AIOps

AIOps itself is a key focus in observability. With the rise of Large Language Models (LLMs), AIOps has once again been pushed to the forefront, becoming a major theme in nearly every industry prediction. Here, we avoid debating specialized terminology and uniformly refer to it as AIOps. The scope of its capabilities is broad:

• AIOps Platform: As AIOps capabilities rapidly evolve, they will eventually become platformized. The platform will manage the entire lifecycle of AIOps, integrating complex anomaly detection, root cause analysis, and automation functionalities, achieving unified AIOps capability integration.
• AI-Driven Prediction: AI-based failure detection and post-mortem analysis will shift toward AI-driven prediction to address the challenges posed by increasing data volumes and complexity. AI and machine learning algorithms will be used to predict issues before they impact business operations, thereby improving system performance and enhancing intervention capabilities.
• AIOps Automation: AIOps will significantly enhance the automation of IT operations (ITOps), automatically detecting and identifying potential issues while reducing the manual workload required for root cause analysis.
• Natural Language Interaction: LLM-based natural language interaction will enable IT personnel to conveniently query observability data, such as through Chat2PromQL and Chat2SQL.
• The Necessity of AIOps in Cloud Computing: As enterprises continue migrating to the cloud and adopting containerization and various cloud-native products, AIOps capabilities will become essential for quickly achieving cloud observability. AIOps will automate monitoring, analysis, and optimization of cloud resources to ensure efficient system operations.
• Integration of DevOps and AIOps: The boundaries between DevOps and AIOps will start to blur, possibly leading to unified operations teams. These teams will integrate AI expertise with traditional software development and IT operations, managing both software and AI model lifecycles while continuously improving processes.

OpenTelemetry

OpenTelemetry is a hot topic in observability, rivaling AIOps. With the backing of CNCF, major cloud providers, and independent observability vendors, OpenTelemetry has become the de facto standard in observability. Beyond Trace, Metric, and Log, OpenTelemetry introduced Profiling as a standard in 2024, aiming to standardize all data formats in observability and create unified correlations. Due to its vendor-neutral OpenTelemetry protocol and OpenTelemetry Collector, it is expected to solidify its role as the cornerstone of telemetry data collection in 2025. Since OpenTelemetry only defines data formats and provides collection capabilities—while backend services are implemented by vendors—more vendor-developed tools will emerge in 2025.

Unified Observability Platform

A major trend in observability in 2025 is the shift toward unified observability platforms. These platforms will consolidate Log, Trace, Metric, Event, and Profile into a single centralized view, offering several advantages:

• Eliminating data silos between monitoring tools and strengthening data correlation.
• Enabling seamless visualization and troubleshooting across hybrid and multi-cloud environments.
• Simplifying root cause analysis by providing comprehensive insights from a single interface.

As observability continues to evolve, vendors like Datadog, Splunk, and New Relic are leading the shift toward higher integration and efficiency.

Observability Shift-Right

The number of consumer and industrial devices in edge computing environments is expected to grow rapidly. These devices will continue to offer greater computing and connectivity capabilities, necessitating the expansion of observability and monitoring to edge devices. For observability vendors that have yet to support this, addressing this demand in 2025 will be critical to serving customers extending their technology stacks to edge environments.

Additionally, companies will place greater emphasis on frontend monitoring, which involves real user experience tracking. This monitoring must extend to various edge and endpoint devices. The focus of observability will shift from aggregate metrics to granular details, with businesses prioritizing individual customer monitoring over overall percentile distributions. Key requirements for observability tools include:

• Lightweight data collection: Deployable in resource-constrained IoT scenarios with some on-device processing capability.
• Efficient, low-latency global networking support: With built-in network acceleration capabilities.
• Cost-effective large-scale data storage and computation: Supporting cold and hot storage separation.
• Real-time global data aggregation: Enabling unified views without moving data.

Observability Shift-Left

Platform engineers, operations engineers, DevOps teams, and all stakeholders are realizing that introducing observability during the development cycle is highly beneficial for developers. This is particularly crucial for highly distributed and interconnected services and applications like Kubernetes. Beyond testing, observing the stack and its interactions with other application components in fine detail throughout the development cycle is another key aspect of observability. This trend is expected to see broader adoption in 2025.

With the maturation of Profiling technologies over the past two years, developers can now quickly integrate profiling and tracing early in development to observe software behavior in detail. This enhancement significantly improves developer experience by providing an unparalleled view of code impact, facilitating faster and more cost-effective optimizations.

Gartner describes this shift-left trend as part of Observability-Driven Development (ODD) engineering practices. By designing observable systems, engineers gain fine-grained visibility into system states and behaviors early in the development cycle and in production, making it easier to detect, diagnose, and resolve unexpected anomalies.

The Next Frontier in Platform Engineering: eBPF

Platform teams are experiencing significant growth. A survey by Grafana on observability found that nearly 25% of respondents work in platform roles. As platform teams become more critical, their responsibilities are expanding to include emerging tools and technologies—such as eBPF. Initially a trendy technology, eBPF is now becoming a cornerstone of modern platform engineering, fundamentally reshaping how organizations handle observability and security. Currently, eBPF is on the verge of a major transformation.

One significant change driven by eBPF is the shift of profiling and overall observability responsibilities from application teams to platform teams. The maturation of OpenTelemetry’s Profiling protocol and its integration with eBPF will enable standardized platform-level collection and processing of observability data.

The Next-Generation Core of Observability: Log

With enterprise digitization reaching an all-time high in 2024, development, security, and operations teams will need to collaborate more closely to solve the most complex challenges in business, technology, and security operations. This evolution has led to the rise of AI-driven observability platforms and a deeper understanding of log data as a crucial system record. In 2025, AI/ML and generative AI technologies will unlock unprecedented insights from structured and unstructured log data, providing unmatched context and intelligence for observability in applications and digital services.

Additionally, log analysis and management tools will see major technological advancements, including:

• Scalable analytics techniques
• Cost-efficient cold-hot storage separation
• Data lake capabilities

Cost-Effective Observability

With growing system complexity, observability costs are also rising. By 2025, organizations will implement the following cost-saving strategies:

• Smarter data sampling and retention strategies to reduce storage expenses.
• Serverless observability tools with usage-based pricing models.
• Balanced trade-offs between functionality and cost-effectiveness.

Beyond Traditional IT Operations

By 2025, observability trends will go beyond traditional infrastructure, middleware, and application monitoring, extending into:

• Business Process Observability: Providing insights into customer product usage and company operational efficiency.
• DevSecOps Observability: Ensuring secure and efficient deployments.
• Sustainability Observability: Tracking and optimizing carbon neutrality footprints through telemetry.

These advancements will redefine the potential and scope of observability.

From Reactive to Proactive Observability

As user expectations for application experience continue to rise, businesses increasingly demand observability systems capable of predicting service disruptions, capacity issues, and performance degradation in advance. This proactive approach helps organizations mitigate risks before they impact end users, improve reliability, and reduce unplanned downtime.

Unlike traditional AIOps methods, which often struggled due to a lack of contextual understanding, next-generation AI-driven observability integrates cross-system observability data, enabling rapid root cause identification and predictive cascading failure detection, making proactive prevention possible.

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