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SignalFx Integrates with Google's Latest Cloud-Native Services

SignalFx announced new integrations with Google Cloud services for cloud-native applications: Google Cloud Functions, Istio on GKE, and Knative.

These new capabilities represent another step forward in SignalFx’s strategy to enable complete observability by collecting and analyzing in real-time metrics, traces, and events from popular cloud services. SignalFx’s collection strategy fully supports open standards-based instrumentation such as collectd, statsd, Prometheus, OpenTracing, OpenCensus, and Zipkin. Along with agent-based auto-instrumentation options, SignalFx provides its customers with ultimate flexibility to speed their time to value.

Companies are turning to cloud-native technologies, microservices, and DevOps to boost application development, resiliency, and agility. Google is making key contributions to open-source projects such as Kubernetes, Knative, and Istio, which is helping to remove barriers to adopting new Google Cloud managed services. SignalFx easily integrates with Google Cloud services, allowing DevOps and SRE teams to gain complete visibility.

Unlike traditional monitoring tools that are incapable of addressing the new operational complexities organizations encounter when moving to a cloud-native stack, SignalFx has been designed from the ground up to handle the highly dynamic, short-lived nature of cloud-native environments such as Google Cloud Functions, containerized microservices, and Knative workloads.

“Our joint customers want a solution that removes the complexity of gaining observability into cloud-native infrastructure, microservices, and digital business performance,” said Arijit Mukherji, CTO, SignalFx. “Customers want to work with a vendor that innovates at the pace of Google Cloud so they can leverage the latest technology. SignalFx’s real-time observability platform enables our customers to realize the full potential of Google Cloud services.”

“As our customers expand their adoption of cloud services, SignalFx provides the flexibility to consume its real-time observability platform from multiple locations and cloud providers,” said Leonid Igolnik, EVP, Engineering, SignalFx. “Additionally, as part of the rapid expansion of our own infrastructure across multiple geographies and clouds, SignalFx now includes a Google Cloud region.”

Developing and operating modern digital services requires a new approach to monitoring. The pressure to maximize developer productivity and to constantly improve customer experience requires a platform capable of providing accurate insights in real-time with minimal operator intervention. SignalFx accomplishes this thanks to its unique Streaming Analytics architecture that leverages patented data science to intelligently analyze metrics and traces in real-time without the latency of traditional batch architectures. To ensure the most accurate characterization of application performance, SignalFx’s NoSample distributed tracing solution analyzes every transaction across microservices – not just a small random sample – thereby intelligently capturing all anomalies - even the P99 outliers.

“Google Cloud services enable developers to focus on what they care most about - delivering the highest quality code fast,” said Igolnik. “Our integrations with Google Cloud enable our customers to successfully monitor and troubleshoot microservices and serverless applications in real-time -- at scale.”

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SignalFx Integrates with Google's Latest Cloud-Native Services

SignalFx announced new integrations with Google Cloud services for cloud-native applications: Google Cloud Functions, Istio on GKE, and Knative.

These new capabilities represent another step forward in SignalFx’s strategy to enable complete observability by collecting and analyzing in real-time metrics, traces, and events from popular cloud services. SignalFx’s collection strategy fully supports open standards-based instrumentation such as collectd, statsd, Prometheus, OpenTracing, OpenCensus, and Zipkin. Along with agent-based auto-instrumentation options, SignalFx provides its customers with ultimate flexibility to speed their time to value.

Companies are turning to cloud-native technologies, microservices, and DevOps to boost application development, resiliency, and agility. Google is making key contributions to open-source projects such as Kubernetes, Knative, and Istio, which is helping to remove barriers to adopting new Google Cloud managed services. SignalFx easily integrates with Google Cloud services, allowing DevOps and SRE teams to gain complete visibility.

Unlike traditional monitoring tools that are incapable of addressing the new operational complexities organizations encounter when moving to a cloud-native stack, SignalFx has been designed from the ground up to handle the highly dynamic, short-lived nature of cloud-native environments such as Google Cloud Functions, containerized microservices, and Knative workloads.

“Our joint customers want a solution that removes the complexity of gaining observability into cloud-native infrastructure, microservices, and digital business performance,” said Arijit Mukherji, CTO, SignalFx. “Customers want to work with a vendor that innovates at the pace of Google Cloud so they can leverage the latest technology. SignalFx’s real-time observability platform enables our customers to realize the full potential of Google Cloud services.”

“As our customers expand their adoption of cloud services, SignalFx provides the flexibility to consume its real-time observability platform from multiple locations and cloud providers,” said Leonid Igolnik, EVP, Engineering, SignalFx. “Additionally, as part of the rapid expansion of our own infrastructure across multiple geographies and clouds, SignalFx now includes a Google Cloud region.”

Developing and operating modern digital services requires a new approach to monitoring. The pressure to maximize developer productivity and to constantly improve customer experience requires a platform capable of providing accurate insights in real-time with minimal operator intervention. SignalFx accomplishes this thanks to its unique Streaming Analytics architecture that leverages patented data science to intelligently analyze metrics and traces in real-time without the latency of traditional batch architectures. To ensure the most accurate characterization of application performance, SignalFx’s NoSample distributed tracing solution analyzes every transaction across microservices – not just a small random sample – thereby intelligently capturing all anomalies - even the P99 outliers.

“Google Cloud services enable developers to focus on what they care most about - delivering the highest quality code fast,” said Igolnik. “Our integrations with Google Cloud enable our customers to successfully monitor and troubleshoot microservices and serverless applications in real-time -- at scale.”

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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 ...