AI
CubeAPM, founded by Trainman’s Vineet Chirania and former BharatPe CTO Vijay Aggarwal, is targeting global observability players such as Datadog and New Relic with a self-hosted platform designed to reduce enterprise monitoring costs. The bootstrapped startup currently serves around 50 enterprise customers and claims nearly $1.5 million in ARR while remaining profitable since inception.
Its architecture runs inside customers’ own cloud environments, keeping telemetry data within their infrastructure while addressing data residency, latency and data-transfer costs. CubeAPM claims its proprietary compression and storage technology can reduce 100 GB of incoming telemetry to roughly 4 GB of stored data. Its pricing is primarily based on data ingestion, with users, hosts, retention and support bundled into the model.
The platform covers APM, log management, infrastructure monitoring, real-user monitoring, synthetic monitoring and error tracking. It has also introduced an MCP server that allows AI coding assistants to access telemetry for conversational troubleshooting.
The Bigger Picture
As enterprises generate growing volumes of telemetry from cloud-native and AI-driven applications, observability costs, data sovereignty and operational complexity are becoming strategic technology considerations.
CubeAPM highlights a broader enterprise software shift towards self-hosted, predictable-cost infrastructure combined with AI-assisted operations. Its focus on interoperability, data residency and cost efficiency positions observability as an important layer in the emerging enterprise AI stack.
