and compliance – Continuous monitoring supports anomaly detection and audit trails required by standards such as PCI DSS and SOC 2.
• Remote-first infrastructure – Distributed teams managing distributed systems require consistent, centralized observability tooling.

Market Restraints and Challenges

• Cost at scale – Consumption-based pricing for high-volume environments can become expensive, prompting teams to evaluate open-source alternatives or sampling strategies.
• Data privacy regulations – RUM and session-replay features may capture personally identifiable information, creating compliance obligations.
• Alert fatigue – Poorly configured monitoring produces noisy alerts, reducing operator trust in the tooling.
• Vendor lock-in – Proprietary instrumentation agents make migration between platforms costly; the OpenTelemetry standard is reducing this friction over time.

Competitive Dynamics: A Framework for Evaluation

When evaluating website monitoring vendors, DevOps and platform engineering teams typically apply criteria along several dimensions:

1. Coverage – Does the platform support your full stack: frontend, backend, mobile, infrastructure, and logs?
2. Instrumentation overhead – What is the CPU and memory impact of the agent on production workloads?
3. Time-to-insight – How quickly can an on-call engineer move from an alert to a root cause?
4. Integrations – Does the tool integrate with your incident management platform (PagerDuty, Opsgenie), CI/CD pipeline, and IaC tooling?
5. Pricing model – Is pricing predictable at your expected data volume and team size?
6. Data retention and export – Can you export telemetry data for long-term analysis or regulatory retention?

Open-Source and Self-Hosted Alternatives

For teams with budget constraints or strict data-residency requirements, a common self-hosted stack combines:

• Prometheus – Metrics collection and alerting rules.
• Grafana – Dashboarding and visualization.
• Alertmanager – Alert routing and deduplication.
• Jaeger or Tempo – Distributed tracing backends.
• Loki – Log aggregation integrated with Grafana.
• OpenTelemetry Collector – Vendor-neutral telemetry pipeline.

This stack requires operational expertise to run reliably at scale, which is why many organizations start with a managed SaaS platform and migrate selectively to self-hosted components as their observability maturity grows.

Summary

The website monitoring software market encompasses a broad spectrum of tools, from lightweight uptime checkers to comprehensive observability platforms. Key enterprise vendors such as AppDynamics and New Relic compete on depth of instrumentation, AI-assisted anomaly detection, and ecosystem integrations. Buyers should evaluate platforms against their specific stack, budget model, compliance requirements, and team’s operational capacity. The growing adoption of the OpenTelemetry standard is making it progressively easier to switch or combine vendors without full re-instrumentation.

Short Interest Jumps Nearly 23% in August 2019

Short interest in New Relic Inc (NYSE: NEWR) increased sharply between mid-July and mid-August 2019. As of August 15th, total short interest stood at 3,112,700 shares — a 22.9% increase from the 2,532,900 shares reported on July 15th. Short sellers held approximately 6.5% of the company’s outstanding shares, and with an average daily trading volume of 1,250,000 shares, the days-to-cover ratio sat at 2.5 days.

A rising days-to-cover ratio and increasing short interest can signal growing bearish sentiment among traders, though it does not necessarily predict future price direction. In New Relic’s case, the move came against a backdrop of declining share price and mixed analyst opinion.

Stock Price and Key Technical Metrics

At the time of reporting, New Relic stock opened at $56.31. The stock had traded in a wide 12-month range of $54.83 to $113.34, indicating significant volatility over the prior year. Key metrics as of late August 2019 included:

• Market capitalisation: $3.42 billion
• Price-to-earnings (P/E) ratio: -181.65 (negative, reflecting net losses)
• Beta: 0.83
• 50-day simple moving average: $77.06
• 200-day simple moving average: $94.10
• Debt-to-equity ratio: 1.32
• Current ratio: 2.88
• Quick ratio: 2.88

The stock was trading well below both its 50-day and 200-day moving averages, a technical signal often interpreted as a bearish trend. The negative P/E ratio reflected the company’s position as a high-growth SaaS business reinvesting heavily in expansion rather than reporting GAAP profits.

Q1 FY2020 Earnings: Revenue Beat, Loss Narrower Than Expected

New Relic reported its quarterly earnings on August 6th, 2019. The company posted an earnings-per-share (EPS) loss of $0.16, which beat the Zacks consensus estimate of -$0.18 by $0.02. Revenue for the quarter came in at $141.01 million, slightly above analyst expectations of $139.73 million and representing 30.3% growth year-over-year. In the same quarter of the prior year, the company had earned $0.15 EPS.

