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SignalFx Delivers Next Generation of Application Performance Monitoring

SignalFx unveiled SignalFx Microservices APM, a real-time application performance monitoring solution designed to accelerate troubleshooting for DevOps teams through advanced real-time analytics.

Powered by NoSample distributed tracing architecture, SignalFx Microservices APM observes every single transaction – not just a small random sample – and reports on every anomaly. With the new Outlier Analyzer, the most challenging issues can now be pinpointed and resolved with a single click.

SignalFx Microservices APM is built on top of SignalFx's advanced streaming analytics platform for metrics which applies unsurpassed data science in real-time to identify the root cause of critical application problems. Integration with advanced automation workflows can remediate issues in seconds.

"Microservices have changed the game for companies, allowing them to move faster by breaking up applications into modules that can be developed and updated independently. At the same time, it is now exponentially more difficult to identify and triage problems as they span hundreds of services and evolve rapidly," said Arijit Mukherji, Chief Technology Officer of SignalFx. "SignalFx Microservices APM is the only end-to-end monitoring solution designed to tackle this problem head-on, dramatically reducing mean time to repair through directed troubleshooting."

Traditional APM systems are not designed to handle the scale, complexity, and dynamic nature of today's microservices environments. Their probabilistic sampling and batch processing approach may work for legacy monolithic applications but falls short in a microservices world, leaving developers in the dark and customers frustrated. 

"The world happens in real-time and if something goes wrong, finding problems minutes later just doesn't cut it," said Karthik Rau, CEO and Founder of SignalFx. "Customers expect technology and applications to just work. Their tolerance for failure is next to zero with the value of their brand held in the balance."

"The collateral damage of disappointing customers far exceeds and outlasts the monetary impact. People remember," said Trevor Rundell, Director of Engineering at Drift. "The key to keeping our customers happy is finding the root cause of the problem and fixing it – preferably before they even notice.

"Being able to capture 100% of traces is very important to us. SignalFx NoSample architecture means we no longer have to ‘hope we can find the problem' because now we ‘know we will find the problem,'" Rundell said.

Adam Nutt, Engineering Manager at Nike said, "With SignalFx Microservices APM we now have all the traces we need to immediately get to the root cause of any problem. That means more successful product launches – and happier customers."

Next Generation of Real-Time Cloud Monitoring

The SignalFx Real-Time Cloud Monitoring Platform for Infrastructure, Microservices, and Applications is unique in Four Key Areas:

Flexible, open instrumentation– SignalFx makes it easy to ingest metrics, traces and events from any source across the stack. DevOps can flexibly use the best tool for the job choosing from a wide range of supported instrumentation options – APIs, Smart Agent, Telemetry Adapters, Function Wrappers, Code Auto-instrumentation. SignalFx instrumentation is based on open standards (such as collectd and StatsD for metrics, and OpenTracing, OpenCensus, and Zipkin for distributed tracing) and therefore agnostic to how developers choose to collect data. Thanks to 150+ pre-built integrations and the auto-discovery and auto-configuration capabilities of the SignalFx Smart Agent, Ops teams can start monitoring popular systems in minutes.

Real-time problem detection– SignalFx is the only cloud monitoring solution based on a streaming metrics architecture with mutable metadata. Thanks to this unique SignalFlow architecture, SignalFx delivers consistent low-latency problem detection and high-resolution visualization (1 sec metrics) – even in high-cardinality use cases typical of digital business applications. In today's digital economy, companies cannot afford to wait minutes for their monitoring solution to identify and alert on a problem anywhere in the stack. Anomalous conditions have to be identified in real-time, and alerts have to fire in seconds to avoid impacting revenue and the customer experience. The SignalFx streaming architecture is the only platform capable of detecting problems in seconds no matter the amount of data generated by modern, short-lived infrastructure and distributed microservices.

Directed troubleshooting– SignalFx shortens problem resolution in a microservices environment by capturing all anomalies and directly assisting the operator during the triage process. Thanks to its unique NoSample architecture, SignalFx observes every transactionand identifies every anomalyvia a complete view of system behavior over time. Through features like Outlier Analyzer, SignalFx applies powerful analytics that guide DevOps to the root cause of issues in seconds.

Built for DevOps velocity– SignalFx is an API-driven solution, designed to be easily integrated with the DevOps toolchain. Every aspect and functionality of the platform can be controlled and programmed through code leveraging well-defined APIs. This enables SRE teams to be more productive by automating monitoring services through code.

