Kloudfuse In Focus: Changing The Economics Of Observability

In the ever-evolving world of tech, each technological advancement has redefined the balance between cost and capability. From virtual machines and containers to cloud computing and open-source software, these innovations have not only enhanced efficiency but also transformed the economics of infrastructure and operations. Observability is now at a similar crossroads.

As data volumes soar and traditional monitoring tools struggle to keep pace—both technically and economically—there's a pressing need for innovation. This is where Kloudfuse enters the scene. Recently, I had the opportunity to discuss this with Pankaj Thakkar, Co-Founder and CEO of Kloudfuse. Their mission is clear: to revolutionize observability through technology innovation and a business model that aligns it directly with business outcomes, making it both more effective and economically sustainable.

Curious how Kloudfuse is redefining the economics of observability? Keep reading to find out.

Value Driven Observability   

In my recent article, Value-Driven Observability: Aligning Data with Business Impact, I explored how observability must evolve from reactive data collection to an intent-driven strategy closely aligned with business outcomes. Achieving lasting ROI and operational efficiency requires a focus on critical business flows, measurable goals, and smart data management, foundational principles for modern observability.

With that context, let’s revisit the five essential pillars that I believe are critical to achieve this and I will then explore how Kloudfuse operationalizes each: 

Prioritizing Business Impact: Effective observability starts with a focus on what truly drives business outcomes. If an application or infrastructure signal doesn’t impact user experience or business performance, should it really be a priority? Instead, the focus should be on tracking critical user journeys, customer interactions, and operational efficiencies to quickly detect meaningful service degradation or failures. By assigning Service Level Objectives (SLOs) and tracking error budgets for these flows, teams can ensure engineering resources are directed where they have the greatest impact. Also part of the value is in using popular query languages or dashboards for data consumption, so time to value is accelerated.

Unified and Intelligent Observability: Achieving unified and intelligent observability starts with standardized, open instrumentation. Technologies like OpenTelemetry and eBPF are foundational to this approach, providing the high-fidelity telemetry and deep system visibility needed to map dependencies and monitor critical business flows. By capturing rich, low-overhead signals across infrastructure and applications, these frameworks enable precise tracing of how user actions and system events propagate through complex environments, laying the groundwork for true observability. Intelligent observability also uses machine learning techniques—like anomaly detection, time series forecasting, and log analysis—to automatically detect issues, predict failures, accelerate root cause analysis, and reduce alert fatigue for faster, smarter operations.

Telemetry Pipelines: Modern observability demands more than just data collection, it requires intelligent telemetry pipelines that can efficiently ingest, process, enrich, and route diverse signals including logs, metrics, traces, digital experience events, and business context. These pipelines are essential for delivering the right data to the right tools in real time, enabling context-aware monitoring and rapid, informed decision-making. At the same time, intelligent filtering, aggregation, and transformation help reduce data noise and control costs by retaining only what’s meaningful, ensuring both performance and financial sustainability.

Data Lakes: As observability data proliferates, centralized telemetry data lakes are becoming the critical nexus for modern operations. For organizations increasingly adopting real-time applications built on microservices and cloud-native architectures, retaining high-fidelity, high-frequency telemetry is absolutely critical. These data lakes are engineered to handle the immense scale, volumes, and high cardinality of data generated by today's complex microservices, ensuring robust monitoring capabilities. A key benefit is the consolidation of various telemetry streams—like metrics, logs, and traces—into a single, unified repository. This eliminates the toil of manually correlating disparate data points, dramatically speeding up troubleshooting. Engineers can now see a complete picture, from application behavior to infrastructure performance, without switching tools or piecing together information.

AI-SRE Tooling: AI-driven Site Reliability Engineering (SRE) tools are rapidly evolving from overhyped automation promises to practical, engineer-assistive solutions that act as co-pilots—helping teams quickly surface anomalies, predict issues, and pinpoint root causes with greater speed and accuracy. Only when combined with high-frequency, high-cardinality telemetry data, typically stored in an observability data lake, these tools provide the precision needed to focus forensic investigations efficiently, delivering detailed, time-sequenced data essential for validating findings down to the code and infrastructure level. This temporal context is crucial for understanding how incidents unfold over time and for uncovering deeper insights into recurring patterns and trends. 

