New Relic announced support for Amazon Virtual Private Cloud (Amazon VPC) Flow Logs on Amazon Kinesis Data Firehose to reduce the friction of sending logs to New Relic.
Amazon VPC Flow Logs from AWS is a feature that allows customers to capture information about the IP traffic going to and from network interfaces in their Virtual Private Cloud (VPC). With New Relic support for Amazon VPC Flow Logs, both AWS and New Relic customers can quickly gain a clear understanding of a network’s performance and troubleshoot activity without impacting the network throughput or latency.
Network telemetry is challenging even for network engineers. To unlock cloud-scale observability, engineers need to explore VPC performance and connectivity across multiple accounts and regions to understand if an issue started in the network or somewhere else. To solve this, New Relic has streamlined the delivery of Amazon VPC Flow Logs by allowing engineers to send them to New Relic via Kinesis Data Firehose, which reliably captures, transforms, and delivers streaming data to data lakes, data stores, and analytics services. With New Relic’s simple “add data” interface, it only takes moments to configure Amazon VPC Flow Logs using the AWS Command Line Interface (AWS CLI) or an AWS CloudFormation template. Instead of digging through raw logs across multiple accounts, any engineer can begin with an Amazon Elastic Compute Cloud (Amazon EC2) instance they own and begin to explore the data that matters, regardless of the AWS account or AWS Region.
“New Relic continues to invest in our relationship with AWS. Helping customers gain visibility into their cloud networking environment increases their overall application observability,” said Riya Shanmugam, GVP, Global Alliances and Channels at New Relic.
“New Relic’s connected experience for Amazon VPC Flow Logs, paired with the simplicity of using Kinesis Data Firehose, enables our joint customers to easily understand how their networks are performing, troubleshoot networking issues more quickly, and explore their VPC resources more readily.” said Nishant Mehta, Director of PM – EC2 and VPC Networking at AWS.
With the New Relic support for Amazon VPC Flow Logs on Kinesis Data Firehose, customers can:
- Monitor and alert on network traffic from within New Relic.
- Visualize network performance metrics such as bytes and packets per second, as well as accepts and rejects per second across every TCP or UDP port.
- Explore flow log deviations to look for unexpected changes in network volume or health.
- Diagnose overly restrictive security group rules or potentially malicious traffic issues.
The New Relic support for Amazon VPC Flow Logs on Kinesis Data Firehose is available to all New Relic Full Platform users.
The Latest
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 ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...