Apica announced new capabilities for its Apica Ascent Platform.
Following the company’s acquisitions of data fabric innovator LOGIQ.AI and telemetry data management pioneer Circonus, Apica has integrated capabilities from both organizations into its platform to provide deeper insights into data management. This makes it easier for customers to gather the needed data and seamlessly apply it to their systems and platforms.
In addition to making two acquisitions over the last 10 months, Apica announced $16M in new capital. With this funding, it expanded its Office of the CTO and strengthened its engineering, research, and development teams. These actions are critical to achieving the integrations necessary to announce Apica Ascent 2.0 today.
“Apica is built on the premise of being flexible and meeting enterprise customers where they are,” said Ranjan Parthasarathy, Chief Technology and Product Officer at Apica. “Organizations struggle to manage tool sprawl, so we’ve provided the platform that ensures their legacy and digital transformation strategies work well together. They don’t have to leave their legacy tools behind while modernizing – Apica can help make everything work together to provide insights needed for the business. We regularly hear from customers and in peer reviews that we are easy to implement, use, and manage, and our costs are the lowest.”
The Ascent platform 2.0 is the outcome of Apica’s strategic approach of looking at data management as a fundamental problem that underlies the challenges companies face when dealing with machine data / operational data. The company first introduced the platform in 2022 and focused on monitoring. As customers’ needs changed, Apica adapted to solve their data management problems as a true observability and data management company. Through strategic acquisitions that are now incorporated into the Ascent platform, the company continues to modernize observability. The platform covers a gamut of things, including network, performance, metrics around compute, memory, storage, and file systems, among others as well as gathering application data like logs, metrics, and traces from organizations’ runtime applications.
Apica posits that observability is a data problem; therefore, providing the necessary controls to introduce data transformation and cleansing capabilities is essential. This is part of the company’s data management strategy, where customers can now have complete control over their data challenges of growing data volume, rising data cost, and poor data quality.
“Many tools on the market solve the search problem and not the data problem. We’ve been solving the data problem as a fundamental underpinning of our data management architecture,” added Parthasarathy. “And, we support an approach that does not stick our clients in a walled garden. With the onset of Generative AI, we now face even more data growth. Customers will also need open-source solutions to keep costs down and avoid a situation with proprietary tech where data is closed off. Apica embraces anything and everything to support ecosystem connectors, enabling open protocols and data collectors to be available in the open ecosystem, and we’re not done yet. We continue to work towards open source to provide the most extensive coverage and help customers control their data better.”
Apica Ascent 2.0 Platform Capabilities
- Consumer-grade UX with a single pane of glass for all your machine data workflows: Digital User Experience Monitoring, Telemetry Pipeline management and Full stack observability
- Simplified data storage at scale, powered by our patented InstaStore technology, in any cloud environment
- Deep integration with open-source software and emerging technologies such as OpenTelemetry
- Built on open data standards, allowing support for a broad ecosystem of data collectors and data integrations out of the box
- Powered by AI/ML and Generative AI for rapid troubleshooting
The Latest
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...
For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...
Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...