Selector announced Series A round for $28 million, bringing total funding to $33M.
The company was founded in 2019 by former Juniper Networks executives and industry veterans, Kannan Kothandaraman and Nitin Kumar. Selector Analytics provides hyper speed-to-value for operations with usable AIOps and zero-touch analytics across any and all data. This round of funding was led by Two Bear Capital, SineWave Ventures, and Atlantic Bridge. Comcast Ventures and Azure Capital Partners are also investors in the company. The new funds will be used to execute Selector’s go-to-market (GTM) strategy, invest in R&D, and expand its market reach in North America. The platform is already successfully deployed by multiple Fortune 500 customers.
This announcement marks the official launch of Selector Analytics, which normalizes clusters and correlates metrics, logs, configuration, events, and alerts from multi-cloud network, application, and security data sources. The platform delivers actionable insights through an intuitive user experience that enables operations to proactively diagnose and remediate issues. Unique to Selector Analytics is the ability to use popular collaboration tools such as Slack and Microsoft Teams, and get insights in seconds using natural language. Such Google-like simplicity eliminates hours of painstaking data hunting and gathering, enabling teams to improve mean time to repair (MTTR) while increasing efficiency, slashing operational overhead and eliminating downtime.
Two Bear Capital, SineWave Ventures and Atlantic Bridge invested in Selector because of the team’s extensive experience and deep domain expertise in networking, applications, and machine learning. The platform’s ability to ingest all manner of data from any data source and provide actionable observability is critical to teams working on multicloud infrastructures.
“At Selector, we have assembled a world-class team looking to transform the AIOps space. By merging networking, AIOps, and observability into a single platform, Selector is approaching the space uniquely. Our mission is to eliminate downtime and empower operations with tools to be more efficient and productive in their jobs,” said Kannan Kothandaraman, Co-Founder and CEO, Selector. “Our customers are having amazing success with the platform and are expanding to new problems they once thought unsolvable.”
Multicloud services are critical to the next phase of internet growth and the economy as a whole. The ability to rapidly triage complex cloud environments requires instant analysis across multiple domains, which is costly and time-consuming to achieve using manual methods for data aggregation and event correlation. Historically, operations teams have had to cross both organizational and tool boundaries to draw actionable insights using many static dashboards – a reactive, often ineffective approach. By contrast, Selector Analytics empowers operations teams to instantly detect anomalies in an environment of increasing complexity and criticality, and maximize the potential of multicloud.
“Existing monitoring solutions are single-domain, manual, and complex, making rapid event correlation and analysis tedious, frustrating, and often impossible,” said Nitin Kumar, Co-Founder and CTO of Selector. “Selector abstracts all of that complexity and enables automated remediation using elastic data ingestion, declarative transformations, and data-centric machine learning.”
The Latest
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 ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...