Cloud Observability Explained: From Three Pillars to AI-Powered Ops

Modern cloud environments are incredibly complex, with microservices communicating across dozens of APIs, containers spinning up and disappearing within seconds, and individual requests traversing multiple systems. While this speed and scale are powerful, they make diagnosing issues far more difficult — traditional monitoring tools were not designed for this level of complexity.

This challenge calls for cloud observability, which is the ability to understand the internal state of cloud systems from their external outputs, commonly grouped into three pillars: logs, metrics and traces. Cloud observability enables teams to explore complex systems, identify unknown issues and proactively improve reliability. In this article, we explore the definition of cloud observability, its three core pillars, why it matters, how it is implemented in practice, and how AI is reshaping observability for modern systems.

Cloud observability is the practice of understanding the internal state of distributed systems built on cloud computing by analyzing the telemetry they produce: logs, metrics and traces. While traditional monitoring alerts you that something is wrong, observability helps you investigate and infer why it went wrong. This represents a shift from reactive alerting to proactively understanding system behavior.

Traditional IT monitoring struggles with highly dynamic, distributed systems and is better suited for less complex ones: a few servers, predictable traffic and commonly encountered failures that are easy to diagnose. But modern cloud architectures are built very differently. For example, a single ecommerce checkout could involve a number of microservices running independently: a frontend service, an API gateway, an authentication service, a payment processor, an inventory database, a recommendation engine, and a session cache. To add to the complexity, these different services could be running across multiple cloud providers, built on an infrastructure that scales up and down in real time.

Traditional monitoring tracks known failure modes — the metrics and thresholds you’ve set in advance to be alerted on. But observability allows you to go much farther and investigate failures you’ve never seen before (the unknown unknowns) and answer questions you didn’t think to ask. When an incident occurs in the middle of the night and the failure pattern doesn’t match anything you have documented, cloud observability is what allows your team to diagnose the problem quickly.

Imagine a scenario in which a latency spike hits your platform and users start complaining that checkout is slow. All that your monitoring tool can tell you is that response times are elevated. An observability platform, however, can trace that checkout request across the dozen services it touched. It enables you to identify that 11 of those services responded normally, while one — the inventory service — is taking 3.2 seconds due to an exhausted database connection pool. This is the difference that observability makes: not just detection but precise, actionable diagnosis.

Cloud-specific factors such as ephemeral containers, auto-scaling groups, serverless functions and multi-cloud deployments require observability. A traditional monitoring dashboard will struggle to capture infrastructure that lasts for 30 seconds and then disappears. Observability platforms are designed to capture that fast-moving data, correlate it across systems, and make it available for investigation later if needed.

For a deeper dive into how the two approaches differ, be sure to read our guide to observability vs. monitoring. For a broader perspective, see our guide to full-stack observability and how it extends visibility across entire systems.

A mature cloud observability strategy is built upon three foundational data types. Let’s explore what each one is and how they work together to make observability so powerful:

Logs are the most granular form of telemetry data — detailed, time-stamped records that capture exactly what happens within a system. Whenever an application processes a request, throws an exception, writes to a database or encounters an error, it can generate a log entry recording what happened, when and in what context.

For example, a log entry might read "Error 500: Database connection timeout at 13:29:08 UTC — connection pool exhausted after 30s." A developer can read that single line and understand what failed, when it failed and the probable cause for the failure. This level of detail is why debugging often starts with reviewing logs, and the detailed chronological record of events makes them particularly useful for post-incident analysis.

In modern cloud environments, however, large applications can produce billions of log entries daily. One way to manage this enormous volume is by using AI-driven log analysis.

Traces capture the end-to-end journey of a single request as it moves through a distributed system. While logs reveal what happened within a single service, traces show how a request travels across all of them — mapping each hop, service call and database query, including the time taken to complete every step.

For example, a user on your ecommerce site clicks “checkout,” but the process is experiencing a slowdown. A distributed trace follows that request from the API gateway (0.3s) to the payment service (0.8s) to the inventory service (3.1s), for a total of 4.2s. Tracing helps you pinpoint exactly where the bottleneck is and what needs to be fixed: the delay is occurring in the inventory service.

