Investing in Ctrl B: Rewriting the Economics of Enterprise Telemetry Data
How CtrlB is building India’s next-generation data infrastructure to democratize observability at scale.
by Team Chiratae
Nov 20, 2025
The modern enterprise generates data like never before. A typical mid-sized company now creates over 1 terabyte of telemetry data daily, equivalent to 500 hours of HD video – and this volume grows 25-50% every quarter. Yet here’s the painful irony: the very systems designed to make sense of this data deluge have become prohibitively expensive, forcing companies into an impossible choice between comprehensive visibility and fiscal responsibility.
Telemetry data within them presents a fundamentally different challenges than the structured business data that platforms like Snowflake were designed to handle. Unlike predictable sales records, telemetry arrives as an unpredictable torrent, millions of log entries per minute with constantly evolving schemas and unstructured fields. The economics are inverted too: traditional platforms optimize for read-heavy workloads, but telemetry is write-heavy with occasional urgent searches during system failures.
When enterprises spend millions annually on observability infrastructure, or when companies are forced to sample away 70% of their logs to control costs, we know something is fundamentally broken. This isn’t just a pricing problem it’s an architectural one that demands a complete rethinking of how observability platforms should work in the cloud era.
The Weekend Problem
Consider a scenario that plays out across enterprises every week: a company stores 2TB of data daily using traditional solutions like Elasticsearch. Keeping this data for 30 days costs $10,500 monthly. To manage costs, most companies reduce retention to just 3-5 days, creating what we call “The Weekend Problem.”If something goes wrong on Friday, by Monday the crucial debugging data has vanished. It’s like having security camera footage that automatically erases itself after three days if you don’t notice a problem quickly enough, you’ve lost the ability to investigate it. When system failures cost large companies $9,000 per minute of downtime, this data scarcity becomes an existential business risk.
This scenario resonated deeply with us as investors we’ve seen too many portfolio companies struggle with this exact trade-off between comprehensive monitoring and budget constraints.
When we first met Adarsh Srivastava and Balasubramanian P, we discovered they had lived through exactly this problem. Adarsh has been obsessing over observability challenges for over two years, having experienced the pain firsthand while building scalable data platforms. Balasubramanian brings ML and big data platform expertise from his work at companies like Craze. Together, they’ve spent over five years in observability, giving them the domain expertise to literally reinvent data lakes with patent-pending storage formats. Their breakthrough came when they realized that the entire industry was building on fundamentally flawed architectural assumptions from 1985.
The Architectural Revolution
Ctrl B’s innovation lies in what they call “shared everything” architecture complete separation of compute from storage, and even compute from compute. Instead of expensive disk storage, they leverage cheap, reliable object storage (S3, Azure Blob). Rather than maintaining permanent compute resources, they spin up serverless functions only when queries are executed.
The technical elegance is in the details: their patent-pending indexing strategy recognizes that hundreds of thousands of logs often boil down to just a few hundred unique patterns. By indexing these patterns rather than complete logs, they achieve 40-60% smaller index sizes while maintaining superior query performance.
The results speak for themselves: up to 10x faster search while costing 90% less than traditional solutions. In benchmark tests, Ctrl B processed 125TB of security logs in 1 minute versus 15-60 minutes for competitors.
The Perfect Storm
Four powerful forces are converging to create an unprecedented window for Ctrl B’s approach:- The AI Data Explosion: The rise of AI and machine learning has fundamentally changed enterprise data requirements. AI workloads generate entirely new categories of telemetry data from model inference logs to vector embeddings to real-time decision traces. Companies training large language models generate petabytes of training logs. Traditional observability tools simply cannot handle this volume economically. We’re seeing AI-first companies spending tens of millions annually just on logging their model performance, creating urgent demand for Ctrl B’s cost-efficient architecture.
- Serverless and Edge Computing Maturity: The serverless revolution has reached critical mass, enabling Ctrl B’s fundamental architecture. What seemed impossible five years ago spinning up hundreds of compute functions on-demand is now the foundation for petabyte-scale solutions. Edge computing adds another complexity layer, with IoT devices and edge AI creating distributed logging challenges that traditional centralized systems can’t handle efficiently.
- Cloud Infrastructure Economics: Object storage costs have plummeted while reliability has soared to 99.999999999% durability. Meanwhile, compute costs have become truly elastic through services like AWS Lambda and Azure Functions. This shift enables Ctrl B’s fundamental insight: separating compute from storage isn’t just technically elegant it’s economically transformative.
- Security and Compliance Convergence: The boundaries between observability and security are blurring. Modern threats require correlating application logs, infrastructure metrics, and security events in real-time. Companies need to retain security logs for 2-7 years for compliance, but traditional SIEM solutions cost $250,000+ monthly for enterprise deployments. Ctrl B’s unified approach makes comprehensive security observability accessible to companies previously priced out.
The timing isn’t coincidental: just as AI workloads demand 10x more observability data, cloud infrastructure has evolved to make Ctrl B’s architecture possible, and regulatory requirements are forcing longer data retention.
The Jevons Paradox
We expect Ctrl B to trigger a Jevons paradox in observability: by making data storage dramatically cheaper, they’ll unlock demand from companies who previously couldn’t afford comprehensive visibility. This isn’t just market share capture it’s market expansion across both observability ($12B) and SIEM markets ($7.13B).
While Ctrl B starts with observability, their architecture creates possibilities far beyond their initial market. Any application requiring fast queries over massive datasets—from security analytics to IoT telemetry to business intelligence becomes addressable. We’re investing in a foundational data platform with the potential to democratize enterprise-grade data capabilities.
Our Conviction
We’re leading Ctrl B’s $2M seed round because we believe they’re solving one of enterprise technology’s most pressing challenges with genuinely differentiated technology. The combination of 90% cost reduction and 10x performance improvement isn’t incremental it’s transformational.
In an era where every company is becoming a data company, Ctrl B is building the infrastructure to make that transformation economically viable. We’re excited to partner with Adarsh and Balasubramanian as they democratize enterprise-grade observability and rewrite the economics of data at scale.