SignalFx announced support for Amazon EventBridge, a newly launched service from Amazon Web Services (AWS).
Amazon EventBridge is a serverless event bus that connects applications together, delivering a stream of real-time data from AWS resources, Software-as-a-Service (SaaS) applications, and data from your own applications. With this new capability, shared SignalFx and AWS customers will be able to operate their infrastructure and applications with continuous closed-loop automation that improves responsiveness, SLA compliance, and customer experience without administrative overhead.
As organizations move to the cloud to take advantage of a programmable infrastructure that can be more easily controlled through automation, they recognize the need for a monitoring solution that can keep up with the highly dynamic and ephemeral nature of cloud-native technologies, such as containers, Kubernetes, and AWS Lambda functions. SignalFx was designed from the ground up to be an ideal solution for monitoring modern cloud environments. Because of its real-time streaming analytics architecture, SignalFx provides accurate visibility and problem detection in seconds instead of minutes or hours provided by traditional solutions.
With Amazon EventBridge, SignalFx users can leverage its analytics engine to detect issues across their stack and trigger the necessary automated events in AWS, via Amazon EventBridge, to remediate issues in seconds before their customers are affected.
For instance, a SignalFx alert could be tied to a combination of system metrics (like CPU utilization) and application metrics (like order processing latency). If CPU utilization is high and processing is slow, an alert is dispatched to Amazon EventBridge in seconds. If CPU utilization is low and orders are still backing up, a different Amazon EventBridge action would be needed—expanded queue handlers. SignalFx can differentiate these patterns using streaming analytics, and then dispatch an event to remediate in seconds.
“AWS customers are increasingly adopting event-driven architectures to rapidly deliver business critical applications,” said David Richardson, vice president, Serverless, Amazon Web Services, Inc. “Support for Amazon EventBridge in SignalFx’s streaming analytics engine now allows AWS users to unlock real-time closed-loop automation. This allows for the immediate detection of issues and event-driven automated remediation.”
The SignalFx cloud monitoring platform uses streaming analytics and a unique NoSample tail-based distributed tracing architecture. Users monitor their services with high-resolution 1-second metrics and observe every single transaction — not just a small sample like legacy platforms. Problems are detected instantly and operators receive meaningful, accurate alerts in seconds, which can trigger automation events like rolling back bad code during CI/CD deployments with SignalFx’s Jenkins integrations and enabling the new automation capabilities with Amazon EventBridge. Findings are filtered in real-time to help developers spot issues and initiate fixes before they impact customers.
“We want our engineering team spending time on product and platform enhancements that make our clients happy and successful,” said Mike Hamrah, VP and Chief Architect at Namely. “The visibility SignalFx provides in real-time across our full system has allowed us to accelerate our product development because we can trust the improvements we’re making to the Namely platform. We’re excited to see SignalFx deepening its relationship with AWS to further facilitate closed-loop automation, which gives our engineers even more time to focus on delivering value to clients.”
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 ...