Skip to main content

Pivotal Introduces Pivotal One Next-Generation Multi-Cloud Enterprise PaaS

Pivotal announced the availability of Pivotal One, a comprehensive, multi-cloud Enterprise PaaS comprised of a set of application and data services that run on top of Pivotal CF, an enterprise distribution of the Cloud Foundry platform.

This new solution will allow enterprise companies to bring new innovation to market faster than ever before, enabling agile development teams to rapidly update and scale applications across public or private clouds, and instantly expand and upgrade with no downtime.

New Pivotal One services will work in conjunction with Pivotal CF to integrate differentiated data services such as Hadoop and visual analytics into the enterprise PaaS experience. With its rich portfolio of data assets and deep programming experience via Pivotal Labs, Pivotal is uniquely positioned to bring an offering like this to market.

As software continues to disrupt a wide variety of industries, there’s been a decided shift in the platforms and processes used to support these businesses, with many looking to PaaS offerings to help them develop new applications quicker and at great scale. Working in agile teams, developers demand a platform that allows them to continuously deliver updates and horizontally scale their applications with no downtime. They seek standardized ways to plug in leading data services and perform deep user analytics on top of massive data sets to drive rapid iteration based on customer needs.

With today’s announcement, Pivotal delivers on its promise to enable the creation of modern software applications that leverage big and fast data on a single, cloud independent platform. Companies can now analyze massive data repositories in their business applications using familiar interfaces, on a common virtual environment running in their private data centers – allowing them to provide more business value and new offerings for their customers. Time-to-value for this new class of applications is dramatically improved, allowing operators to continuously update, manage and scale this integrated platform with no production downtime.

The Pivotal CF enterprise Cloud Foundry platform is a key enabler for modern, software-driven organizations. It includes:

- Pivotal CF Elastic Runtime Service – Provides a complete, scalable runtime environment, extensible to most modern frameworks or languages running on Linux. Deployed applications leverage built-in services, and can automatically bind to new data services or to an existing user provided service.

- Pivotal CF Operations Manager – Turnkey enterprise PaaS management platform with IaaS integration that enables zero-downtime patching and updates to the platform without service interruption.

- Pivotal One Services – Available add-ons such as Pivotal HD, Pivotal AX, Pivotal RabbitMQ, and MySQL include automatic application binding and service provisioning.

- Pivotal HD Service – The Pivotal HD service for Pivotal CF enables cloud operators to build, manage, and scale Hadoop as a natively integrated Pivotal CF Service. Via the Service Broker, applications can bind to this service automatically assigning capacity in HDFS, a database in HAWQ, and a resource queue in YARN. For application developers this reduces development cycle time by eliminating the typical complexities around deployment, security, networking, and resource management that are commonly associated with developing applications on Hadoop.

- Pivotal AX Service – Pivotal AX is next-generation analytics software that is purpose-built on Pivotal HD and deploys and scales as the Pivotal CF Service. Enterprises and Service Providers can offer a self-service analytics environment to each company’s division that addresses the creation, collection, storage, query, and visualization of data.

- Pivotal RabbitMQ Service – Pivotal RabbitMQ is a message broker for applications running on Pivotal CF. With Pivotal RabbitMQ, applications can integrate with applications both within and outside of Pivotal CF.

- MySQL Service – Using the MySQL service, enterprises can provision multi-tenant, single instance MySQL databases suitable for rapid application development and testing.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Pivotal Introduces Pivotal One Next-Generation Multi-Cloud Enterprise PaaS

Pivotal announced the availability of Pivotal One, a comprehensive, multi-cloud Enterprise PaaS comprised of a set of application and data services that run on top of Pivotal CF, an enterprise distribution of the Cloud Foundry platform.

This new solution will allow enterprise companies to bring new innovation to market faster than ever before, enabling agile development teams to rapidly update and scale applications across public or private clouds, and instantly expand and upgrade with no downtime.

New Pivotal One services will work in conjunction with Pivotal CF to integrate differentiated data services such as Hadoop and visual analytics into the enterprise PaaS experience. With its rich portfolio of data assets and deep programming experience via Pivotal Labs, Pivotal is uniquely positioned to bring an offering like this to market.

As software continues to disrupt a wide variety of industries, there’s been a decided shift in the platforms and processes used to support these businesses, with many looking to PaaS offerings to help them develop new applications quicker and at great scale. Working in agile teams, developers demand a platform that allows them to continuously deliver updates and horizontally scale their applications with no downtime. They seek standardized ways to plug in leading data services and perform deep user analytics on top of massive data sets to drive rapid iteration based on customer needs.

With today’s announcement, Pivotal delivers on its promise to enable the creation of modern software applications that leverage big and fast data on a single, cloud independent platform. Companies can now analyze massive data repositories in their business applications using familiar interfaces, on a common virtual environment running in their private data centers – allowing them to provide more business value and new offerings for their customers. Time-to-value for this new class of applications is dramatically improved, allowing operators to continuously update, manage and scale this integrated platform with no production downtime.

The Pivotal CF enterprise Cloud Foundry platform is a key enabler for modern, software-driven organizations. It includes:

- Pivotal CF Elastic Runtime Service – Provides a complete, scalable runtime environment, extensible to most modern frameworks or languages running on Linux. Deployed applications leverage built-in services, and can automatically bind to new data services or to an existing user provided service.

- Pivotal CF Operations Manager – Turnkey enterprise PaaS management platform with IaaS integration that enables zero-downtime patching and updates to the platform without service interruption.

- Pivotal One Services – Available add-ons such as Pivotal HD, Pivotal AX, Pivotal RabbitMQ, and MySQL include automatic application binding and service provisioning.

- Pivotal HD Service – The Pivotal HD service for Pivotal CF enables cloud operators to build, manage, and scale Hadoop as a natively integrated Pivotal CF Service. Via the Service Broker, applications can bind to this service automatically assigning capacity in HDFS, a database in HAWQ, and a resource queue in YARN. For application developers this reduces development cycle time by eliminating the typical complexities around deployment, security, networking, and resource management that are commonly associated with developing applications on Hadoop.

- Pivotal AX Service – Pivotal AX is next-generation analytics software that is purpose-built on Pivotal HD and deploys and scales as the Pivotal CF Service. Enterprises and Service Providers can offer a self-service analytics environment to each company’s division that addresses the creation, collection, storage, query, and visualization of data.

- Pivotal RabbitMQ Service – Pivotal RabbitMQ is a message broker for applications running on Pivotal CF. With Pivotal RabbitMQ, applications can integrate with applications both within and outside of Pivotal CF.

- MySQL Service – Using the MySQL service, enterprises can provision multi-tenant, single instance MySQL databases suitable for rapid application development and testing.

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...