Other reported financial health indicators included:

• Return on equity: -8.55%
• Net margin: -9.93%
• Full-year EPS consensus estimate: -$0.68

While the revenue growth rate of 30% remained strong for an enterprise SaaS business, the transition from GAAP profitability (the year-prior quarter showed positive EPS of $0.15) to a net loss reflected increased investment in sales, marketing, and product development.

Insider Selling Activity

Several senior executives sold significant blocks of stock in the months preceding this report:

• CEO Lew Cirne sold 19,500 shares on June 4th at an average price of $97.72 per share, for a total transaction value of $1,905,540. The sale was disclosed in a filing with the U.S. Securities and Exchange Commission.
• CFO Mark Sachleben sold 15,000 shares on June 18th at an average price of $96.98 per share, totalling $1,454,700.

In total, company insiders sold 133,904 shares valued at approximately $11.5 million over the preceding quarter. Company insiders owned approximately 17.40% of total outstanding shares at the time of reporting. Insider selling at prices materially above where the stock subsequently traded is a data point sometimes noted by analysts, though insider transactions are regularly part of pre-planned trading arrangements and do not necessarily reflect a negative view of the company’s prospects.

Institutional Ownership and Recent Activity

Institutional investors and hedge funds held approximately 74.72% of New Relic’s outstanding shares. Several institutions adjusted their positions around this period:

• First Trust Advisors LP increased its stake by 24.7% in Q2 2019, bringing its total to 1,516,324 shares valued at approximately $131.2 million after acquiring an additional 300,484 shares.
• Jennison Associates LLC increased its stake by 58.7% in Q2 2019, bringing its total to 723,864 shares valued at approximately $62.6 million after acquiring an additional 267,840 shares.
• Diversified Trust Co increased its stake by 17.8% in Q2 2019, adding 970 shares to reach a total of 6,432 shares valued at approximately $556,000.
• O Shares Investment Advisers LLC initiated a new position in Q1 2019 valued at approximately $445,000.
• Peregrine Asset Advisers Inc. initiated a new position in Q1 2019 valued at approximately $296,000.

Analyst Ratings and Price Targets

Following the August 6th earnings release, multiple Wall Street analysts revised their price targets. Coverage as of late August 2019 showed a wide spread of opinion:

• DA Davidson: Reduced price target to $95.00; maintained “Buy” rating.
• Wedbush: Reiterated “Outperform” rating; reduced price target from $124.00 to $99.00.
• UBS Group: Set a $73.00 price target; assigned a “Hold” rating.
• BMO Capital Markets: Reduced price target to $73.00; maintained a “Positive” rating.
• Needham & Company LLC: Set a $120.00 price target; assigned a “Buy” rating.

Across all analysts covering NEWR at the time: 2 issued sell ratings, 7 issued hold ratings, and 10 assigned buy ratings. The consensus rating was “Hold” with an average price target of $96.67 — approximately 71% above where the stock was trading at the time of the report, illustrating the divergence between analyst targets and market price.

About New Relic

New Relic, Inc. is a software-as-a-service (SaaS) company that provides observability and digital intelligence products to organisations worldwide. Its cloud-based platform enables businesses to collect, store, and analyse telemetry data across their entire technology stack. Core products at the time of this report included:

• New Relic APM (Application Performance Management): Provides code-level visibility into server-based application performance, covering response times, transaction throughput, error rates, and user satisfaction scores.
• New Relic Mobile: Monitors the performance and health of iOS and Android mobile applications at the code level.
• New Relic Browser: Tracks page-load and interaction experiences for real end-users across desktop and mobile browsers.

New Relic operates in the observability and application performance monitoring (APM) market, competing with vendors such as Datadog, Dynatrace, and AppDynamics. The company’s transition to a consumption-based pricing model — announced in subsequent years — would later reshape how it reported revenue and user metrics.

Source data originally reported by Sundance Herald, August 2019. All figures are historical and reflect conditions as of the reporting date.

KeyCorp Reiterates “Hold” Rating on New Relic

Investment analysts at KeyCorp reiterated a “hold” rating on shares of New Relic, Inc. (NYSE: NEWR) in a research note published on Wednesday, August 7, 2019, according to TipRanks.

