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Spotinst Rebrands as Spot and Launches Cloud Analyzer

Spotinst – now rebranded as Spot – unveiled a new company name and brand while launching a new product, Cloud Analyzer.

Using Cloud Analyzer, any company can, for the first time, have visibility and recommendations that automate performance and availability and minimize costs across all their cloud accounts.

“In unstable times such as those we are all facing today, organizations must be able to adapt and align their cloud infrastructure and costs, as well as their ability to scale up or down, with their constantly changing and unpredictable needs,” said Amiram Shachar, Founder & CEO of Spot. “Using machine learning algorithms, automation, and now with broader visibility into spend and usage - Cloud Analyzer is able to free up CloudOps teams to deploy applications fast, while maintaining uptime at lowest possible cloud infrastructure costs.”

Cloud Analyzer provides a holistic view into all of a company’s multi cloud accounts, spending trends and anomalies, gaining unprecedented visibility into every potential opportunity to optimizee their cloud. It integrates with Spot’s other products to automatically eliminate the inefficiencies it has identified, and make it possible for CloudOps to handle huge changes in demand.

The ability to handle such changes is critical during events such as the coronavirus outbreak. Our data shows there has been up to 20x greater volatility in the cloud capacity and demand in recent weeks and because it’s so difficult to adapt to such fluctuations with manual solutions, it’s an area where clear data-driven automation is a necessity.

Since its launch in 2015, Spot’s advanced machine learning software uses automation and analytics to continuously optimize cloud infrastructure. It has been used by some of the world’s most successful companies – such as Samsung and Sony – to save millions on their cloud infrastructure costs and maintain performance and availability.

Cloud Analyzer not only helps companies understand and manage loads, it also identifies significant savings opportunities for customers. Early testing has identified hundreds of thousands and even multi-million-dollar potential savings for a range of companies including a regional financial services company, a marketing software solution company and a communications company.

In addition to bringing value to enterprises, Cloud Analyzer has potential to be useful for managed service providers (MSPs), too. As part of its future plans for Cloud Analyzer, Spot is currently working with partners to deliver a tailored version of Cloud Analyzer that will enable MSPs to provide their customers with insights into cloud usage and to maximize the cost efficiency of their offerings.

Cloud Analyzer is now generally available.

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Spotinst Rebrands as Spot and Launches Cloud Analyzer

Spotinst – now rebranded as Spot – unveiled a new company name and brand while launching a new product, Cloud Analyzer.

Using Cloud Analyzer, any company can, for the first time, have visibility and recommendations that automate performance and availability and minimize costs across all their cloud accounts.

“In unstable times such as those we are all facing today, organizations must be able to adapt and align their cloud infrastructure and costs, as well as their ability to scale up or down, with their constantly changing and unpredictable needs,” said Amiram Shachar, Founder & CEO of Spot. “Using machine learning algorithms, automation, and now with broader visibility into spend and usage - Cloud Analyzer is able to free up CloudOps teams to deploy applications fast, while maintaining uptime at lowest possible cloud infrastructure costs.”

Cloud Analyzer provides a holistic view into all of a company’s multi cloud accounts, spending trends and anomalies, gaining unprecedented visibility into every potential opportunity to optimizee their cloud. It integrates with Spot’s other products to automatically eliminate the inefficiencies it has identified, and make it possible for CloudOps to handle huge changes in demand.

The ability to handle such changes is critical during events such as the coronavirus outbreak. Our data shows there has been up to 20x greater volatility in the cloud capacity and demand in recent weeks and because it’s so difficult to adapt to such fluctuations with manual solutions, it’s an area where clear data-driven automation is a necessity.

Since its launch in 2015, Spot’s advanced machine learning software uses automation and analytics to continuously optimize cloud infrastructure. It has been used by some of the world’s most successful companies – such as Samsung and Sony – to save millions on their cloud infrastructure costs and maintain performance and availability.

Cloud Analyzer not only helps companies understand and manage loads, it also identifies significant savings opportunities for customers. Early testing has identified hundreds of thousands and even multi-million-dollar potential savings for a range of companies including a regional financial services company, a marketing software solution company and a communications company.

In addition to bringing value to enterprises, Cloud Analyzer has potential to be useful for managed service providers (MSPs), too. As part of its future plans for Cloud Analyzer, Spot is currently working with partners to deliver a tailored version of Cloud Analyzer that will enable MSPs to provide their customers with insights into cloud usage and to maximize the cost efficiency of their offerings.

Cloud Analyzer is now generally available.

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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 ...