Although still maturing, the trajectory is clear: AI should enhance human decision-making rather than obscure it. Effectively integrated, AI-SRE tooling empowers teams to shift from reactive firefighting to proactive, systemic improvements that drive smarter, more resilient operations.

Bonus: The Power of a Single, Owned Solution While each of these pillars is vital, their true power is unlocked when combined within a single, unified platform that you can own. Such a solution enables organizations to operationalize all these principles seamlessly, delivering end-to-end observability aligned with business impact.

In this model, the customer deploys observability in a Virtual Private Cloud (VPC) or on-premises for greater control over their data security and operational costs. Coupling this with licensing models that avoid punitive fees or usage penalties, this approach empowers teams to scale observability efficiently while optimizing spending.

Crucially, this architecture also ties data ownership directly to business value. For example this can empower advanced agentic workflows directly on your data, avoiding the need to pull it out of proprietary systems. This approach offers more control, reduced vendor lock-in, and greater agility in leveraging your observability insights.

Prioritizing Business Impact with Kloudfuse

Kloudfuse empowers teams to operationalize the prioritization of critical business flows by unifying telemetry across the entire technology stack into a centralized, intelligent data lake. This approach ensures that digital experience and user journeys, and other application performance KPIs, and the underlying infrastructure metrics are closely monitored, allowing teams to detect and respond to meaningful service degradation quickly.

At the core of Kloudfuse's platform is a highly flexible Service Level Objective (SLO) framework, enabling teams to define SLOs against any combination of logs, traces, events, digital experience data, or other business KPIs. This precision ensures that engineering resources are focused on the most impactful flows, directly supporting business outcomes.

With real-time error budget monitoring and burn rate analysis, Kloudfuse provides instant visibility into service health, helping teams proactively manage reliability risks. In the event of an SLO breach, engineers can drill down into the underlying telemetry without switching tools or losing context, significantly reducing mean time to resolution (MTTR).

Kloudfuse also supports historical analysis to benchmark performance, identify recurring patterns, and refine reliability targets over time, even in complex, high-cardinality environments. Presented through an intuitive interface, Kloudfuse delivers a unified view of logs, metrics, traces, and digital experience insights, breaking down silos between SREs, DevOps, developers, and customer-facing teams.

Additionally, users can query Kloudfuse using multiple open and popular query languages, such as PromQL, TraceQL, LogQL, SQL, and GraphQL, ensuring seamless data access, which is a huge propellant to value. Kloudfuse also embeds Garfana dashboards in addition to having its own, to enable devops and SREs teams to use the platform with interfaces they are already familiar with.

This comprehensive, data-driven approach allows organizations to focus their engineering efforts where they will have the greatest business impact, driving faster and more meaningful incident resolution.

Unified and Intelligent Observability With Kloudfuse

Delivering actionable, business-aligned observability demands the consistent collection, correlation, and analysis of data across diverse systems and layers. Kloudfuse addresses this challenge with a unified data collection framework that embraces openness and flexibility, allowing organizations to leverage OpenTelemetry (OTel) where desired while also integrating seamlessly with a wide array of other applications, agents, and telemetry sources.

The platform integrates traces, metrics, and logs and infrastructure-level signals, Kubernetes events, and application telemetry to provide a unified view of system health. In addition, The platform also supports real user monitoring (RUM), LLM-specific telemetry, continuous profiling, and custom application data. This broad compatibility means organizations can combine existing tools and standards without needing to rip and replace, enabling them to incrementally build a high-fidelity, future-proof observability layer.

This flexibility to ingest signals from any data source across infrastructure, services, and front end applications allows Kloudfuse to create a unified data lake rich in context and insight. Organizations can then discover, monitor, and analyze end to end frontend and backend observability, tracking health, performance, and impact in both real time and historically.

With this foundation, teams can define high-cardinality, custom Service Level Objectives (SLOs) transaction success rates, application availability, or service latency, aligning technical observability to break down silos between SREs, developers, and business stakeholders, and empower faster, more informed decisions grounded in business value.

