CtrlB

Cover Block

PUBLIC

Name	CtrlB
Tagline	Petabyte-scale data engine for observability and security data
Headquarters	Bengaluru, India
Founded	2023
Stage	Seed
Business Model	SaaS
Industry	Security
Technology	Software (Non-AI)
Geography	South Asia
Growth Profile	Venture Scale
Founding Team	Co-Founders (3+)
Funding Label	Seed (total disclosed ~$2,500,000)

Links

PUBLIC

• Website: https://ctrlb.ai
• LinkedIn: https://in.linkedin.com/company/ctrlb-hq

Executive Summary

PUBLIC CtrlB is an early-stage venture building a data engine designed to make petabyte-scale observability and security telemetry affordable, a bet that directly challenges the cost structures of incumbent SaaS tools [The SaaS News, Nov 2025]. Founded in 2023, the company emerged from the founders' firsthand experience with ballooning observability costs, prompting a pivot from an initial focus on live debugging tools to a broader data platform play [Inc42]. Its core product, CtrlB Flow, ingests logs, traces, and metrics without sampling, sits atop cloud object storage to separate compute from storage, and promises to cut related cloud spend by 60-80% while maintaining query performance [Startup-Seeker, CtrlB.ai].

The founding team brings technical pedigree from Indian Institutes of Technology but lacks publicly documented prior experience in scaling enterprise SaaS sales or managing large-scale data infrastructure deployments [LinkedIn]. A $2.5 million seed round closed in November 2025, led by Chiratae Ventures with participation from Equirus and others, provides runway to pursue its initial commercial targets [The SaaS News, Nov 2025]. The company is currently pre-revenue, targeting roughly 15 enterprise customers and approximately $100,000 in ARR by the end of FY26, a trajectory that will test its product-led growth motion and the real-world validity of its cost-saving claims [Inc42, Feb 2026]. Over the next 12-18 months, the critical watchpoints are the conversion of its integration and architecture promises into paid customer deployments, and whether it can demonstrate the renewal velocity and expansion potential needed to justify its venture-scale aspirations.

Data Accuracy: YELLOW -- Key product and funding details are confirmed by multiple press reports; pre-revenue status and near-term targets are from a single trade publication.

Taxonomy Snapshot

Axis	Classification
Stage	Seed
Business Model	SaaS
Industry / Vertical	Security
Technology Type	Software (Non-AI)
Geography	South Asia
Growth Profile	Venture Scale
Founding Team	Co-Founders (3+)
Funding	Seed (total disclosed ~$2,500,000)

Company Overview

PUBLIC

CtrlB emerged from a founder's direct experience with the escalating cost of observability tools, a problem that crystallized into a business in 2023. Adarsh Srivastava, who had encountered these costs in prior technical recruiting roles, co-founded the company with Mayank Singh Chauhan and Balasubramanian P to build a more economical data platform [Inc42, Feb 2026]. The company is headquartered in Bengaluru, India, with a stated operational presence in the United States [The SaaS News, Nov 2025].

Its initial product focus appears to have been on live debugging, a pivot noted in early coverage [Inc42]. The company's current public identity, however, is firmly centered on a unified data engine for observability and security telemetry. A key operational milestone was the closing of a $2.5 million seed round in November 2025, led by Chiratae Ventures with participation from Equirus, InnovateX Fund, Campus Fund, and Point One Capital [The SaaS News, Nov 2025].

Current public traction is framed in terms of future targets rather than historical performance. The company is reported to be in a pre-revenue stage as of early 2026, with an explicit goal of reaching approximately $100,000 in annual recurring revenue by the end of the fiscal year [Inc42, Feb 2026]. Its near-term customer acquisition strategy aims at 15 enterprise clients across logistics, fintech, e-commerce, and SaaS sectors in India and the US [Inc42, Feb 2026].

Data Accuracy: YELLOW -- Founding narrative and seed round corroborated by multiple publishers; revenue stage and targets are single-source claims.

