Digitate has announced the general availability of their flagship product ignio™ on Amazon Web Services (AWS).
The company’s innovations integrated with key AWS products and services have resulted in significant improvements in performance, scalability, and cost-effectiveness and are already driving benefits for global Fortune 500 customers.
Digitate provides unified observability, AI-powered insights, and close-loop automation integrated with AWS technologies such as Amazon EKS and Amazon Aurora. Its offerings are tailored to global businesses with complex technology landscapes operating in a variety of industries, including retail, banking, finance, manufacturing, utilities, healthcare, and life sciences.
Digitate’s ignio AI platform accelerates the automation of IT lifecycle tasks from triaging to resolution. It also enables predictive and preventive operations through intelligent recommendations and self-healing. By rearchitecting ignio’s SaaS environment using AWS cloud-native technologies, Digitate enhances global scalability, sustainability, and cost-effectiveness across IT and business operations, delivering agility, assurance, and resiliency for its global enterprise customers.
"Our collaboration with AWS has been transformative," said Rahul Kelkar, Chief Product Officer, Digitate. "Leveraging AWS's cloud-native technologies accelerates the time-to-market of ignio’s products and solutions. This association lowers our bill-of-materials, reduces costs, and simplifies our SaaS operations. The elasticity and auto-scaling capabilities of AWS have proven particularly beneficial for ignio, enabling a more efficient pay-as-you-go model.”
The collaboration with AWS streamlines Digitate's go-to-market strategy. By leveraging AWS's process-driven approach to Independent Software Vendor partnerships and global scale, Digitate is positioned to expand its market reach and accelerate customer acquisition. As Digitate continues to expand its presence on the AWS Marketplace, the company looks forward to driving further innovation and delivering enhanced value to its growing customer base.
The Latest
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 ...
Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...