Broader Analyst Coverage

KeyCorp’s reiterating of the hold rating came amid a flurry of analyst activity around the same period:

• Zacks Investment Research upgraded New Relic from “sell” to “hold” on July 17, 2019.
• Needham & Company LLC maintained a “buy” rating and set a price target of $120.00 on August 7, 2019.
• Oppenheimer lowered its price target from $125.00 to $109.00 while retaining an “outperform” rating on August 7, 2019.
• ValuEngine downgraded New Relic from “buy” to “hold” on August 8, 2019.
• Morgan Stanley cut its price target from $115.00 to $104.00 while keeping an “overweight” rating on August 7, 2019.

Across all tracked analysts at the time, one had assigned a sell rating, seven had issued hold ratings, and ten had given buy ratings. The consensus rating stood at “Buy” with an average price target of $99.07.

Stock Performance and Key Metrics

As of the trading session referenced in the report, NEWR shares rose $1.50 to reach $58.50. Trading volume came in at 1,447,334 shares, below the average daily volume of approximately 1,842,761 shares.

Quarterly Earnings Results

New Relic reported its fiscal quarter earnings on Tuesday, August 6, 2019. Key highlights included:

• EPS: ($0.16), beating the Zacks consensus estimate of ($0.18) by $0.02.
• Revenue: $141.01 million, slightly above analyst estimates of $139.73 million.
• Year-over-year revenue growth: 30.3%.
• Net margin: -9.93%.
• Return on equity: -8.55%.
• Prior-year quarter EPS: $0.15 (profitable).

Analysts were collectively forecasting full-year EPS of -$0.68 for the then-current fiscal year.

Insider Transactions

Several notable insider sales were disclosed to the SEC during the period:

• CEO Lewis Cirne sold 19,500 shares on June 4, 2019, at an average price of $97.72 per share, totaling approximately $1,905,540.
• CFO Mark Sachleben sold 15,000 shares on June 18, 2019, at an average price of $96.98 per share, totaling approximately $1,454,700.

In aggregate, insiders sold 133,904 shares valued at approximately $11,498,947 during the preceding quarter. Company insiders held approximately 17.40% of outstanding shares at the time.

Institutional Ownership Activity

A number of institutional investors adjusted their New Relic positions in the first and second quarters of 2019:

• Cornerstone Advisors Inc. increased its position by 58.4% in Q1 2019, holding 358 shares valued at approximately $35,000.
• Wedbush Securities Inc. grew its stake by 5.9% in Q2 2019, holding 2,661 shares valued at approximately $230,000.
• Nvwm LLC raised its position by 4.1% in Q2 2019, holding 4,869 shares valued at approximately $421,000.
• Coldstream Capital Management Inc. increased its holdings by 8.6% in Q2 2019, holding 2,516 shares valued at approximately $218,000.
• Flinton Capital Management LLC expanded its stake by 51.9% in Q2 2019, holding 656 shares valued at approximately $57,000.

Collectively, hedge funds and other institutional investors owned approximately 74.72% of New Relic’s outstanding shares at the time of the report.

About New Relic

New Relic, Inc. is a software-as-a-service (SaaS) company that provides cloud-based observability and digital intelligence products to organizations worldwide. Its platform enables teams to collect, store, and analyze telemetry data across their software stacks. Core product offerings included:

• New Relic APM (Application Performance Management): Delivers code-level visibility into server-based application performance, including response times, transaction throughput, error rates, and user satisfaction scores.
• New Relic Mobile: Monitors the performance and health of iOS and Android mobile applications at the code level.
• New Relic Browser: Tracks real-user page view experiences for desktop and mobile browser-based applications.

Source: rivertonroll.com (original syndicated report, August 2019). All financial data reflects conditions as of the report date and should not be interpreted as current.

Overview

New Relic, Inc. (NYSE: NEWR) and UMeWorld Ltd. (OTCMKTS: UMEWF) are both classified as computer and technology companies, but they operate in very different market segments and at vastly different scales. New Relic is an established, publicly traded SaaS observability platform, while UMeWorld is a small-cap educational technology holding company. This comparison examines the two businesses across seven dimensions: institutional ownership, insider ownership, risk and volatility, analyst recommendations, profitability, valuation, and earnings.