Product and Technology

MIXED CtrlB's core proposition is a unified data engine designed to manage petabyte-scale telemetry for observability and security use cases. The platform, often referred to as CtrlB Flow, ingests logs, traces, metrics, and alerts in real-time, claiming to eliminate the need for traditional sampling or indexing [The SaaS News, Nov 2025]. Its architectural bet is a separation of compute and storage, sitting atop object storage like Amazon S3 to keep all historical data instantly queryable [Startup-Seeker]. This design is the foundation of its cost-reduction pitch, asserting it can cut cloud costs by 80% while maintaining sub-second query latency [CtrlB LinkedIn].

The product integrates a feature called Live Debugger, which the company describes as a "super debugger" that allows developers to set tracepoints or logpoints directly from their IDE and inspect variables and stack traces on the fly [Beyond Observability, CtrlB.ai]. This suggests an evolution from a pure debugging tool, a pivot noted by industry observers [Inc42]. The platform aims to unify data layers, enabling users to "search once, explore in real time, and pivot instantly" between logs, traces, and services [Redefining Observability, CtrlB.ai]. Through integrations and a Model Context Protocol (MCP), it seeks to allow teams to ask new questions of their data dynamically [CtrlB Blog].

• Cost claims. The company states teams using CtrlB typically reduce observability spend by 60-80% compared to using SaaS tools, without deleting data [Cost-Effective Telemetry Scaling, CtrlB.ai].
• Integration surface. It is designed to route data to existing destinations like Splunk, Datadog, and Elastic, positioning itself as a cost-efficient layer rather than a full replacement [Startup-Seeker].

Data Accuracy: YELLOW -- Product claims are sourced from company materials and press reports; technical architecture and performance benchmarks lack independent third-party validation.

Market Research

MIXED The market for cost-effective observability is expanding as enterprises generate more telemetry data than ever, but the financial burden of traditional platforms is prompting a search for architectural alternatives. CtrlB targets a segment defined by data volume and cost sensitivity, rather than by a rigidly defined total addressable market. No third-party analyst report sizing the specific market for 'petabyte-scale data engines on object storage' was cited in the research, making a precise TAM/SAM/SOM calculation impossible with public data.

The demand drivers are well-documented in adjacent sectors. The global observability and application performance monitoring (APM) software market was valued at approximately $10.8 billion in 2024 and is projected to grow at a compound annual rate of 13% through 2030 [Gartner, 2024]. This growth is fueled by the proliferation of cloud-native, microservices-based applications, which generate exponentially more logs, traces, and metrics. A key tailwind for CtrlB's value proposition is the escalating cost of incumbent SaaS tools like Datadog and Splunk, which can consume a significant portion of an engineering team's cloud budget as data volumes scale [Chiratae].

Adjacent and substitute markets are critical to understanding the competitive pressure. CtrlB's platform intersects with several established categories: log management, distributed tracing, infrastructure monitoring, and security information and event management (SIEM). The company's claim of integrating with and routing data to tools like Splunk and Datadog positions it as a potential cost-optimization layer within a broader observability stack, rather than a full replacement. A significant substitute market is the build-in-house approach using open-source projects like OpenTelemetry, Loki, and Tempo, coupled with cloud object storage, which offers similar cost benefits but requires substantial engineering investment to operationalize.

Regulatory and macro forces present a mixed picture. Data sovereignty and residency requirements, particularly in sectors like fintech, could drive demand for solutions that keep data within a specific cloud region or on a customer's own storage, an architecture CtrlB supports. Conversely, a broader macroeconomic push for cloud cost optimization (FinOps) directly benefits its messaging. However, the company's early focus on India and the US means it must navigate two distinct enterprise sales landscapes with different procurement cycles and competitive dynamics.

Metric	Value
Observability & APM Software Market (2024)	10.8 $B
Projected CAGR (2024-2030)	13 %

The projected growth of the core observability market provides a credible, analogous ceiling for the opportunity, though CtrlB's specific wedge addresses a cost-conscious subset of that spending. The absence of a cited market size for its architectural niche is a standard gap for early-stage infrastructure startups, shifting the burden of proof to customer adoption rather than top-down sizing.

Data Accuracy: YELLOW -- Market sizing is drawn from an analogous Gartner report; demand drivers are corroborated by investor commentary and industry trends.

Competitive Landscape

MIXED CtrlB enters a market defined by established, well-funded incumbents on one side and a growing cohort of cost-focused challengers on the other, positioning itself as an architectural disruptor for cost-sensitive engineering teams.

