AppFirst unveiled the enterprise version of its platform with the option to securely run on-premise, in the cloud or in hybrid scenarios. The company also announced a partnership with Accretive to deliver a predictive analytics offering to help enterprises expose, anticipate and intervene in IT risks across organizational silos. This partnership, coupled with AppFirst’s enterprise-grade platform, provides unparalleled insight and predictive capabilities across all layers of an organization's application stack.
AppFirst’s enterprise platform represents a paradigm shift from traditional monitoring and logging tools. It establishes a complete and universal timeline of events across an enterprise application environment - at a sub-millisecond level. This includes every application call, system event, log file entry, configuration change, third party application or custom code event, and data from thousands of plug-ins. This is accomplished through a patented data collection and aggregation methodology that provides an unmatched level of visibility - all correlated into a universal timeline that can live in the cloud, on-premise or in a hybrid environment. Armed with this platform, enterprises are now able to achieve granular and continuous visibility between IT and the business.
“AppFirst is redefining what’s possible for executives looking to optimize the technology that runs their business. And it starts with setting a single sub-millisecond timeline across the entire enterprise stack. Unless you start with perfect data, you cannot achieve true visibility,” said David Roth, CEO and Co-Founder of AppFirst. “We are applying deep predictive analytics and applications on top of the most detailed data in the world. Taken together, this finally offers CIOs the ability to truly manage cost, risk and capacity without fingerpointing or guessing.”
New features of the AppFirst platform include:
- Real-time service topology mapping: Customers now have the ability to auto-discover and map their service and application topologies in real-time. AppFirst auto-installs under new custom or third-party application components as they come into your environment. Therefore, you’ll always have an up-to-date view of what is running and how it is running across your enterprise.
- Predictive analytics: Leveraging predictive analytic models updated in real-time, IT and business leaders can identify system limits, predict issues across the enterprise, and perform what-if analysis on how changing complexity will impact performance quality and cost.
On-premise deployments: In addition to the core SaaS & Private SaaS offering, customers can easily deploy AppFirst behind their firewall. This is critical for enterprise customers in highly regulated environments and in countries where data needs to securely reside within their borders.
Starting with Accretive, AppFirst will be announcing a series of enterprise focused applications leveraging this platform, consisting of those developed by AppFirst, various partners and the enterprise community.
“During the past 12-18 months, the use cases for our platform have extended far beyond incident response and troubleshooting,” continued Roth. “Our partnership with Accretive is the direct result of enterprise organizations looking to holistically optimize the business from the top - all the way down to the database or web server. We are now able to reset the standard for how organizations bridge business to IT.”
“Only the combination of AppFirst and Accretive provides business leaders with the real-time and forward-thinking visibility required for true IT continuity and optimization,” said Dr. Nabil Abuelata, CEO and Founder of Accretive. “Unlike traditional data warehousing and big data analytics technologies, it’s now possible to feed and update predictive models in real-time with a unified dataset to understand what is happening now and in the future. This capability delivers tremendous value to both the core business and IT operations. We are excited to work with AppFirst to change the way enterprises think about optimizing the entire technology chain that supports their business.”
AppFirst’s enterprise platform is now available for customers worldwide.
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 ...