Kloudfuse Telemetry Pipelines

Kloudfuse's approach to telemetry pipelines is designed to provide organizations with comprehensive control over their observability data, and with zero disruption to their existing instrumentation. Kloudfuse addresses these needs by offering robust control functions within its open, poly-agent approach (multiple telemetry agents and data collectors) to ingesting and telemetry pipelines, designed to optimize data handling and maximize observability value. This includes:

Any Agent—Open Source or Vendor-Specific: Kloudfuse is built for flexibility, seamlessly integrating with any telemetry agent—whether open-source or vendor-specific. This means you can continue using tools like Datadog, New Relic, Elastic, Fluent Bit, Fluentd, Prometheus, and OpenTelemetry, as well as advanced methods like eBPF for kernel-level insights. 

This poly-agent approach ensures you can maintain your existing telemetry stack without disruption, making adoption incredibly smooth. Kloudfuse also simplifies migration by automatically converting your existing alerts and dashboards, so your teams retain full operational continuity. This translates to fast onboarding, a minimal learning curve, and immediate value for your SRE and engineering teams.

Ingestion Rate Control: To prevent system overload and prioritize critical data, Kloudfuse allows precise rate limiting of incoming telemetry across all data types—metrics, logs, traces, events, and Real User Monitoring (RUM) data. This helps maintain system stability and prioritize part of each data stream over others during data spikes.

Retention Policies: Users can define customizable retention rules based on data type, priority, and compliance requirements. High-value data can be retained longer, while less critical telemetry is discarded sooner, optimizing storage use and ensuring adherence to governance standards. 

Flexible Log Routing: Kloudfuse supports dynamic routing of logs to different destinations depending on business needs—such as sending error logs to specialized analysis tools or routing compliance-sensitive logs to secure storage—streamlining workflows and enhancing security.

Data Transformation and Enrichment: Advanced pipeline functions enable real-time transformation, aggregation, remapping, relabeling, parsing, and enrichment of telemetry data with external sources. This includes adding contextual metadata, converting formats, and extracting meaningful attributes to provide richer insights and facilitate faster root cause analysis.

Control Plane Designed for Easy Manageability: The platform’s Control Plane monitors the health and performance of Kloudfuse’s cluster deployed in customer’s VPC. This includes cluster installation details, drill down into node and service-level metrics, providing insights into performance of the cluster, as well as memory usage, space consumed, and ingest and throughput rates for metrics, logs, traces, real user monitoring data and other data pipelines. This allows users to easily monitor and manage their own deployment with comprehensive visibility at every level.

Scalability in High Cardinality Workloads: Kloudfuse’s telemetry pipelines are also architected for horizontal scalability and robust system integrity, ensuring reliable observability even in the most demanding environments. Designed to handle the high volume and high-cardinality data generated by modern distributed systems, these pipelines include built-in fault tolerance and advanced data recovery mechanisms. This design safeguards against data loss and service disruptions, ensuring that critical telemetry remains accurate and available under adverse conditions, empowering teams to maintain continuous visibility and proactively manage incidents.

The Kloudfuse Data Lake: Purpose-Built for Unified Observability

Kloudfuse has engineered its observability data lake around Apache Pinot, a real-time distributed OLAP datastore celebrated for its low-latency, high-throughput analytics. This foundational choice allows Kloudfuse to efficiently handle the vast volumes and high cardinality of telemetry data generated by modern applications.

However, Kloudfuse's data lake goes far beyond standard deployments. It's specifically architected for the unique demands of observability, incorporating advanced enhancements such as schema-less ingestion, intelligent log fingerprinting, and native integration of diverse telemetry data types—including metrics, logs, and traces. Furthermore, the system embeds machine learning capabilities to support real-time monitoring, enabling rapid anomaly detection and accelerated incident resolution.

Unlike traditional observability platforms that often rely on a patchwork of backend data stores—each designed for a single telemetry type and later expanded—Kloudfuse takes a fundamentally different approach. It  was designed from the ground up as a unified telemetry platform, purpose-built to store, analyze, and correlate all observability data within a single, high-performance data lake. This foundational design eliminates data silos, simplifies architecture, and dramatically improves cross-signal correlation, which is critical for understanding complex, distributed systems.