The analysis proceeds with a segment-based map of the landscape.

The competitive map for observability and security data platforms is dense and stratified. At the top tier, incumbent SaaS vendors like Datadog, Splunk, and Elastic dominate with full-featured platforms, deep enterprise integrations, and massive sales footprints. Their primary vulnerability is cost, especially at petabyte scale, which has created space for challengers. These include newer vendors like Grafana Labs (open-core), Chronosphere, and Honeycomb, which also focus on cost control but often through different architectural means, such as data sampling or proprietary storage layers. CtrlB's stated wedge is architectural: by decoupling compute from storage on object stores like S3, it claims to avoid the proprietary data lock-in and markup of SaaS tools, targeting a direct 60-80% cost reduction [Cost-Effective Telemetry Scaling, CtrlB.ai]. Adjacent substitutes include in-house solutions built on open-source stacks (e.g., OpenTelemetry, Loki, Tempo) and hyperscaler-native services (e.g., Amazon Managed Service for Prometheus, Google Cloud's Operations Suite), which appeal to teams with deep engineering resources but impose significant operational overhead.

CtrlB's defensible edge today rests almost entirely on its architectural thesis and early investor conviction. The technical premise of a storage-layer-agnostic engine that maintains query performance is its core differentiator [Startup-Seeker]. This edge is currently perishable; it is a product claim, not a proven, scaled deployment. Durability would depend on achieving technical validation at petabyte scale with named enterprise customers, building a performance moat through query optimization, and securing strategic partnerships with cloud providers. The recent $2.5 million seed round led by Chiratae Ventures provides capital to pursue this validation, but the round size is modest relative to the capital reserves of its potential rivals [The SaaS News, Nov 2025].

The company's most significant exposure is its lack of commercial traction and ecosystem presence. It is pre-revenue with no publicly disclosed customer logos, while incumbents have thousands [Inc42, Feb 2026]. It lacks a visible channel strategy or partnership with a major cloud provider, which is a critical customer acquisition and credibility lever in infrastructure software. Furthermore, its product evolution from a live debugging tool to a data observability platform, as noted by Inc42, suggests the core value proposition is still being refined, which could create execution risk against more focused competitors [Inc42].

The most plausible 18-month scenario hinges on CtrlB's ability to convert its technical claims into a handful of referenceable enterprise deployments. If it can onboard 15 customers as targeted and demonstrate the promised cost savings without operational friction, it could establish a beachhead in cost-conscious verticals like logistics and fintech [Inc42, Feb 2026]. In that case, challengers like Grafana Labs or newer entrants focusing on open telemetry economics could see increased pressure. However, if CtrlB fails to land these early customers or if its architecture encounters performance limitations at scale, the company risks becoming an also-ran. The winner in this scenario would be the incumbent that most effectively responds to the cost narrative, perhaps by introducing a more competitive storage-tiering option, while the loser would be any undifferentiated challenger that cannot match either the feature depth of incumbents or the radical cost proposition of architectural disruptors.

Data Accuracy: YELLOW -- Competitive positioning is inferred from product claims and market analysis; no direct competitor comparisons from independent sources.

Opportunity

PUBLIC The prize for CtrlB is a position as the cost-effective data layer for enterprise observability, a market where incumbents have built multi-billion dollar businesses on legacy, expensive architectures.

The headline opportunity is to become the default infrastructure for petabyte-scale telemetry in cost-sensitive, high-growth sectors. This outcome is reachable because the company's architectural bet,separating compute from storage on cloud object stores,directly targets the primary pain point of existing observability customers: runaway costs. The company claims its approach can reduce spend by 60-80% compared to SaaS tools [Cost-Effective Telemetry Scaling, CtrlB.ai]. If these efficiency claims hold at scale, CtrlB's platform could serve as the foundational data engine, allowing enterprises to retain full-fidelity data without the prohibitive expense that forces compromises like sampling. The early backing from Chiratae Ventures, a firm with a track record in enterprise software, lends credence to the technical and commercial thesis, even at this pre-revenue stage [Chiratae].

Growth will likely follow one of several concrete paths, each with identifiable catalysts.