Insider and Institutional Ownership

Institutional ownership is a widely watched signal of long-term confidence from large money managers, hedge funds, and endowments. At the time of this comparison:

• New Relic: 79.3% of shares held by institutional investors; 17.4% held by insiders.
• UMeWorld: Institutional ownership data was not reported; 6.0% of shares held by insiders.

New Relic’s high institutional ownership suggests broad confidence from professional investors. UMeWorld’s comparatively low insider ownership and lack of reported institutional interest reflect its status as a micro-cap OTC-traded company.

Risk and Volatility

Beta measures a stock’s price sensitivity relative to the broader market (S&P 500). A beta of 1.0 means the stock moves in line with the index; values below 1.0 indicate lower volatility, while negative values suggest inverse correlation.

• New Relic: Beta of 0.83 — approximately 17% less volatile than the S&P 500.
• UMeWorld: Beta of -1.21 — indicating an inverse and amplified relationship with the broader market, which is common in very thinly traded OTC stocks.

A negative beta for UMeWorld should be interpreted with caution. For micro-cap OTC stocks with low trading volume, beta calculations can be statistically unreliable due to sparse price data.

Analyst Recommendations

The following ratings summary was reported by MarketBeat.com at the time of the original article’s publication:

New Relic carried a consensus analyst price target of $99.08 at the time, representing a potential upside of approximately 72.3% from its then-current trading price. UMeWorld had no analyst coverage, which is typical for OTC micro-cap stocks and limits the available information for retail investors.

Profitability

Key profitability metrics for both companies at the time of comparison:

New Relic was operating at a net loss at the time, a common characteristic for high-growth SaaS companies that prioritize revenue expansion and customer acquisition over near-term profitability. UMeWorld’s profitability metrics were unavailable, likely due to the company’s minimal reported revenue.

Valuation and Earnings

The revenue gap between these two companies is stark. New Relic reported over $479 million in gross revenue, while UMeWorld reported just $10,000 — a difference of more than 47,000x. Both companies reported net losses. UMeWorld’s extremely high Price/Sales ratio of 731.64 reflects its near-zero revenue base rather than any meaningful market premium.

Summary Scorecard

Across the 11 financial and qualitative factors examined in this comparison, New Relic scored more favorably than UMeWorld on 6 of those factors, primarily driven by its scale, institutional coverage, analyst following, and revenue base.

Company Profiles

New Relic, Inc.

New Relic, Inc. is a software-as-a-service (SaaS) observability company headquartered in San Francisco, California, and founded in 2007. Its cloud-based platform enables organizations to collect, store, and analyze telemetry data across their entire software stack. Key product areas at the time of this article included:

• Application Performance Management (APM): Visibility into server-side application performance, including response times, error rates, and transaction throughput.
• New Relic Mobile: Code-level performance monitoring for iOS and Android applications.
• New Relic Browser: Real-user monitoring for desktop and mobile browser-based applications.
• New Relic Synthetics: Simulated usage testing across the software development lifecycle.
• New Relic Infrastructure: Host-level health and configuration monitoring across cloud, on-premises, and hybrid environments.
• New Relic Insights: Real-time analytics for both technical and business users.
• New Relic Applied Intelligence: AIOps capabilities for alert correlation and incident detection.

New Relic distributes its products through direct sales, online channels, and partner integrations.

UMeWorld Ltd.

UMeWorld Ltd. is a holding company incorporated in Hong Kong, operating as an educational technology business in the digital media and digital education segments. Its product portfolio at the time included UMFun, UMTang, UEXiao, and 17Speech. The company was founded by Michael M. Lee on August 8, 1997.

Editorial Note

This article reflects publicly available financial data from August 2019 and is preserved here for historical reference. Both companies’ financial positions, analyst ratings, and market conditions have changed significantly since this comparison was written. This article does not constitute investment advice.

Overview

Carbon Black (NASDAQ: CBLK) and New Relic (NYSE: NEWR) were both publicly traded technology companies operating in overlapping areas of enterprise software — security and observability, respectively. This comparison examines how they stacked up in mid-2019 across revenue, profitability, analyst sentiment, institutional ownership, and market volatility.

Revenue and Valuation

The table below compares gross revenue, net income, earnings per share, and key valuation ratios for both companies.

New Relic generated significantly higher revenue than Carbon Black — more than double — while posting a smaller net loss in absolute terms. Both companies carried similar price-to-sales ratios (approximately 7.7x), reflecting comparable market premium expectations for growth-stage software businesses. Neither company was profitable at the time of this comparison.

