Real-Time Observability Without Operational Overhead: What’s Next?

Observability is meant to provide clarity, speed, and confidence in modern systems. Yet for many organizations, it has become a source of complexity, cost, and operational drag.

Managing pipelines, tuning clusters, and balancing scalability against system costs all come with significant overhead. This not only leads to operational inefficiencies, but also diverts valuable resources that could otherwise be spent on meaningful innovation.

That’s why modern organizations and observability vendors are rethinking the foundation of how data is fed into observability platforms. This goes beyond the front-end experience. You’ll see both traditional and modern vendors offer fully managed observability and security platforms, increasingly with AI-powered components to streamline ad-hoc data exploration. While helpful, this doesn’t solve the challenge of optimizing infrastructure costs and scalability on the backend一which can ultimately detract from front-end visibility too, when data is selectively filtered out to keep costs at bay.

We believe there’s a better way to approach this challenge. By leveraging real-time datastore technology as a data layer for observability, organizations like Netflix, PayTM, and Salesforce have drastically reduced infrastructure costs and operational overhead.

The Traditional State: Operational Overhead Crisis

Traditional observability architectures demand constant babysitting. Engineers must fine-tune storage indexes, provision new nodes manually, manage hot and cold data tiers, and balance cost against performance. This becomes even more complicated when working with multiple observability vendors and data sources.

Even worse, these systems often require specialized expertise just to operate effectively. Teams are forced to hire and train platform engineers whose sole job is to keep observability systems alive. Instead of gaining agility, organizations find themselves burdened by technical debt.

The Ideal State: Observability That “Just Works”

Imagine an observability platform that simply works, without demanding constant care and feeding. No intricate cluster sizing. No manual tuning. No arcane performance optimization rituals. No compromises on the scope or fidelity of data included for full visibility.

Modern observability should be:

• Simple: Easy to adopt and easy to manage.
• Scalable: Elastically handles growing telemetry volumes without skyrocketing costs.
• Fast: Delivering real-time insights with subsecond latency for faster issue resolution.
• Accessible: Usable by developers, SREs, and analysts without specialized database knowledge (and without the need for large R&D teams to manage the backend).

This is not a dream. New architectures and technologies are making it possible today.

“Our response time with Imply has improved from minutes to seconds, and our ability to fully analyze attacks has shrunk from hours to minutes.”
一 Senior Director, Leading FinTech Company

Technology Shifts Enabling Simpler, Faster Observability

Several trends are converging to redefine how observability works:

• Automated Infrastructure Management: By simplifying storage tuning at the data layer or the technology powering your observability application, organizations can focus on using observability data, not running observability systems.
• Separation of Compute and Storage: Decoupling search performance from storage footprint facilitates cost-effective scaling. Real-time databases like Imply enable lightning-fast searches on massive datasets without complex tuning.
• Auto-Scaling: Tools like Imply Polaris and Apache Druid® can scale ingestion dynamically with load, ensuring real-time performance while eliminating the need to guess at peak capacity and saving costs when event flow is lower.
• Unifying Traces, Metrics, and Logs: Modern vendors use a wide, un- or semi-structured datastore approach to support high cardinality and dimensionality for a unified dataset while reducing observability costs (unlike traditional observability platforms that store traces, metrics, and logs separately). Furthermore, Imply’s schema auto-discovery solves the challenge of manually redefining schemas for efficient querying, while reducing storage needs.

The result? Observability platforms that deliver power without the pain.

“Druid gives us the flexibility to define pre-aggregations, the ability to easily manage ingestion tasks, the ability to query data effectively, and the means to create a highly scalable architecture.” 一 Dun Lu, Lead Software Engineer, Salesforce

What’s Next: The Future of Observability Platforms

The next frontier for observability goes beyond ‘managing less’ to ‘doing more’:

• Zero-Tuning Architectures: Systems that auto-optimize based on workload patterns, maintaining full-fidelity data without skyrocketing costs.
• Experiences Designed for IT, SRE, and Dev: Query languages and tools designed for the people closest to the code and systems, not just backend specialists.
• AI-Assisted Insights: Automated anomaly detection, root cause analysis, and intelligent alerting for faster detection and response (MTTD and MTTR).
• Unified Telemetry: Metrics, logs, and traces available in a single system with seamless cross-search capabilities.
• Redefining the Data Layer for Observability: Vendor lock-in makes it costly and challenging to test new tools. Rather than replacing your existing observability platform as an all-in-one solution, Imply can serve as an underlying data layer that feeds into your preferred observability platform with reduced storage costs.

These innovations will enable organizations to go from reactive monitoring to proactive optimization—without hiring an army to manage it.

“Imply Polaris helped us overcome the hurdles of inflexible data and costly infrastructure to transform our observability system. We were able to streamline analytics and alerts, improving site performance while displacing InfluxDB and offsetting costs from Datadog.” 一 Nativ Ben-David, Web Architect, Natural Intelligence

Why Now? Why Imply?

At Imply, we focus on supporting our customers’ vision for observability 一 build or buy 一 backed by an engine that works seamlessly on the backend. Built on Apache Druid and designed for real-time, high-scale environments, Imply’s solutions eliminate operational overhead while delivering unparalleled speed and scalability.

Whether you’re monitoring millions of IoT devices, financial transactions, or cloud services, Imply provides the simplicity, elasticity, and real-time insights you need—without the cost and operational burden you don’t. Built on Apache Druid, Imply delivers real-time speed at any scale, elasticity without complexity, full-fidelity data coverage, and a dev-friendly platform that makes it easy to explore ad-hoc exploration out of the box.

The Future is Now

The next generation of observability is about freedom: freeing teams from operational complexity, freeing data to deliver real-time insights, and freeing organizations to innovate without constraints.

Observability that “just works” isn’t a luxury. It’s the future. And we’re building it now.