In the last decade, SaaS observability platforms revolutionized how teams monitor systems, troubleshoot issues, and make data-driven decisions. But today, we’re at a turning point—the “easy button” for observability is a thing of the past. Traditional SaaS platforms fail to meet the data ownership, cost, and scalability demands of modern, distributed and AI-native systems.

In the later years—especially with the transition to cloud-native architectures and the rise of platform engineering—self-hosted observability became a natural counterweight. It promised control and sovereignty over telemetry data. Yet, it brought its own set of problems: complex setup, ongoing maintenance burdens, fragmentation across multiple tools, and heavy operational overhead. Teams were forced to trade off power for pain.

Now, with the advent of AI, an entirely new push is reshaping the landscape. Companies don’t just want to monitor their infrastructure and applications—they want to own their stack, their AI pipelines, their data, and their compliance posture. Observability can no longer live in someone else’s cloud; it must live alongside the workloads it protects and powers.

Enter: Self-SaaS Observability—a new category for a new era. It combines the simplicity of SaaS with the control of self-hosted solutions, without the trade-offs.

In this post, we explore why this shift is happening, who it’s for, and how Self-SaaS is built for the future of cloud-native and AI-native workloads.

SaaS Observability: From Game-Changer to Bottleneck

SaaS platforms like Datadog and New Relic helped teams get up and running quickly by consolidating logs, metrics, and traces under one roof. But beneath the sleek interfaces lie real limitations:

• Exploding Costs: Usage-based pricing doesn’t scale predictably. A spike in telemetry meant a spike in your bill. Vendors often charge for ingest, compute/root cause analysis, retention, and even users on the system. The more data, the more queries, the more users—the more your costs balloon.

• Sampling & Data Loss: To keep costs in check, teams sample or trim data before it’s ingested—killing fidelity and visibility. Many companies even invested in additional telemetry pipelines to filter or shape data before it reaches the SaaS platform—ironically adding more complexity and cost.

• Vendor Lock-In: Proprietary agents and closed data formats make switching tools a nightmare. Even with OpenTelemetry gaining traction, most SaaS platforms weren’t designed for it. Advanced features still depend on proprietary collectors, and even “Otel-compatible” data often remains locked behind extraction limits, proprietary query languages, and expensive egress.

• Compliance Concerns: Sensitive telemetry sits in multi-tenant environments, exposed to security breaches—raising red flags not just for regulated industries, but increasingly for any company building AI-native workloads.

As systems grow more distributed, tracing issues across microservices requires correlating data from fast-scaling, short-lived, ephemeral workloads. That means capturing and storing more data, paying more to SaaS vendors, and facing even deeper vendor lock-in—all while reducing security posture.

The False Choice: SaaS vs. Self-Hosted

You might ask: what about self-hosted? After all, open source tools exist to mitigate SaaS costs, vendor lock-in, and compliance risks.

And yes—self-hosting does give you:

• Full data sovereignty

• No vendor lock-in or rate limits

• Deep customization and control

• Open query access for data extraction and analysis

But the downsides are significant:

• Dedicated ops teams required for upgrades, orchestration, and lifecycle management

• Complex setup and maintenance

• Tool sprawl—Prometheus for metrics, Loki for logs, Jaeger for tracing, etc.

The result? A false binary between “easy but expensive” SaaS and “powerful but painful” self-hosted.

So, if SaaS doesn’t scale and self-hosted is too complex—what’s left?

Today’s Needs: AI-Native and Agentic Workloads

Today, nearly every company has an AI initiative. From copilots to autonomous agents, AI workloads are rapidly moving from pilot projects to production systems.

However, monitoring AI-native applications is far more complex than their Cloud-native counterparts. Teams must:

• Trace calls between microservices, LLMs, vector databases, external APIs, and custom pipelines

• Monitor not just performance and latency, but correctness and trustworthiness

• Handle non-deterministic AI behavior that requires ground-truth validation with data that often lives inside company walls

• Protect intellectual property and sensitive datasets that cannot be shipped to a third-party SaaS vendor

• Manage real-time, high-volume telemetry generated by agents and LLMs without running into rate limits or runaway costs

AI-native workloads demand an observability system that is:

• Comprehensive: interoperable with LLMs, MCP servers, vector DBs, and microservices for end-to-end tracing

• Programmable: with runtime validators, allow/deny policies, and human-in-the-loop workflows to prevent destructive or unsafe actions

• Sovereign: keeping sensitive datasets, prompts, and evaluation metrics within company boundaries

• Scalable in real time: AI workloads generate orders of magnitude more telemetry than traditional services

• Full fidelity: no sampling, no rate limits—because losing context in an AI pipeline is losing trust in the system; full-fidelity data is essential for audit trails and compliance

• Contextual: beyond metrics and logs, observability in AI must track data quality, context, and correctness

• Cost-efficient: delivering observability at scale without runaway spend

Clearly, neither SaaS nor self-hosted solutions can deliver this.

Enter Self-SaaS: The Best of Both Worlds

Self-SaaS is the clear middle path: you run observability within your infrastructure and under your control, but with SaaS-grade automation, simplicity, and UX.

Key criteria for Self-SaaS:

• Deploy anywhere: AWS, Azure, GCP, hybrid, or multi-cloud

• Data never leaves: Full privacy, compliance-ready, no egress fees

• No rate limits: Ingest and query at full fidelity—whether troubleshooting issues or feeding ML/AI pipelines

• Predictable pricing: Flat, no seat-based charges, no usage spikes, no extra fees for retention

• SaaS-like experience: Auto-scaling, auto-upgrades, high availability, multi-AZ support, intuitive dashboards for ops teams

Where self-hosted left gaps in maintenance, upgrades, and scaling, Self-SaaS fills them.

