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Qpoint Raises $4M in Pre-Seed Funding

Qpoint closed $4 million in pre-seed funding led by Mango Capital with participation from Preface Ventures, Scribble Ventures and Bloomberg Beta.

Qpoint leverages next generation eBPF technology to give platform teams and operators unmatched visibility and control over their applications' critical external dependencies and traffic flows, to enhance reliability, maximize productivity, and safeguard sensitive data. The funds will be used to further product development and meet rising demand.

Qpoint transforms how companies oversee their external integrations by providing ops teams with a purpose-built solution that delivers real-time, granular visibility and control over the flow of traffic. Powered by cutting-edge eBPF technology that runs in the Linux kernel, Qpoint taps directly into the request flow between the primary applications and their external dependencies, providing unparalleled insights without impacting performance or requiring data to leave the environment. This enables teams to boost reliability, streamline troubleshooting, reduce cloud spend and minimize security risk with minimal operational hassle.

“Modern applications are highly dependent on the stability of external services in order to run smoothly. When you can’t easily see or control those connections, vendor issues become your interruptions and you waste countless hours trying to resolve reliability and security problems in the dark,” said Tyler Flint, co-founder and CEO of Qpoint. “By delivering comprehensive visibility and control over your applications' interactions with their external dependencies, Qpoint becomes a game changer for platform teams and site reliability engineers.”

With founding members from Shopify, Instacart, DigitalOcean, Hashicorp and NS1 (acquired by IBM), the Qpoint team has a history of success building software and scaling operations for globally influential technology companies.

Qpoint enables an operations team to tackle a wide range of integration-related use cases, including:

- External Service Reliability: Immediately identify issues or anomalous behavior with external services to minimize impacts on mission-critical applications.

- Rate Limit Detection: Continuously track external API usage, providing alerts when nearing capacity limits to prevent throttling and maintain availability for production systems.

- Root Cause Analysis and Debugging: Enable improved analysis and troubleshooting of integration-related issues for dramatically faster mean time to resolution.

- Cloud Bandwidth and Billing Attribution: Get insight into bandwidth utilization to accurately attribute resource usage and cloud costs to specific teams or projects.

- Vendor Audit Trails: Track vendor API interactions to provide clear evidence of SLA violations and ensure vendor accountability

- Zero Trust Security: Limit access to external endpoints to only those applications that have been explicitly authorized, minimizing the risk of unauthorized access and sensitive data exposure.

“Modern applications increasingly rely on a myriad of external services, which drastically increases management complexity and system reliability,” said Robin Vasan, founder and general partner at Mango Capital. “Qpoint’s novel approach leveraging eBPF and seamlessly integrating with existing solutions is a breakthrough for managing third-party dependencies and traffic flows.”

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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 ...

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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 ...

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Qpoint Raises $4M in Pre-Seed Funding

Qpoint closed $4 million in pre-seed funding led by Mango Capital with participation from Preface Ventures, Scribble Ventures and Bloomberg Beta.

Qpoint leverages next generation eBPF technology to give platform teams and operators unmatched visibility and control over their applications' critical external dependencies and traffic flows, to enhance reliability, maximize productivity, and safeguard sensitive data. The funds will be used to further product development and meet rising demand.

Qpoint transforms how companies oversee their external integrations by providing ops teams with a purpose-built solution that delivers real-time, granular visibility and control over the flow of traffic. Powered by cutting-edge eBPF technology that runs in the Linux kernel, Qpoint taps directly into the request flow between the primary applications and their external dependencies, providing unparalleled insights without impacting performance or requiring data to leave the environment. This enables teams to boost reliability, streamline troubleshooting, reduce cloud spend and minimize security risk with minimal operational hassle.

“Modern applications are highly dependent on the stability of external services in order to run smoothly. When you can’t easily see or control those connections, vendor issues become your interruptions and you waste countless hours trying to resolve reliability and security problems in the dark,” said Tyler Flint, co-founder and CEO of Qpoint. “By delivering comprehensive visibility and control over your applications' interactions with their external dependencies, Qpoint becomes a game changer for platform teams and site reliability engineers.”

With founding members from Shopify, Instacart, DigitalOcean, Hashicorp and NS1 (acquired by IBM), the Qpoint team has a history of success building software and scaling operations for globally influential technology companies.

Qpoint enables an operations team to tackle a wide range of integration-related use cases, including:

- External Service Reliability: Immediately identify issues or anomalous behavior with external services to minimize impacts on mission-critical applications.

- Rate Limit Detection: Continuously track external API usage, providing alerts when nearing capacity limits to prevent throttling and maintain availability for production systems.

- Root Cause Analysis and Debugging: Enable improved analysis and troubleshooting of integration-related issues for dramatically faster mean time to resolution.

- Cloud Bandwidth and Billing Attribution: Get insight into bandwidth utilization to accurately attribute resource usage and cloud costs to specific teams or projects.

- Vendor Audit Trails: Track vendor API interactions to provide clear evidence of SLA violations and ensure vendor accountability

- Zero Trust Security: Limit access to external endpoints to only those applications that have been explicitly authorized, minimizing the risk of unauthorized access and sensitive data exposure.

“Modern applications increasingly rely on a myriad of external services, which drastically increases management complexity and system reliability,” said Robin Vasan, founder and general partner at Mango Capital. “Qpoint’s novel approach leveraging eBPF and seamlessly integrating with existing solutions is a breakthrough for managing third-party dependencies and traffic flows.”

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.