SignalFx announced a series of platform updates to SignalFx Microservices APM that provide AI-driven problem detection and alerting on trace latency and error metrics.
The new enhancements enable DevOps and site reliability engineering (SRE) teams to use SignalFx’s Microservices APM to not only troubleshoot, but also monitor microservices through distributed tracing in a single solution. Combined with Splunk’s log analytics, these capabilities further enhance the industry’s first enterprise-grade end-to-end observability platform.
With the continued adoption of cloud-native technologies such as containers and microservices, applications are becoming increasingly complex to monitor. Distributed tracing has become a critical part of modern monitoring and observability strategies, helping DevOps teams understand how requests are being handled end-to-end by an application’s set of microservices. SignalFx Microservices APM is the industry’s only Application Performance Monitoring (APM) solution that metricizes traces and spans, and applies streaming analytics to those metrics. With our NoSample™ tail-based approach that observes and analyzes 100% of all transactions, alerts from SignalFx are more accurate than any other APM solution in the market.
The latest release of SignalFx Microservices APM includes:
- Real-Time, AI-Driven Application Alerts — Leveraging the industry-leading capabilities of SignalFx’s NoSample tail-based sampling, trace and span metricization, and SignalFlow® streaming analytics engine, Microservices APM now supports real-time alerts based on AI-driven problem detection on trace latency and error rate metrics. These self-configuring alerts learn and trigger on behavior changes, and remove the guesswork and back-and-forth associated with manual configuration. This helps DevOps and SRE teams detect sudden spikes and historical anomalies in services and endpoints, significantly reducing mean time to detect (MTTD) and mean time to resolution (MTTR).
- Enhanced Service Maps — Dynamic service maps have been enhanced with automatic detection of inferred services, such as databases, message queues, caches, and other third-party web services. This provides a clearer and more accurate picture of service dependencies by making otherwise untraceable services visible. Combined with existing service performance and alert status details in the service map, users can easily visualize, correlate, and pinpoint potential issues in complex microservices environments.
Database Query Metrics — SignalFx Microservices APM now has the ability with the addition of database query metrics to provide insight into the performance of databases and their impact on end-to-end transactions. This gives DevOps and SRE teams the ability to monitor, alert, and more quickly investigate database-related issues.
- New Auto-Instrumentation Options — Go and Kotlin are now supported with auto-instrumentation, a process that automatically identifies frameworks and libraries, and instruments them to capture trace spans. Along with existing auto-instrumentation support for Java, Python, Ruby, and Node.js, as well as open-standards support for OpenTracing, OpenCensus, Jaeger, and Zipkin, SignalFx is one of the leaders in offering a wide selection of instrumentation options and helping our customers avoid vendor lock-in.
“The value of distributed tracing extends beyond simple troubleshooting and root cause analysis when incidents occur,” said Arijit Mukherji, CTO, SignalFx. “Traditional APM tools are not suited for modern application environments. Since our Microservices APM supports open instrumentation, observes every single transaction, and leverages advanced statistics and algorithms for analytics, we are able to offer the industry’s most unique monitoring and alerting capabilities. No other APM solution can do this.”
The Latest
According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...
Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...
IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...
Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...
In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ...
In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...
In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...
In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...
In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...
From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...