Traces are the most direct way to capture cause-and-effect relationships across service boundaries, which is where many production failures occur in microservices architectures.

Metrics are numerical measurements of system health, such as CPU utilization, memory usage, request latency, error rates and throughput. Unlike logs and traces, which capture specific events and individual requests, metrics show trends and patterns in how a system behaves over time.

Metrics are the foundation of dashboards and alerting. For example, if the average API response time increased from 120ms to 890ms over the past hour, that slowdown trend is visible in metrics before it becomes an outage and users are meaningfully impacted.

The three pillars form the foundation of observability, but their true value comes from your ability to connect them. Modern observability platforms add additional signals such as events, profiles and context graphs (service maps or dependency graphs), which help correlate logs, metrics and traces into a unified view of system behavior.

Context graphs are especially powerful because they don’t just show what individual services are doing — they show how they interact and influence one another. This is the layer where AI-driven analysis is particularly effective as it surfaces correlations that are hard for humans to detect manually.

Not all observability solutions deliver the same depth of insight or operational value. These are the key capabilities that a mature observability platform should offer:

An effective observability platform should ingest logs, metrics and traces across your cloud services without requiring complex agent management in many cases. Instead of needing to check five different dashboards for five different services, you should be able to see everything correlated in one place, with shared context (such as trace and span IDs) linking signals and making them easier to navigate through a centralized data catalog. This helps reduce the constant context switching that can become a drag on productivity and slow down troubleshooting.

End-to-end distributed tracing is necessary in microservices environments. It allows you to follow a request from the user’s browser through your API gateway, through your auth service, into the database, and back out again — effectively providing a form of data lineage for runtime system behavior. That level of visibility is what turns a vague “it’s slow” complaint into a precise, actionable diagnosis. With a well-designed tracing layer, you can identify that a single database query is responsible for 92% of total request latency and pinpoint the exact query at fault.

Instead of requiring a human engineer to manually piece together logs, metrics and traces across a dozen services, AI-powered root cause analysis can automate much of this process. The system can flag anomalies using techniques such as predictive analytics, connect them to related signals (for example, a recent deployment, configuration changes or a dependency that has started behaving differently) and surface likely root causes along with suggested fixes.

Imagine a scenario in which an AI observability agent detects a latency spike, correlates it with a recent deployment and then identifies a misconfigured connection pool as the root cause — all within minutes.

AI site reliability engineering (SRE) agents push this even further by not only diagnosing issues but in some cases helping resolve issues autonomously — often significantly faster than is possible with manual investigation.

One important capability of modern observability platforms is the ability to store high-fidelity or even 100% of telemetry data rather than retaining just a sampled subset, which many organizations are forced to do because of cost concerns. The problem is that the data you discard today could be the exact data you need to diagnose an incident a week from now.

New platforms built on scalable object storage, and leveraging Apache Iceberg table format for managing large-scale telemetry data, are now making full or near-complete telemetry retention increasingly viable. When you have the ability to query your entire telemetry history with far fewer gaps, your post-incident analysis becomes faster, more complete and more likely to uncover the true root cause.

OpenTelemetry (OTel) is the Cloud Native Computing Foundation (CNCF) standard for telemetry collection and export, and has emerged as a widely adopted standard for organizations that seek to avoid vendor lock-in. You can instrument your applications once with OTel and then route that telemetry to any compatible backend — a commercial observability platform or even a custom data warehouse.

Combined with open storage technologies such as Apache Iceberg, OTel becomes the foundation of an open, vendor-neutral observability architecture. Building on these open standards allows your organization to maintain the flexibility to adopt new best-in-class tools over time as the market evolves.

As organizations deploy AI agents, generative AI applications and LLM-powered systems at scale, it’s becoming clear that traditional observability frameworks weren’t designed for this new class of software. The chief reason is that AI systems introduce new challenges — non-deterministic outputs, multi-step reasoning and tool calls.