Comparison Table

When Self-SaaS Matters Most

1. You’re Scaling Fast
   Telemetry is exploding. SaaS costs don’t just scale—they spiral. Self-SaaS restores predictability.

2. You Handle Sensitive Data
   Healthcare, finance, government, and AI companies cannot risk telemetry in multi-tenant, vendor-managed SaaS platforms.

3. You Need Full-Fidelity Observability
   Mission-critical apps can’t afford sampling or partial signals. Missing context means missed signals and root causes.

4. You’re Building AI-Native Workloads
   AI/ML pipelines demand unrestricted telemetry. Rate limits break them. Proprietary AI data is your crown jewel—why expose it to third parties?

5. You Want True Data Ownership
   Leverage your own infra and your cloud credits. Enforce governance policies, and fully own your costs, performance, and data strategy.

What to Look for in a Self-SaaS Platform

Not all Self-SaaS solutions are equal. Look for:

• Full data access: Open formats with no limits, no lock-in, supporting open standards and query languages

• Interoperability and programmability: Seamless integration with your workflows and AI/ML pipelines

• Data privacy: Data never leaves your environment, ensuring security and compliance

• Flexible deployment: Multi-cloud and cloud-agnostic options to meet sovereignty and residency requirements

• Built-in automation: Autoscaling, high availability, and lifecycle management to remove the operational complexity of self-hosted solutions

Core Architecture: Control Plane + Data Plane

To achieve Self-SaaS, a fundamental architectural approach is critical—a clear separation between the data plane and the control plane.

• The data plane handles high-throughput telemetry ingestion, processing, and monitoring close to the source—keeping your data private and fully under your control.

• The control plane acts as an intelligent command center, managing orchestration, policy enforcement, upgrades, scaling, and overall infrastructure health. It’s primarily operated by your Cloud Ops or Platform Engineering team as a central management layer, or optionally, the Self-SaaS vendor can manage it on your behalf.

Core Capabilities

1. Centralized Observability Management
   Gain a unified view across all managed clusters, services, agents, and telemetry streams—from a single dashboard—across multiple regions and environments. Track agent versions and hosts to maintain configuration consistency with ease.

2. Deployment Health Monitoring
   Monitor the real-time health of your observability deployments. Collect cluster- and node-level data such as namespaces, Helm chart versions, software versions, resource allocations, and readiness states to quickly detect failed pods, degraded services, and misconfigurations.

3. Usage Monitoring
   Track resource utilization, plan capacity, optimize uptime, and maintain visibility across environments, teams, and users.

4. Real-Time Agent & Stream Analytics
   Monitor metrics, logs, traces, and other telemetry in real time—without exposing the underlying data. Detect bottlenecks to ensure pipeline reliability before performance issues occur. Track internal service metrics—latency, query response times, and error rates—to prevent downstream impact.

5. Flexible Control Plane Deployment
   Deploy the control plane to meet regulatory or operational requirements. In restricted environments, it can run within the same boundary as the data plane with limited access, or in the vendor’s infrastructure while keeping your data private. Agent-only or decentralized setups are also supported for flexible deployments across diverse environments.

Kloudfuse pioneered this Self-SaaS architecture, giving teams the control of self-hosted solutions with the simplicity and automation of SaaS.

Self-SaaS for the Future: Agentic Observability

The next era of observability goes beyond infrastructure signals, application logs, and RED metrics. It’s about dynamic, autonomous systems that observe, reason, and act in real time. These systems need full access to their data—without sampling, rate limits, or security risks—as they integrate with knowledge bases, agents, LLMs, and internal or external databases. 

With Self-SaaS Observability, You Can Achieve:

• AI Observability: Monitor AI agent activity—including prompts, prompt-response pairs, database or API calls, and more. Track LLM tokens to control costs and assess model behavior to prevent hallucinations. Achieving this requires a comprehensive observability platform that goes beyond traditional metrics, logs, and traces, and supports integration with other AI workflow components, such as vector databases, model evaluation pipelines, and external knowledge bases.

• Large-Context Workflows: Correlate reasoning and actions across complex, multi-step AI processes without sampling or rate limits to ensure full-fidelity insights. Full fidelity is critical because AI agents generate high-volume, non-deterministic data; losing any portion can break reasoning chains, obscure errors, or reduce the accuracy of downstream evaluations.

• Autonomous Remediation Loops: Trigger automated workflows from real-time events by programmatically integrating observability into your systems. Safeguards such as human-in-the-loop workflows ensure that automated actions remain safe, compliant, and aligned with organizational policies.

• Zero-Trust Enforcement: Restrict AI agents to specific tools or datasets via MCP servers using RBAC, short-lived credentials, and strict access controls. Clearly defined policy boundaries reduce the risk of accidental data leaks and keep agents operating securely within intended limits.

This is the future of observability—and it cannot be achieved when your data is locked inside a SaaS vendor.

The Takeaway

Self-SaaS isn’t just another option in the mix—it’s the evolution of observability.

If you’re building AI-native, globally distributed, or compliance-driven systems, it’s time to move past the old models. Self-SaaS gives you both control and simplicity, protecting your data while enabling full-fidelity observability.

Discover how Kloudfuse is leading the Self-SaaS movement:

Learn more:

→ Download the paper: The Rise of Self-SaaS: A New Category for a New Era
→ Request a Demo