We started Kloudfuse because the observability market forces bad choices on the enterprises it claims to serve. Send every metric, log, and trace to a vendor’s cloud and accept the cost, compliance, and sovereignty implications—or run an open-source stack in-house and accept that your platform team will spend more time maintaining observability than improving the systems it monitors.

Neither option works for organizations operating at scale under real compliance constraints. Kloudfuse 4.0 is our answer to that false binary: unified observability that runs inside your infrastructure, meets federal-grade security standards by default, and delivers AI-native capabilities under the same governance model you apply to every other production system.

Why Now

Three curves are colliding simultaneously:

1. The compliance bar is rising. On September 22, 2026, NIST will move all FIPS 140-2 certificates to the Historical List. After that date, FIPS 140-2 validated modules should not be used for new federal systems or procurements. Organizations still on FIPS 140-2 face a mandatory transition with a shrinking runway.

2. AI access to production data is becoming operational. Natural-language interfaces to observability telemetry are no longer experimental. Datadog, New Relic, and others now market AI-driven operational workflows—validating that this is a real category direction. But the governance layer for AI access to production data remains largely unsolved across the industry.

3. Telemetry volume and cost complexity are compounding. As infrastructure scales, observability spend scales faster. Datadog’s 2023 earnings materials documented that larger enterprise customers were actively scrutinizing and optimizing observability usage. Meanwhile, the CNCF’s 2023 Annual Survey identified observability and monitoring as the second-most-cited challenge in running Kubernetes at scale.

The False Choice the Market Created

SaaS Observability: Simplicity at a Compounding Cost

SaaS observability platforms deliver genuine operational simplicity. But the economics work against enterprise buyers over time. Pricing models based on per-host, per-GB, per-custom-metric, and per-analyzed-span create multi-dimensional cost exposure that grows harder to predict and govern as infrastructure scales.

Beyond cost, SaaS platforms create a data sovereignty tension. Your production telemetry—service dependencies, error rates, deployment patterns, internal endpoint names—lives in someone else’s cloud. For organizations subject to HIPAA, PCI DSS, CMMC, or operating in defense and intelligence contexts, this is a compliance gap that procurement and security teams flag during every evaluation cycle.

Open-Source Stacks: Control Without Capability

Running Prometheus, Grafana, Loki, and Tempo in-house preserves data sovereignty. But it transfers an enormous operational burden to your platform team. There is no unified query language across these tools, no built-in AI layer, no compliance certification, and no cost visibility. Each tool has its own storage backend, scaling model, and failure mode. Correlation across signals requires custom engineering. Every improvement—better retention, faster queries, cardinality control—requires work that does not ship customer value.

We built Kloudfuse to occupy the space between these models: a platform that runs in your VPC with the operational simplicity of SaaS and the control of infrastructure you own.

Three Business Drivers Behind 4.0

Compliance Has Become a Deployment Blocker

In regulated industries and government, the observability platform—which touches every service, ingests every log, and stores every trace—is often the largest surface area an organization hasn’t hardened to current cryptographic standards. HIPAA-regulated healthcare organizations, PCI DSS-bound financial services firms, and any enterprise pursuing CMMC Level 2 now face requirements that flow from the same NIST standards driving the FIPS 140-2 sunset. Procurement cycles stall. Security reviews drag. Deployments that should take weeks stretch into quarters.

AI Is Transforming Operations—But Governance Is Missing

Every engineering organization is adopting AI agents that interact with production systems. The promise is real: natural-language queries against live telemetry, faster root cause identification, intelligent correlation across signals. But in practice, AI access to observability data runs in one of two modes—either completely ungoverned or completely blocked. Neither serves the business.

Cost and Complexity Scale Faster Than the Business

A metric label with unbounded cardinality can multiply storage costs by orders of magnitude before anyone notices. Recording rules accumulate until maintaining them becomes a full-time job. Ingestion, query, and control plane contend for the same resources, meaning a surge in one workload degrades all three. Platform engineering leaders need visibility into what drives cost, the ability to separate workloads, and performance that holds as data volumes grow.

What We Built Differently in Kloudfuse 4.0

Security Is Architecture, Not a SKU

Most observability platforms treat security as a tier. FIPS-validated cryptography ships in a federal product. Hardened containers are documented in a post-deployment guide. Supply chain signing is a roadmap item.

We believe security must be a property of the architecture—present in every deployment, not unlocked through procurement.

“Enterprise buyers should not have to choose between strong security and operational simplicity. We built Kloudfuse 4.0 so that customers start with a security posture that meets the expectations of regulated environments, without being forced into a separate product or a more complex deployment model. That means faster security reviews, broader deployment confidence, and a platform organizations can standardize on across both commercial and federal workloads.”

— Pankaj Thakkar, CEO & Co-founder, Kloudfuse

The containers ship hardened on Red Hat UBI9-minimal. Images and Helm charts are cryptographically signed. Every service runs as non-root by default. FIPS crypto policy is enabled at the operating system level. This is how the platform ships—not a configuration guide customers follow after deployment.

AI Access to Production Data Needs Production-Grade Governance

The Kloudfuse MCP Server gives AI agents direct access to production observability data using natural language. Connect Claude, ChatGPT, custom models, or IDE-embedded agents and query live metrics, logs, traces, profiling data, RUM sessions, and APM execution breakdowns—with automatic FuseQL translation.

What makes it enterprise-ready is how the server is governed.