Why a Kloudfuse Observability Data Lake?

Kloudfuse's high-performance observability data lake, enhanced with proprietary innovations, delivers several key advantages for real-time, large-scale telemetry use cases:

• Low-Latency, High-Throughput Analytics: You'll experience sub-second query responses even on high-cardinality, high-volume datasets. This real-time insight is essential for immediate operational decision-making.

• True Real-Time Ingestion: Unlike traditional data lakes that might rely on batch processing, Kloudfuse supports genuine real-time streaming ingestion from sources like Kafka, Pulsar, and its own unified telemetry pipelines, ensuring up-to-the-second visibility.

• Optimized for High Cardinality: Through advanced indexing and columnar storage innovations, Kloudfuse efficiently handles the millions of unique time series and event types typical in modern observability workloads.

• Horizontally Scalable Architecture: The Kloudfuse platform scales effortlessly to petabyte-sized datasets across distributed cloud environments, making it ideal for the most demanding, cloud-native observability needs.

• Unified Schema for Deep Correlation: By normalizing logs, metrics, traces, and other telemetry into a single, cohesive schema, Kloudfuse enables high-speed, cross-domain analysis—from infrastructure to applications to user interactions—all in one place.

• Cost-Efficient Storage and Compute: Kloudfuse is engineered to optimize both storage and compute resources, enabling high-frequency querying and long-term data retention without ballooning infrastructure costs.

Conclusion: The Analytical Backbone for Unified Observability

While Kloudfuse leverages Apache Pinot as its foundation, this strategic choice and subsequent significant internal IP development have tailored it specifically for the demanding scale and unique requirements of modern telemetry workloads. This means Kloudfuse isn't just a data lake—it's the analytical backbone for value-driven observability. It unifies logs, traces, metrics, digital experience, and business events, enabling teams to proactively detect issues, measure business impact, and optimize user experiences. This approach significantly enhances operational efficiency and directly aligns technical performance with business outcomes, driving informed decision-making.

Kloudfuse and AI-SRE Tooling

Kloudfuse redefines observability by integrating AI-driven insights into its high-cardinality telemetry engine and unified data lake. This approach empowers engineering teams to move beyond traditional monitoring, enabling proactive identification of anomalies, prediction of issues, and rapid pinpointing of root causes. Noteworthy capabilities include: 

AI for High-Cardinality, Business-Aligned Analysis: Modern digital systems generate vast amounts of telemetry data across users, regions, transactions, and microservices. Kloudfuse is purpose-built to ingest and analyze this high-cardinality data without performance trade-offs. This capability allows AI models to detect anomalies and patterns across dimensions like “checkout success by user cohort” or “API latency by region,” metrics that directly impact customer experience and revenue.

K-Lens: Visual Intelligence at the Point of Decision: K-Lens, Kloudfuse’s visualization and exploration layer for distributed tracing, transforms raw data into actionable insights. Engineers can drill down from an SLO breach to a root cause, tracing the flow across traces in a single interface, guided by AI-prioritized signals. KLens surfaces relevant anomalies, correlates them with business flows, and provides context-aware explanations, enabling swift action without sifting through noise.

Smarter SLO Management with Real-Time Burn Tracking: Kloudfuse employs AI to monitor error budgets in real time and predict burn rates before they breach. This proactive approach allows teams to prioritize remediation based on actual business impact—not just infrastructure signals—ensuring engineering efforts align with customer-facing priorities.

The Kloudfuse Advantage: AI-SRE tooling in Kloudfuse is not an add-on; it's integrated into the platform's foundation of unified telemetry, high-cardinality analytics, and real-time observability. This integration results in faster root cause resolution, smarter alerting, and more resilient digital experiences—without engineering guesswork.

Enabling Agentic AI SRE Tooling: Beyond its native AI features, Kloudfuse supports seamless integration with third-party AI and machine learning platforms, including agentic SRE AI products. This interoperability enables organizations to extend observability insights into broader domains such as security monitoring, automated incident response, compliance auditing, and risk management. 