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SignalFx Delivers Next Generation of Application Performance Monitoring

SignalFx unveiled SignalFx Microservices APM, a real-time application performance monitoring solution designed to accelerate troubleshooting for DevOps teams through advanced real-time analytics.

Powered by NoSample distributed tracing architecture, SignalFx Microservices APM observes every single transaction – not just a small random sample – and reports on every anomaly. With the new Outlier Analyzer, the most challenging issues can now be pinpointed and resolved with a single click.

SignalFx Microservices APM is built on top of SignalFx's advanced streaming analytics platform for metrics which applies unsurpassed data science in real-time to identify the root cause of critical application problems. Integration with advanced automation workflows can remediate issues in seconds.

"Microservices have changed the game for companies, allowing them to move faster by breaking up applications into modules that can be developed and updated independently. At the same time, it is now exponentially more difficult to identify and triage problems as they span hundreds of services and evolve rapidly," said Arijit Mukherji, Chief Technology Officer of SignalFx. "SignalFx Microservices APM is the only end-to-end monitoring solution designed to tackle this problem head-on, dramatically reducing mean time to repair through directed troubleshooting."

Traditional APM systems are not designed to handle the scale, complexity, and dynamic nature of today's microservices environments. Their probabilistic sampling and batch processing approach may work for legacy monolithic applications but falls short in a microservices world, leaving developers in the dark and customers frustrated. 

"The world happens in real-time and if something goes wrong, finding problems minutes later just doesn't cut it," said Karthik Rau, CEO and Founder of SignalFx. "Customers expect technology and applications to just work. Their tolerance for failure is next to zero with the value of their brand held in the balance."

"The collateral damage of disappointing customers far exceeds and outlasts the monetary impact. People remember," said Trevor Rundell, Director of Engineering at Drift. "The key to keeping our customers happy is finding the root cause of the problem and fixing it – preferably before they even notice.

"Being able to capture 100% of traces is very important to us. SignalFx NoSample architecture means we no longer have to ‘hope we can find the problem' because now we ‘know we will find the problem,'" Rundell said.

Adam Nutt, Engineering Manager at Nike said, "With SignalFx Microservices APM we now have all the traces we need to immediately get to the root cause of any problem. That means more successful product launches – and happier customers."

Next Generation of Real-Time Cloud Monitoring

The SignalFx Real-Time Cloud Monitoring Platform for Infrastructure, Microservices, and Applications is unique in Four Key Areas:

Flexible, open instrumentation– SignalFx makes it easy to ingest metrics, traces and events from any source across the stack. DevOps can flexibly use the best tool for the job choosing from a wide range of supported instrumentation options – APIs, Smart Agent, Telemetry Adapters, Function Wrappers, Code Auto-instrumentation. SignalFx instrumentation is based on open standards (such as collectd and StatsD for metrics, and OpenTracing, OpenCensus, and Zipkin for distributed tracing) and therefore agnostic to how developers choose to collect data. Thanks to 150+ pre-built integrations and the auto-discovery and auto-configuration capabilities of the SignalFx Smart Agent, Ops teams can start monitoring popular systems in minutes.

Real-time problem detection– SignalFx is the only cloud monitoring solution based on a streaming metrics architecture with mutable metadata. Thanks to this unique SignalFlow architecture, SignalFx delivers consistent low-latency problem detection and high-resolution visualization (1 sec metrics) – even in high-cardinality use cases typical of digital business applications. In today's digital economy, companies cannot afford to wait minutes for their monitoring solution to identify and alert on a problem anywhere in the stack. Anomalous conditions have to be identified in real-time, and alerts have to fire in seconds to avoid impacting revenue and the customer experience. The SignalFx streaming architecture is the only platform capable of detecting problems in seconds no matter the amount of data generated by modern, short-lived infrastructure and distributed microservices.

Directed troubleshooting– SignalFx shortens problem resolution in a microservices environment by capturing all anomalies and directly assisting the operator during the triage process. Thanks to its unique NoSample architecture, SignalFx observes every transactionand identifies every anomalyvia a complete view of system behavior over time. Through features like Outlier Analyzer, SignalFx applies powerful analytics that guide DevOps to the root cause of issues in seconds.

Built for DevOps velocity– SignalFx is an API-driven solution, designed to be easily integrated with the DevOps toolchain. Every aspect and functionality of the platform can be controlled and programmed through code leveraging well-defined APIs. This enables SRE teams to be more productive by automating monitoring services through code.

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...