“AI access to production data is becoming a real operational requirement, but most organizations still do not have a governance model for it. We built the Kloudfuse MCP Server so enterprises can adopt AI-driven observability without creating a new control gap. When identity, auditability, and query safety are built into the architecture, security teams can approve the capability instead of blocking it.”

— Pankaj Thakkar, CEO & Co-founder, Kloudfuse

The server scales horizontally behind a load balancer as a centrally managed enterprise service. For organizations where the alternative is either ungoverned local AI tools or no AI access at all, this offers a third path: governed, audited AI observability that security teams can approve.

Observability Platforms Should Scale Like the Systems They Monitor

Modern data infrastructure separates concerns. Kafka separates brokers and consumers. Data warehouses separate compute and storage. Each workload scales independently based on its actual resource profile.

Observability platforms have not made this transition. Most run as monoliths where ingestion, query, and control plane share resources.

“Ingestion, query, and control plane workloads do not behave the same way, so they should not be forced to scale the same way. Treating them as shared compute creates architectural debt that compounds with every new service and every increase in telemetry volume. Kloudfuse 4.0 introduces workload isolation so each layer can be tuned and scaled independently, improving both efficiency and resilience at enterprise scale.”

— Ashish Hanwadikar, CTO & Co-founder, Kloudfuse

“At our scale, the observability platform itself must never become a constraint. It needs to grow with the business without increasing the complexity teams face when ingesting, querying, or managing telemetry. Kloudfuse 4.0’s workload isolation is critical because it allows each layer to scale independently based on real usage patterns, which better reflects how cloud environments operate. This approach gives us a more resilient and efficient foundation as our platform continues to expand.”

— Kasi Sockalingam, Cloud Engineering Leader, Automation Anywhere

What This Means for the Business

Shorter procurement cycles. When the platform ships with FIPS 140-3, STIG hardening, SOC 2, and architectural data residency, security review becomes a documentation exercise rather than a multi-quarter remediation project.

AI adoption without organizational friction. Engineering teams gain AI-native observability today—governed, audited, and query-safe—while peers at other organizations are still debating whether to allow AI access to production data.

Predictable, controllable economics. No per-GB ingestion pricing. No data egress fees. The Metrics Cardinality Explorer surfaces cost drivers before they reach the invoice. Multi-rollup resolution eliminates recording rules—removing a maintenance burden that accumulates across hundreds of configurations, each a potential source of drift or misconfiguration. Workload isolation means you scale what needs scaling, not the entire platform.

Engineering capacity returned to building. New Relic’s 2024 Observability Forecast reported that organizations practicing full-stack observability experienced fewer outages, lower median annual outage cost, and stronger MTTR outcomes compared to those with fragmented tooling.

“At our scale, reliability depends on how quickly teams can identify issues, understand service dependencies, and take action with confidence. Kloudfuse has helped simplify that by giving our teams a more unified view of production behavior across the platform. With Kloudfuse 4.0’s workload isolation, we can also scale observability infrastructure more deliberately as demand grows, without creating new operational bottlenecks. That combination strengthens both reliability execution and long-term resilience.”

— Michael Kuperman, Chief Reliability Officer & GM, Zscaler

“For SRE teams, the value of observability comes down to how quickly you can move from telemetry to action. Kloudfuse has already helped us simplify that foundation. At Zscaler’s scale, that simplification translates directly into faster troubleshooting and less time spent proving dependencies across teams.”

— Duncan Winn, VP of Engineering, Zscaler

Why We Call It Self-SaaS

Every capability described here runs inside your VPC. On your cloud account. On your infrastructure. Your observability data—every metric, log line, trace, and user session—stays within your environment.

We call this Self-SaaS because it resolves the tension between operational simplicity and infrastructure control. Managed updates, expert support, platform-as-a-service ease of operation—without sending a byte of production telemetry to a third party. You control retention, storage costs, and access. Your compliance posture is a function of your own security controls, not a vendor’s data handling practices.

Who Kloudfuse 4.0 Is Built For

• CISOs and Security Leaders whose observability platform is the largest unresolved item on their compliance roadmap. Who need validated cryptography, hardened containers, and a signed supply chain without migrating to a separate federal product.

• VPs of Engineering and Platform Leaders running observability for hundreds of services and thousands of developers. Who need workload isolation, cost visibility, and AI capabilities their security team will approve.

• CTOs and Technology Executives making platform decisions that compound for the next five years. Who see the FIPS 140-2 sunset, the AI governance gap, and the cost trajectory of per-GB pricing as converging problems that demand a single architectural decision.

What We’re Still Building

We are not done. AI workloads are creating telemetry patterns that existing models do not capture well. Multi-cloud and hybrid architectures are fragmenting the data plane. The regulatory landscape—from the EU AI Act to evolving NIST frameworks—will continue to raise the bar on what compliant observability means.

We are building toward a future where observability is invisible infrastructure: always present, always secure, always intelligent, never the thing your team spends time operating. Kloudfuse 4.0 is the most significant step we have taken toward that vision. But it is a step, not a destination.

Observability should not force a trade-off. Not between security and simplicity. Not between AI and governance. Not between control and scale.

See how Kloudfuse 4.0 runs in your environment. Request a demo.

About Kloudfuse

Kloudfuse is a unified observability platform integrating with over 700 infrastructures, cloud services, and applications. Built on open standards like OpenTelemetry and PromQL, Kloudfuse deploys within customer VPCs to deliver metrics, logs, traces, events, and real user monitoring with enterprise security, AI-native capabilities, and cost control. Trusted by Zscaler, GE Healthcare, Workday, Tata 1mg, and Automation Anywhere.