For example, you could give an LLM application two identical inputs but get back two very different outputs — one potentially excellent, the other problematic. Traditional application performance management (APM) tools can tell you if an AI system returned a response and how long it took. They cannot, however, assess whether that response was accurate, relevant or safe.

AI observability is an emerging extension of observability and MLOps designed to close this gap:

Effective cloud observability depends as much on process, discipline and strong data governance implementation as it does on tooling. The teams that get genuine value from observability tend to follow these eight best practices:

1. Begin with service-level objectives (SLOs), not infrastructure metrics: Define what “healthy” looks like from a user perspective — latency thresholds, availability targets, error rate budgets — and instrument your systems accordingly. Infrastructure metrics are secondary; user experience is what really matters.

2. Instrument with OpenTelemetry early on: It’s easier to avoid vendor lock-in from the start rather than unwinding proprietary tooling later on. OTel is the industry standard and offers a mature and well-supported ecosystem.

3. Centralize or unify access to telemetry data: Splitting logs, metrics and traces across tools makes correlation difficult and can undermine data integrity, especially when signals become inconsistent or incomplete across systems. A unified telemetry layer is the foundation of meaningful observability. Ensure telemetry is correlated (e.g., via trace and span IDs) so teams can move seamlessly from symptoms to root cause.

4. Use AI-powered analysis to cut through the noise: No human team can correlate petabytes of telemetry data. AI-powered anomaly detection and root cause analysis are often the only practical way to operate at scale.

5. Retain as much telemetry data as is practical: Sampling can introduce blind spots, and the data you delete today may be critical in diagnosing a future incident. Modern platforms increasingly make full data retention economically viable.

6. Automate alerts based on service impact, not resource thresholds: An alert triggered when CPU hits 80% is usually noise, but those tied to SLO violations offer actionable insight. Reduce alert fatigue by focusing on outcomes, not symptoms.

7. Integrate observability into your CI/CD pipeline: Catching observability gaps — such as missing instrumentation or incomplete traces — before deployment is much less costly than discovering them during a live incident.

8. Assign clear ownership: Every service should have a dedicated team responsible for its observability and data stewardship. Without explicit ownership, shared responsibility tends to devolve into no responsibility.

Snowflake’s approach to observability is built around a premise that’s different from most other solutions. Instead of layering an observability tool onto a data platform, or bolting a data platform onto an observability tool, Snowflake treats observability as native to the platform itself. This provides a unified architecture in which the same system running your data and AI workloads is designed to deliver end-to-end visibility into how they behave.

This approach aligns with a broader industry shift. As SanjMo analyst Sanjeev Mohan notes, “the lines between data platforms and observability platforms are blurring.”

Snowflake Trail delivers built-in telemetry across pipelines, applications and compute — capturing metrics, logs and span events through a simple configuration. This can reduce the need for separate agent deployment and additional infrastructure. Teams can begin generating telemetry from their Snowflake workloads within minutes.

Jason Freeberg, Product Manager at Snowflake, walks through the new Snowflake Trail observability features for Snowpark.

For AI-specific observability, Snowflake provides native model performance evaluation and AI quality monitoring directly within the platform — allowing teams to track LLM output quality, hallucination rates and model drift alongside their broader telemetry signals.

On the AI SRE side, Snowflake’s acquisition of Observe adds AI-driven root cause analysis powered by context graphs — technology that correlates signals across logs, metrics and traces to automatically identify the precise source of an incident, in some cases resolving production issues significantly faster (in some cases up to 10x) than manual investigation.

Snowflake’s architecture is built on open standards throughout, including OpenTelemetry for data collection and Apache Iceberg for storage. This means that teams can integrate their existing tools without worrying about vendor lock-in. The data lives in Snowflake’s scalable object storage, making full telemetry retention feasible without the cost penalties that force many organizations to sample.

All of this offers data platform-native observability — a single environment to run your workloads, observe their behavior, apply AI-driven analysis and act on insights.