Kentik announced a partnership with New Relic to help customers better understand the relationship between application and network performance.
Through the new service Kentik Firehose, engineers and operations professionals will gain access to enriched, contextual network telemetry correlated with their application data, including New Relic’s full-stack performance data, to address use cases such as troubleshooting complex applications, detecting and geo-locating network threats affecting their applications, and understanding the role of cloud infrastructure on application performance.
“By partnering with Kentik, we are enhancing the understanding of application performance in the context of network conditions,” said Raj Ramanulam, VP of Alliances and Channels, New Relic. “New Relic data and analysis reveal what is happening inside the app environment through end-user performance and correlating it with network performance results, all in a single dashboard. Now users can analyze how much traffic their app generates and how it is affected by network conditions.”
Underlying this integration is Kentik Firehose, a newly-released product that gives partners such as those providing DevOps observability or business intelligence access to a vast array of enriched network observability data, including flow records, streaming telemetry, SNMP, device configurations and performance metrics, in a scalable, high-speed way through standard interfaces and formats.
Kentik Firehose can publish to New Relic, Splunk, Kafka, Prometheus, Grafana, stdout and output data in JSON, NetFlow, Avro and Influx formats. It can clean, groom, enrich and correlate network intelligence, and deliver it in a scalable, distributed, real-time manner for business, development, operations and security use cases, something no other network observability solution in the market can do.
“Network telemetry is one of the richest sources of information for understanding user trends, application usage and security posture. However, most observability systems do little more than ingest Syslog data, leaving IT departments, DevOps or SREs with an incomplete view”, said Avi Freedman, CEO of Kentik. “Kentik provides network intelligence from the internet, edge, cloud, data center, and hybrid infrastructures and enriches that data with geo, BGP attributes, custom dimensions and interface classification for business context.”
The Latest
AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...
In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...
Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...
Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...
As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...