Observability Trends in 2026: AI, Cost-Efficiency, and Scale

Many organizations are generating petabytes of telemetry data, including billions of lines of logs on a daily basis. This has made observability both a necessity and a challenge.  Enterprises, cloud-native companies, and digital-first organizations alike are facing a pressing need to not only capture but understand and act upon massive volumes of operational data in real time. Considering that observability is now a business-critical capability for ensuring uptime, performance, and user satisfaction, addressing the challenges of scale has become more important than ever.

As data volumes surge and observability pipelines struggle with massive scale, understanding what’s changing and how to stay ahead of the curve has never been more important. This post explores ten key trends shaping observability and the strategies that make large‑scale observability faster, leaner, and far more cost‑effective.

TL;DR

• Stateless infrastructure lowers storage costs significantly.
• Monitoring is evolving into autonomous action.
• Observability 2.0 uses high-cardinality wide events.
• Decoupled compute and storage enable faster, more flexible analytics.
• The “shift-left” mindset moves observability earlier in the development process.

1. Stateless Infrastructure and Object Storage

Stateless infrastructure is increasingly seeing more applications in observability. The rise of cloud-native architectures, Kubernetes, microservices, and containerization has fueled a shift to stateless infrastructure. This includes separation of compute and storage, with each component (such as ingest and query) decoupled. These decoupled, stateless systems scale more easily and remain resilient under load. Not surprisingly, more teams are converting from stateful to stateless infrastructure.

Object storage, such as Amazon S3, Google Cloud Storage, and Azure Blob Storage, has become the de facto choice for storing observability data at scale, and a great choice for stateless systems.

These storage systems provide:

• Virtually unlimited capacity
• High durability and availability across regions
• Cost-effective tiered pricing models
• Compute on demand

Traditional observability platforms often have tightly coupled storage and compute. They may rely on expensive local storage or in-memory solutions for faster queries, but these solutions are too expensive at scale. Observability tools need to evolve so that they can stream logs, traces, and metrics to object storage, enabling real-time, ad hoc, and batch analytics at a lower cost and with fewer infrastructure bottlenecks. 

The cost-effectiveness of this architecture allows organizations to keep data for far longer, unlocking use cases around long-term analytics and AI-based learning.

This leads into our next point, which highlights the increased importance of using AI.

2. AI-Driven Observability

AI tools are increasingly playing a role in supporting observability use cases. This includes support through all stages of the observability pipeline, including ingesting, parsing, correlating, and acting on telemetry data in the process of AIOps.

Natural Language Processing (NLP)

Modern observability platforms will see more integrations with NLP to enable intuitive query capabilities and make it easier to democratize data. Instead of relying on complex queries, even non-technical stakeholders will be able to ask questions like: “Why did service A fail last night during peak traffic?”

The system translates this into a series of time-based, context-rich queries across logs, metrics, and traces, pulling relevant incidents, error codes, and dependencies to generate a response. 

Tools like OpenAI Codex, ChatGPT, and Elasticsearch’s NLP modules are being embedded directly into dashboards and Slack integrations.

Model Context Protocol (MCP)

Model Context Protocol is an open protocol that makes it easier for developers to build secure, two-way connections between data sources and AI-powered tools. Through MCP, developers can either connect directly to MCP servers or build their AI applications to access these servers. This will lead to more data being accessible to security leaders, improving observability.

Anomaly Detection

Machine learning models now autonomously baseline system performance and detect statistically significant deviations. These models can handle complex patterns such as:

• Daily and weekly seasonality
• Inter-service traffic shifts
• Container restarts and rolling deploys

Advanced platforms integrate unsupervised learning techniques like isolation forests and Gaussian Mixture Models, while some now leverage LLM-powered embeddings to group similar logs or traces.

Agentic AI

Agentic AI takes automation even further than basic AI applications. While these systems haven’t been fully developed yet, they will transform observability when they arrive. These systems operate as autonomous agents that:

• Continuously monitor telemetry streams
• Correlate logs, metrics, and traces in real time
• Recommend or execute remediation actions
• Learn from historical context

In some environments, agentic AI will be able to trigger auto-scaling, reroute traffic, halt a faulty deployment pipeline, or even implement other mitigations without human intervention.

