Puppet Labs announced the next major update to its flagship product: Puppet Enterprise 2015.2.
This release includes new features providing DevOps teams with clarity, simplicity and additional management capabilities, including an all-new user interface, an interactive graph for visualizing infrastructure code, a new unified agent and broader infrastructure support.
With Puppet Enterprise 2015.2, IT teams have a visual representation of the models defined by their code, making it easier to respond to changes quickly.
"One of the fundamental tenets of DevOps is to manage your infrastructure as code. It makes practices like unit testing, peer review, version control and continuous delivery possible,” said Nigel Kersten, CIO of Puppet Labs. “I’m thrilled that, for the first time, users can now visualize their code — actually see the models they build. This kind of visualization and insight will become more important for teams as they manage an increasingly complex set of technologies and infrastructure."
New User Interface: Puppet Enterprise is rapidly becoming the lingua franca of the data center. More and more organizations rely on Puppet Enterprise, extending it across diverse teams and scaling it to their rapidly expanding infrastructure to manage compute, storage and networking from a single platform. Puppet Enterprise 2015.2 offers a new user interface for managing large, complex infrastructures, including the first interactive graph for visualizing infrastructure code.
Interactive Node Graph: Puppet Enterprise 2015.2 includes a dynamic, interactive graph that visualizes infrastructure models that admins have defined with their Puppet code. Now that teams can visualize the models they’ve built, it’s easier to optimize their code and respond to changes faster, ultimately reducing the time it takes to return infrastructure to the correct, desired state when it diverges.
Why Code Visualization Matters: DevOps-driven organizations have multiple code contributors from different teams across IT. This makes collaboration key to creating agile, maintainable and robust code. Visualizing the models defined by infrastructure code helps multiple teams identify where the models can be improved to minimize failures, improve change response time and further enable collaboration around infrastructure. The 2015 State of DevOps Report, Puppet Labs annual report and survey polling more than 5,000 IT professionals, points out that a key component of success is the ability to provide entire teams with visualizations that show entire teams the state of work in progress and the quality of that work.
Inventory Filtering: It's now easy to filter and search thousands of puppetized nodes and quickly locate infrastructure with specific characteristics. This is especially powerful for organizations that use Puppet Enterprise to track operating system updates, server migrations, warranty expirations and other factors that change across their infrastructure. For example, if you want to migrate from Windows Server 2003 to Windows Server 2012, the new node filtering makes it simple to track the burndown of 2003 and uptake of 2012 across your data center.
New Unified Agent: The 2015.2 release unifies the open source and commercial agent technologies on the same codebase. The result is a simpler, easier upgrade path for the thousands of organizations around the world currently using Open Source Puppet that want to take advantage of additional innovation.
Further improvements to core agent technologies make the Puppet agent lighter and faster. For example, one of the core agent components is now at least 20 times faster, and has a 50 percent smaller memory footprint.
Puppet Language Changes: Puppet’s simple programming language is the most widely used means of describing and managing infrastructure. The newest release of Puppet Enterprise includes major enhancements to the Puppet language that make it easier to write and maintain even more powerful Puppet code. Enhancements include iteration, parameter type checking and better error handling, in addition to changes that improve the usability, completeness and consistency of the language itself.
Additional Infrastructure Support: Last year, Puppet Labs introduced Puppet Supported modules, assuring customers of professional support for managing their most commonly used and critical infrastructure, such as Amazon Web Services, Docker, Microsoft SQL Server, Tomcat, and more. As part of the 2015.2 release, Puppet Labs is announcing availability of a couple new Puppet Supported modules:
- Citrix NetScaler. A new Puppet Supported module eliminates errors with load balancing and content switching, by enabling application delivery controller devices to match the same defined configurations as computing infrastructure.
- VMware vSphere. A new Puppet Supported module accelerates provisioning of virtual machines, and allows IT teams to launch and configure virtual machines and manage CPU, memory and machine configuration over time.
The Latest
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 ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.