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SignalFx Extends Monitoring as Code Capabilities with Official Terraform Provider Certified by HashiCorp

SignalFx announced its Terraform provider has been certified by HashiCorp, the creators of Terraform.

The official SignalFx Terraform provider gives DevOps teams the ability to programmatically create, manage, and version control SignalFx charts, dashboards, and detectors.

“We’re pleased to see the launch of this new provider by the SignalFx team and appreciate the collaboration that went into this release,” said Burzin Patel, VP of Worldwide Alliances, HashiCorp. “Our goal with Terraform is to allow users to incorporate any service as part of their provisioning workflow. This provider means that SignalFx users can accomplish exactly that.”

HashiCorp Terraform is used by developers and operators to programmatically create and manage infrastructure resources as code. The SignalFx Terraform provider allows real-time charts, dashboards, and alerts to be created and managed in the same way for monitoring as code. These capabilities are essential for DevOps and Site Reliability Engineering (SRE) teams in large enterprises maintaining a centralized observability service for the entire organization.

“Monitoring as code is a vital tool for empowering engineers, reducing the friction of change, and establishing best practices,” said Cory Watson, Technical Director, SignalFx. “Beyond version control, Terraform’s modularity enables best practices and reuse. Official certification by HashiCorp provides our joint customers with automatic installation and confidence that the SignalFx provider has met HashiCorp’s strict quality standards.”

SignalFx already offers software operators the ability to identify and alert on anomalies in seconds thanks to its architecture, which leverages real-time streaming analytics and NoSample™ tail-based distributed tracing. This certification as an official Terraform provider builds on these established performance advantages by further enabling shared visibility and monitoring best practices, both of which are critical to running a monitoring and observability service at enterprise scale.

Earlier this year, SignalFx released major platform updates featuring enhancements to SignalFx Service Bureau, which provides a unique set of capabilities that enable centralized observability teams to efficiently provide the entire organization with an easy-to-consume observability service, through features like Mirrored Dashboards and Metrics Finder.

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SignalFx Extends Monitoring as Code Capabilities with Official Terraform Provider Certified by HashiCorp

SignalFx announced its Terraform provider has been certified by HashiCorp, the creators of Terraform.

The official SignalFx Terraform provider gives DevOps teams the ability to programmatically create, manage, and version control SignalFx charts, dashboards, and detectors.

“We’re pleased to see the launch of this new provider by the SignalFx team and appreciate the collaboration that went into this release,” said Burzin Patel, VP of Worldwide Alliances, HashiCorp. “Our goal with Terraform is to allow users to incorporate any service as part of their provisioning workflow. This provider means that SignalFx users can accomplish exactly that.”

HashiCorp Terraform is used by developers and operators to programmatically create and manage infrastructure resources as code. The SignalFx Terraform provider allows real-time charts, dashboards, and alerts to be created and managed in the same way for monitoring as code. These capabilities are essential for DevOps and Site Reliability Engineering (SRE) teams in large enterprises maintaining a centralized observability service for the entire organization.

“Monitoring as code is a vital tool for empowering engineers, reducing the friction of change, and establishing best practices,” said Cory Watson, Technical Director, SignalFx. “Beyond version control, Terraform’s modularity enables best practices and reuse. Official certification by HashiCorp provides our joint customers with automatic installation and confidence that the SignalFx provider has met HashiCorp’s strict quality standards.”

SignalFx already offers software operators the ability to identify and alert on anomalies in seconds thanks to its architecture, which leverages real-time streaming analytics and NoSample™ tail-based distributed tracing. This certification as an official Terraform provider builds on these established performance advantages by further enabling shared visibility and monitoring best practices, both of which are critical to running a monitoring and observability service at enterprise scale.

Earlier this year, SignalFx released major platform updates featuring enhancements to SignalFx Service Bureau, which provides a unique set of capabilities that enable centralized observability teams to efficiently provide the entire organization with an easy-to-consume observability service, through features like Mirrored Dashboards and Metrics Finder.

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The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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