Data Democratization

AI tools have significantly lowered the entry barrier for using observability platforms. Through intuitive UIs, explainable ML models, and self-service query builders, non-engineering teams like QA, product, and business ops now interact directly with observability insights. 

Additionally, dashboards can be generated on demand, enabling faster cross-functional feedback loops and more accessible troubleshooting for non-engineers.

3. Data Interoperability

Observability no longer lives in isolated silos. Metrics, logs, traces, events, and profiles are expected to interoperate. This demand has driven widespread adoption of open standards.

OpenTelemetry has become the universal observability framework, offering consistent instrumentation libraries across popular languages (Go, Python, Java, .NET). This empowers teams to collect:

• Structured logs
• Metrics (counters, gauges, histograms)
• Distributed traces with context propagation

Additionally, the rise of open formats, such as Parquet, JSON Lines, Protobuf, and Apache Arrow, supports schema evolution and high-throughput query execution. Interoperability ensures that teams can:

• Switch vendors without re-instrumenting code
• Use shared dashboards and alerting rules across domains
• Correlate data from different pipelines more easily

The Hydrolix Connector for Apache Spark combines the cost and query efficiency of Hydrolix with the distributed computing power of Apache Spark. With this connector, you can query your Hydrolix data in Spark-compatible systems such as Databricks, AWS EMR, and Microsoft Fabric. Hydrolix offers long-term, cost-effective log analytics and retention, allowing your teams to explore data and train AI models in Spark-compatible systems.

4. Observability 2.0 and Arbitrarily Wide Events

Observability platforms are increasingly moving towards a new model for observability which is sometimes referred to as Observability 2.0. At the heart of this evolution is the concept of arbitrarily wide events, which are structured payloads that can carry hundreds of fields of contextual metadata in a single event.

Unlike traditional observability systems that require teams to define metrics or logs upfront, this model encourages emitting detailed, high-cardinality events that are rich in context. These events capture a snapshot of system activity, including user identifiers, request parameters, feature flags, environment tags, and more—all without being constrained by fixed schemas.

Tools like Honeycomb, Edge Delta, and Hydrolix embrace this model by allowing schema-on-read. Analysts can then perform ad hoc queries to discover patterns, bottlenecks, and regressions.

Advantages include:

• Eliminating the need to predict all observability needs upfront
• Capturing high-cardinality attributes like user IDs, deployment IDs, or A/B test flags
• More cost-effective, flexible ingest and query that allow you to capture more data while paying less to store and query it

This paradigm shift addresses the complexity of modern systems where rigid dashboards fall short of capturing “unknown unknowns.”

5. Cost-Effective at Petabyte Scale

There is also an increase in interest in searching for the best cost-effective solutions when working with big data.

Data growth is inevitable. The challenge lies in storing and querying data at petabyte scale without breaking the budget.

Modern observability platforms apply a combination of:

• Hot-warm-cold storage tiers: Immediate insights for active data, cheaper storage for older logs. However, when such storage tiers are set in place, accessibility to cold storage is limited to ensure cost-effectiveness.
• Intelligent sampling and filtering: Sampling traditionally involved throwing a randomized percentage of data away, but solutions are increasingly taking a more intelligent approach. Solutions will increasingly offer options like the ability to retain full data during incidents and apply aggressive sampling during normal periods. However, sampling, even when applied intelligently, can still come with a significant downside: gaps in data that make it harder to mitigate issues such as breaches.
• Ingestion policies: Solutions like Cribl Stream allow enterprises to filter logs based on content or severity before they hit storage. Filtering can also be used to filter attributes (columns). However, filtering can also potentially lead to gaps in data.
• Object storage optimization: Traditionally, object storage has been too slow for real-time analytics, hindering its usefulness for observability. Now platforms and enterprises are increasingly using techniques that optimize object storage for fast queries. These techniques include partition-based storage (often time-based), columnar storage with high-density compression, and approaches that maximize predicate pushdown and return narrow byte ranges of data.
• Retention policies: Additionally, policy-based retention and lifecycle management systems automatically expire or migrate data based on regulatory requirements or usage frequency.