In these scenarios, Kloudfuse observability data lake becomes the knowledge graph that feeds high-fidelity telemetry and business context into external AI systems, helping create a cohesive, intelligent operational ecosystem where data-driven insights enhance cross-functional workflows and decision-making at scale. Since Kloudfuse is deployed directly within customer-managed environments, all observability data remains under the customer’s exclusive control. 

This architecture eliminates vendor lock-in, which can otherwise lead to partial or complete data inaccessibility or impose significant costs when integrating with agentic platforms. It also enables low-latency access to data and ensures compliance with stringent data sovereignty, security, and privacy requirements by avoiding external data transfers.

By embedding AI directly into its observability stack and enabling ecosystem integrations, Kloudfuse not only improves incident response times but also provides a foundation for self-healing, highly resilient systems that align technical performance with business objectives. This intelligent automation is key to building a data-rich operational backbone that supports both internal teams and external partners.

Kloudfuse Bonus: The Economics of Observability with Virtual Private/On Premises Deployments 

Kloudfuse incorporates all five essential principles of value-driven observability within a single, unified platform—eliminating the need to integrate multiple disparate tools. This consolidation not only simplifies operations but also significantly reduces the total cost of ownership by lowering integration complexity, maintenance overhead, and tooling sprawl.

With flexible deployment options—whether in your own Virtual Private Cloud (VPC) or on-premises—Kloudfuse ensures you retain full control over data security and infrastructure costs. Coupled with a transparent, predictable licensing model that avoids punitive fees and unexpected expenses such as overages, this approach enables organizations to scale observability sustainably while optimizing operational budgets.

By bringing these capabilities together in one platform, Kloudfuse helps teams drive down both direct and indirect costs, making observability more efficient, affordable, and aligned with delivering real business value.

Wrapping Up: The Future of Observability Starts with Striking the Balance Between Cost & Capability 

In the opening, we discussed how technological advancements have consistently redefined the balance between cost and capability, transforming infrastructure and operations. Kloudfuse is poised to do the same for observability. By integrating key principles like prioritizing critical business flows, unified and intelligent observability, intelligent telemetry pipelines, a purpose-built data lake, and AI-SRE tooling into a single, unified platform, Kloudfuse offers a compelling value proposition. 

For many organizations, gaining enterprise-grade observability at a fraction of the traditional cost is a game-changer. Kloudfuse empowers teams to achieve the depth, scale, and performance they need—often at just 20% of the cost of legacy solutions. While savings will vary by customer and use case, Kloudfuse consistently delivers comprehensive functionality and exceptional value, making advanced observability both accessible and economically sustainable.

Born during the emergence of OpenTelemetry, its architecture was built from the ground up to embrace open standards, unlike legacy platforms that have merely retrofitted existing systems. This foundational design ensures smooth, non-disruptive migrations and positions the platform to meet the next generation of observability challenges, including the rise of LLMs and agentic workflows. At its core is a unified observability data lake, built on Apache Pinot, enabling ultra-fast, high-cardinality analytics across logs, traces, metrics, and business signals, all correlated within a single source of truth.

This unified approach empowers teams to rapidly investigate performance and availability issues, whether in backend infrastructure or frontend applications. Unlike traditional solutions that penalize deep observability with soaring costs, Kloudfuse redefines efficiency by offering high scale observability at a flat pricing model and Bring Your Own Cloud (BYOC) deployment. This eliminates unnecessary egress and retention overhead, ensuring you don’t get penalized for gaining deeper insights. The result is accelerated root cause analysis, enhanced decision-making, and smarter SLO management, all while providing your teams the context they need to move faster and more strategically.

Call to Action: If you’re interested in learning more about how Kloudfuse can transform your observability strategy, I encourage you to reach out directly to the Kloudfuse team. They can provide in-depth demos, answer specific questions, and help you explore a proof of concept tailored to your organization’s needs. Don’t hesitate to connect with them to discover how Kloudfuse can empower your business with unified, AI-driven observability. 

With an impressive customer list that includes industry leaders across SaaS, healthcare, fintech, cloud-native enterprises and service providers—organizations that demand scale, reliability, and innovation—Kloudfuse is built to support your journey, whether you're modernizing legacy systems or building for the future.

This article was originally posted here.