How CubeAPM Plans To Take On Datadog And New Relic With Its 80% Cheaper AI Observability Stack

SUMMARY

• CubeAPM is building an observability platform to challenge global players such as Datadog and New Relic with a lower-cost, self-hosted architecture.
• As enterprises generate more telemetry and face rising monitoring costs, CubeAPM is betting that predictable pricing and infrastructure efficiency will make enterprise switch to its platform compelling.
• Founded by Trainman’s Vineet Chirania and former BharatPe CTO Vijay Aggarwal, CubeAPM serves 50 enterprise customers, is clocking $1.5 Mn in ARR and has remained profitable since inception.

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When Vineet Chirania was scaling his train-ticket booking startup, Trainman, a decade ago, he encountered a problem that would eventually inspire his next venture. His team relied on Datadog, an application monitoring tool, to track the performance of its software systems. Then one day, the startup received an unexpectedly high bill after one of its developers started feeding additional custom metrics into Datadog.

Over at BharatPe, senior engineering executive Vijay Aggarwal was facing a similar challenge with another observability giant, New Relic. 

Both Aggarwal and Chirania quickly realised the problem. As apps grow and generate more telemetry data, monitoring costs become increasingly difficult to forecast. Add concerns around data residency and dashboard latency, and the pain points become hard to ignore.

These experiences eventually led the duo to build CubeAPM, an observability platform that claims to reduce enterprise monitoring costs by as much as 60-80%, while keeping customer data within their own cloud environments.

Today, the bootstrapped startup serves around 50 enterprise customers, including Delhivery, RedBus, PolicyBazaar, Shadowfax and the Ola Group. Simultaneously, CubeAPM claims to be churning an annual recurring revenue (ARR) of nearly $1.5 Mn, up roughly 3-4X over the past year, all while remaining profitable since inception.

But CubeAPM’s ambitions extend beyond being an affordable alternative to incumbents. The startup is betting that its architecture, designed around self-hosting, data residency and AI-assisted troubleshooting, can help it carve a place in the global observability market, which is estimated to become a $20 Bn opportunity by 2031.

From Trainman To CubeAPM

The origins of CubeAPM are deeply rooted in the founders’ operational experiences. Chirania spent much of his career building Trainman, which eventually scaled to more than 30 Mn app downloads before being acquired by the Adani Group.

Aggarwal, meanwhile, built his reputation leading engineering teams at high-scale internet startups. Before founding CubeAPM, he served as the CTO of BharatPe and earlier headed engineering at Grofers (now Blinkit). 

Both hail from IIT Roorkee. After Chirania exited Trainman, the two reconnected and discovered that they had experienced the same frustrations with observability software.

“The common thread was application performance monitoring. We had used different tools, but the problems were remarkably similar: unpredictable pricing, poor visibility into future costs, and challenges around data residency,” said Chirania.

Initially, the founders believed modest cost savings would be sufficient to convince enterprises to switch platforms. But most observability purchases are displacement sales. Customers already have a monitoring platform in place, and migrating critical infrastructure monitoring systems requires massive engineering effort. Saving 15% or 20% wasn’t compelling enough.

“We realised customers were willing to migrate only if the economics were dramatically better, sending us back to the drawing board,” Chirania said.

Reinventing The Observability Architecture

At its core, observability software helps engineering teams understand what is happening inside their apps. When a website slows down or a payment service crashes, observability platforms collect telemetry data, such as logs and metrics, to help teams identify the root cause and resolve issues quickly.

However, scaling observability is a key challenge as enterprises generate massive volumes of telemetry data, making real-time storage, indexing and analysis increasingly expensive and resource-intensive. Moreover, traditional observability vendors such as Datadog and New Relic typically operate SaaS-based models, where customer data is sent to the vendor’s cloud for processing and storage.