6. Data Federation

Platforms are now starting to embrace a federated query model, allowing engineers to run analytics across distributed telemetry sources without data duplication.

Data federation is a virtual layer that allows users to access and query data from multiple, disparate data sources as if they were a single, unified database, without physically moving or copying data. Observability platforms can provide that virtual layer or they can even be one of the data sources for yet another data layer (such as Databricks).

When observability platforms act as the virtual layer, data can be stored in many different sources, reducing egress costs and making data pipelines simpler. And when observability solutions act as a source for other data layers, it makes observability data available for other use cases such as machine learning and business intelligence.

Read about the value of federating log data: Observability Isn’t Enough. It’s Time to Federate Log Data.

7. Data Sovereignty

Data sovereignty is also increasingly becoming a priority among governments and enterprises around the world.  Governments are implementing stringent data residency and processing requirements. Logs and observability data often include PII and metadata about customer actions, necessitating enhanced protection.

Leading platforms offer:

• Geo-fencing of data streams
• Customer-managed encryption keys (CMEK)
• Tokenization or redaction at the ingestion layer
• Field-level audit trails and access controls
• Flexibility to retain full control of observability data by keeping it within their own Virtual Private Clouds (VPCs), rather than having it walled into proprietary SaaS platforms

Having a sovereignty-conscious design going forward ensures compliance while maintaining operational observability. 

8. Edge Computing and Observability

Edge computing can bring computation closer to users and devices, but also creates fragmented telemetry ecosystems. This brings some challenges to the table:

• Intermittent connectivity
• Limited compute and storage on edge devices
• Diverse hardware and software stacks
• An overwhelming amount of log data (such as monitoring CDNs on edge services)

However, there are some solutions emerging in 2025:

• Streaming agents that buffer and compress telemetry at the edge
• WASM runtimes that enable secure, portable instrumentation
• Edge-native observability frameworks (e.g., Akri, KubeEdge, and OpenTelemetry Edge SDKs)

Having observability at the edge means companies can reduce mean time to recovery for local issues, maintain SLAs, and analyze telemetry data they wouldn’t have access to otherwise.

9. Shift to Modular Tools

Gone are the days of vendor lock-in via monolithic observability suites. Teams now prefer assembling toolchains that fit their specific needs. This makes smaller organizations more agile while being cost-effective.

A typical modern observability stack might include:

• Collection: Fluent Bit, Vector
• Aggregation and storage: Hydrolix, ClickHouse, Snowflake, S3
• Correlation and querying: Trino, Druid, DuckDB
• Visualization: Grafana, Superset, Redash

Benefits of modularity:

• Greater performance and cost control per layer
• Flexibility to adopt new technologies
• Resilience against vendor outages or pricing changes

API-first platforms can be used to make integration easier within existing DevOps workflows. 

10. “Shift Left” Mindset

Observability is becoming a design-time concern rather than a runtime patch. With this thought in mind, companies are adopting a “Shift Left” mindset. It means moving observability practices, like monitoring and debugging, to earlier stages of the software development lifecycle (SDLC), specifically during development and testing.

Here are some best practices that are emerging in 2025:

• Observability as code: More consistent, version-controlled dashboards, alerts, and metrics
• Pre-merge instrumentation testing: Ensuring telemetry output is valid before code is deployed
• Local observability sandboxes: Emulating production conditions for developers

Adopting observability earlier in the SDLC reduces debugging and troubleshooting time post-deployment, accelerates delivery, and improves reliability from day one.

Final Thoughts

Observability these days is highly focused on flexibility, intelligence, and scale. As enterprises embrace cloud-native, multi-region, edge, and AI-enabled architectures, their observability strategies must evolve.

Key takeaways:

• AI and agentic automation transform human-in-the-loop monitoring into AI-driven action
• Open standards and data interoperability empower vendor-neutral, future-proof architectures
• Cost and sovereignty controls are essential for global, compliant scale
• Modular and “Shift Left” approaches align observability with modern DevOps pipelines

The organizations that adapt to these trends will see improved uptime, be able to make better informed decisions, and use their observability data for a wide range of use cases while reducing their observability bills..

Observability is changing rapidly , and to achieve true operational excellence that scales, these trends cannot be ignored.