kipi.bi announced the launch of its Watchkeeper™ Monitoring Accelerator for Snowflake.
Driven by real-time data insights, this new Accelerator provides out-of-the-box optimization to organizations looking to enhance monitoring and observability in their Snowflake environment.
As a Snowflake Elite Services Partner, kipi.bi designed Watchkeeper™ to enable Snowflake customers to self-serve monitor cloud compute and storage resources, credit consumption, performance, and security governance. With this unified monitoring solution, users can shift their approach to analytics from descriptive to diagnostic and maximize their data investments.
“With Watchkeeper, enterprises now have a single source of truth to review and action their monitoring results, and can more easily identify errors or inefficiencies that may be impacting query response times and user experience,” said Rakesh Reddy, CTO at kipi.bi.
The Watchkeeper Monitoring Accelerator supports over 120 native KPIs and empowers business leaders and decision-makers to better understand historical trends and effectively forecast, enables IT development teams to optimize pipelines and improve load times, and assists administrators in enforcing governance and security across their organization.
Watchkeeper supports a variety of visualization tools, including Tableau, Streamlit, and Snowflake’s out-of-the-box web interface - Snowsight, and can deploy packaged dashboards in minutes, empowering customers with on-the-go visualization at their fingertips.
With custom alerts, Watchkeeper integrates with email, Slack, ServiceNow, and other API-based systems to allow notifications at predefined thresholds or create service tickets with end-to-end automation.
“Watchkeeper maintains an exact view on business-critical metrics within an organization’s Snowflake platform,” said Sumit Bhatia, Vice President of Technology at kipi.bi. “It empowers Business Stakeholders, Administrators, and Technical teams with real-time insights into the health of their Snowflake environment to quickly enact change and more effectively plan and allocate resources, govern risk, and augment performance.”
The Watchkeeper Monitoring Accelerator is one of a series of new Accelerators and Solutions by kipi.bi.
The Latest
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 ...
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.