The widely divergent price-to-earnings ratios are a consequence of both companies operating at a loss; the P/E figures are negative and not directly comparable in the traditional sense. Investors in this segment typically focus on price-to-sales or price-to-gross-profit multiples rather than P/E when evaluating unprofitable SaaS and security companies.

Analyst Recommendations

The following breakdown reflects analyst ratings and consensus price targets as reported by MarketBeat at the time of publication.

Carbon Black carried a consensus price target of $18.30 at the time, implying a potential downside of approximately 16.54% from its then-current price. New Relic, by contrast, had a consensus price target of $99.08, representing a potential upside of approximately 57.29%. The broader analyst community leaned more bullish on New Relic, driven largely by a higher number of outright buy ratings (10 vs. 2 for Carbon Black).

Insider and Institutional Ownership

Institutional ownership can serve as a proxy for large-fund confidence in a company’s long-term growth prospects.

• Carbon Black: 64.5% of shares held by institutional investors; 21.6% held by insiders.
• New Relic: 78.3% of shares held by institutional investors; 17.4% held by insiders.

New Relic had meaningfully higher institutional ownership, suggesting greater interest from hedge funds, endowments, and large asset managers. Carbon Black had a higher insider ownership percentage, which can indicate management confidence — but also a less liquid float. Neither metric alone is a definitive signal; they are most useful when considered alongside the other factors in this comparison.

Volatility and Risk (Beta)

Beta measures a stock’s price sensitivity relative to the broader market (S&P 500), where a beta of 1.0 means the stock moves in line with the index.

• Carbon Black: Beta of 1.28 — approximately 28% more volatile than the S&P 500.
• New Relic: Beta of 0.83 — approximately 17% less volatile than the S&P 500.

New Relic offered a comparatively smoother ride for investors concerned about short-term price swings. Carbon Black’s higher beta reflected its smaller size, more concentrated product portfolio, and the inherent uncertainty of the cybersecurity growth market at the time.

Profitability Metrics

The table below compares net margins, return on equity (ROE), and return on assets (ROA) — three standard measures of how efficiently a company converts revenue and assets into profit.

Both companies were operating at a loss, but New Relic’s losses were significantly smaller as a percentage of revenue and assets. Carbon Black’s deeply negative margins reflected heavy investment in sales, marketing, and R&D relative to its revenue base — a common pattern for early-stage security vendors scaling aggressively. New Relic’s comparatively tighter losses suggested a more mature cost structure.

Summary

Across the 15 factors examined in the original comparison, New Relic outperformed Carbon Black on 9 of them. New Relic held advantages in revenue scale, profitability metrics, institutional ownership, analyst upside targets, and lower market volatility. Carbon Black was favoured in neither category decisively, though its insider ownership and niche focus in endpoint security were notable differentiators.

It is worth noting that both companies were loss-making growth businesses at the time, and direct stock comparisons in this context carry significant uncertainty. Shortly after this analysis, Carbon Black was acquired by VMware for approximately $2.1 billion in October 2019, rendering the public equity comparison moot.

About Carbon Black

Carbon Black, Inc. (formerly Bit9, Inc., renamed in January 2016) was a cybersecurity company founded in 2002 and headquartered in Waltham, Massachusetts. It developed the CB Predictive Security Cloud, a big-data analytics platform for endpoint security. Key products included CB Defense (next-generation antivirus and EDR), CB ThreatHunter (threat hunting and incident response), CB LiveOps (real-time endpoint querying and remediation), and CB Protection (application control for critical infrastructure). The company served enterprises, government agencies, and mid-market organizations globally. Carbon Black was acquired by VMware in 2019 and subsequently rebranded as VMware Carbon Black.

About New Relic

New Relic, Inc. is a software-as-a-service observability company founded in 2007 and headquartered in San Francisco, California. Its cloud-based platform provides application performance monitoring (APM), infrastructure monitoring, browser performance tracking, mobile monitoring, and synthetic testing. New Relic’s tools are used by engineering and operations teams to collect, store, and analyze telemetry data — including metrics, events, logs, and traces — across distributed software environments. The company sells primarily through a direct sales model supplemented by online and self-serve channels. New Relic subsequently transitioned to a consumption-based pricing model and made significant portions of its platform available under a free tier, representing a major strategic shift from its subscription model of the 2019 era.