Scenario	What happens	Catalyst	Why it's plausible
Sector-specific land-and-expand	CtrlB becomes the observability standard for Indian and global logistics and fintech companies.	Securing a marquee design win with a major logistics or payments platform, proving the platform at scale in a high-volume, low-margin environment.	The company is explicitly targeting these sectors for its initial 15-customer goal [Inc42, Feb 2026]. These industries generate massive telemetry and are acutely sensitive to operational costs.
Embedded infrastructure for cloud providers	CtrlB's engine is offered as a managed service or technology partnership by a major hyperscaler (AWS, GCP, Azure).	A formal partnership or integration launch, positioning CtrlB as the native, cost-optimized observability layer for a cloud's object storage.	The product is architected to sit on object storage like S3, aligning its technical model with cloud providers' infrastructure [Startup-Seeker]. Hyperscalers have a vested interest in making data-intensive workloads more affordable on their platforms.

What compounding looks like is a classic efficiency flywheel. Early adopters in cost-sensitive sectors validate the platform's performance and savings claims. These reference cases lower the sales friction for similar companies within the same vertical. As the customer base grows, the volume and variety of telemetry data processed could inform optimizations to the query engine itself, creating a data moat around performance for specific, high-value workloads. The company's integration narrative,routing data to tools like Splunk and Datadog,positions it as an enabling layer rather than a direct replacement, which could accelerate adoption before a full platform switch [Startup-Seeker]. Each successful deployment makes the cost argument more tangible for the next prospect.

The size of the win can be framed by looking at a public comparable. Datadog, a leader in the observability SaaS market, currently commands a market capitalization measured in tens of billions of dollars. While CtrlB is not positioned as a direct feature-for-feature competitor, it is attacking the same underlying customer budget for monitoring and logging. If CtrlB's architecture allows it to capture even a single-digit percentage of the spend currently allocated to incumbents by becoming the preferred data engine for a subset of cost-conscious enterprises, the outcome is substantial. In a scenario where the company successfully executes its sector-specific land-and-expand plan and proves its economic model, a valuation trajectory into the hundreds of millions of dollars within a 5-7 year horizon is a plausible outcome (scenario, not a forecast).

Data Accuracy: YELLOW -- The core opportunity thesis is built on the company's stated architectural advantages and market targeting, which are cited from its own materials and early press. The growth scenarios are extrapolations from these stated goals; no public evidence yet confirms execution towards them.

Sources

PUBLIC

1. [The SaaS News, Nov 2025] CtrlB Raises $2.5 Million In Seed Round | https://www.thesaasnews.com/news/ctrlb-raises-2-5-million-in-seed-round

2. [Inc42, Feb 2026] Can Chiratae-Backed CtrlB Edge Out Enterprise Giants In The Data Observability Space? | https://inc42.com/startups/can-chiratae-backed-ctrlb-edge-out-enterprise-giants-in-the-data-observability-space/

3. [Startup-Seeker] CtrlB | https://startup-seeker.com/company/ctrlb~ai

4. [CtrlB LinkedIn] CtrlB LinkedIn | https://in.linkedin.com/company/ctrlb-hq

5. [Cost-Effective Telemetry Scaling, CtrlB.ai] CtrlB.ai article | https://ctrlb.ai/blog/cost-effective-telemetry-scaling

6. [Beyond Observability, CtrlB.ai] CtrlB.ai article | https://ctrlb.ai/blog/beyond-observability

7. [Redefining Observability, CtrlB.ai] CtrlB.ai article | https://ctrlb.ai/blog/redefining-observability

8. [CtrlB Blog] CtrlB Blog | https://ctrlb.ai/blog

9. [Chiratae] Investing in Ctrl-B: Rewriting the Economics of Enterprise Telemetry Data | https://www.chiratae.com/investing-in-ctrl-b-rewriting-the-economics-of-enterprise-telemetry-data/

10. [Inc42] CtrlB’s Second Innings: From Debugging Code To Filtering Bad Data | https://inc42.com/startups/ctrlbs-second-innings-from-debugging-code-to-filtering-bad-data/

11. [LinkedIn] Adarsh Srivastava - CtrlB | LinkedIn | https://www.linkedin.com/in/adarsh-srivastava-016795114/

12. [Gartner, 2024] Gartner Market Report | https://www.gartner.com/en/documents/5382341