Key Highlights
- Cloud-native observability consolidates metrics, logs, traces, and events into a single platform, facilitating faster troubleshooting and performance optimization.
- Open source tools like OpenTelemetry, Prometheus, and Jaeger provide flexible, vendor-agnostic solutions that help mitigate vendor lock-in and enhance security assessments.
- Unified SaaS platforms such as Datadog, Dynatrace, and New Relic offer comprehensive visibility across hybrid and multi-cloud environments, integrating diverse telemetry data into real-time dashboards.
- Operational complexity from multi-cloud deployments necessitates automation and AI-assisted observability to reduce manual overhead and address talent gaps.
- Effective implementation involves telemetry consolidation, stakeholder champions, and leveraging AI/ML for anomaly detection, ensuring scalable and reliable cloud operations.
In the last decade, cloud environments have become increasingly diversified and dynamic, with applications distributed across multiple platforms. Frequently, IT teams manage a mix of multi-cloud, hybrid and public cloud deployments. Together, these create unprecedented operational complexity.
Cloud-native observability makes it possible to not only simplify processes but also improve cloud performance using root-cause analysis and fast problem resolution. Once in place, C-suite and IT leaders gain greater cost control and a deeper understanding of cloud system behaviors and operations. IT teams can then use these proactive, full-stack approaches to scale workloads with confidence, ensure application and system reliability, and quickly roll out new services.
Over 90% of large enterprises have adopted a multi-cloud infrastructure. Moreover, legacy single-purpose tools are simply inefficient for conducting the required levels of cloud observability. To counter that deficit, today’s open-source observability tools and platforms gather real-time telemetry data to produce system snapshots that organizations can use to pinpoint anomalies and unusual behavior. Administrators and DevSecOps also use open source cloud observability to improve security and quickly assess cyberthreats based on clear context.
We explore the benefits of modern observability in terms of cost controls and the primary steps for implementation, as well as consider key tools and platforms.
Understanding observability: What it is and how it works
Cloud observability compiles all the vital signs of a high-functioning cloud environment in one place. It not only facilitates troubleshooting and helps optimize performance, but also ensures fast, proactive responses to security risks. In the current digital era, the massive ingestion of operational data in the form of metrics, events, logs and traces (MELT) has outpaced the capabilities of manual operations. It’s clear that siloed, single-use solutions that offer local debugging and tracing are no longer sufficient.
Moreover, dependence on a single cloud vendor’s proprietary tools can make it costly and time-consuming to switch providers. And while CSP telemetry capabilities provide ease of use, native integrations and minimal configuration setup, open source solutions and unified platforms offer diverse features to counter the effects of vendor lock-in. These open source solutions can enable a range of functions, from more complex data queries, customized visualizations, and broad security assessments to agentic AI that can automate observability and deliver new business insights.
“Ultimately, adoption of OpenTelemetry is growing dramatically,” says Andre Scott, developer advocate at Coralogix, the AI-driven observability platform and SaaS company. “Enterprises understand they need to get off these proprietary CSP formats and start owning their telemetry data, which is arguably the richest information about the business and which provides important business insights.”
One of the key goals of open source cloud observability is to improve end user experience by reducing application downtimes and latencies while eliminating internal management silos. Employing predefined dashboards, IT teams can easily access compiled telemetry data. This includes:
- Metrics that consist of numerical data for a host of functions, such as CPU speed, memory, storage and overall system performance
- Events that capture resource changes across cloud environments to indicate precisely where an anomaly occurred
- Logs that display the specific sequence of actions that led to a change in state or to system errors
- Traces, which capture the flow of requests across multiple systems and are used to pinpoint bottlenecks and optimize overall performance
The longer it takes for IT teams to resolve system issues, the higher the costs due to downtime and service interruptions. A proactive approach to accelerating mean-time-to-detect (MTTD) and the reduction of mean-time-to-restore (MTTR) will positively impact error budgets, ensure smooth service delivery, and avoid brand damage due to service interruptions.
Evaluating observability tools
Optimizing costs based on resource utilization insights remains an important driver for open source cloud observability adoptions. Increasingly, C-suite executives and IT leaders are recognizing the value of owning their telemetry data and preserving that information within efficient, cost-effective object storage.
Today, organizations frequently deploy multi-cloud and hybrid cloud instances while also managing on-premise deployments, such as Kubernetes environments. Proprietary CSP tools may not cover all use cases or offer as many levels of customization as open source solutions. Flexible open source alternatives not only mitigate vendor lock-in, but they also help optimize costs at scale as data volumes expand.
The choices for open source observability tools are expansive and varied. For example, OpenTelemetry, Grafana and Datadog have specific and often complementary roles in assembling data from the cloud. A few of the most important tools include:
- OpenTelemetry: This industry standard for instrumentation offers a framework and method to generate, collect and export telemetry data. It not only simplifies observability for complex, distributed systems, but it also provides a vendor-agnostic tool that integrates seamlessly with other open source observability solutions.
- Prometheus: IT teams employ open source Prometheus for metrics and data collection related to system health and overall performance. Administrators can use time-stamped data from application endpoints, servers and containers to gain specific insights on the status of storage, cloud components and services.
- Splunk: Administrators and IT teams use Splunk to index and analyze large volumes of log data from cloud-native environments and then correlate them with traces and metrics for real-time monitoring, troubleshooting and compliance.
- Jaeger: Jaeger provides end-to-end visibility into service requests as they travel across cloud and distributed architectures. Administrators and IT teams use this open source tracing tool in combination with the Grafana platform to visualize data and troubleshoot complex issues.
Cloud observability platforms
The role of SaaS-based, full-stack, unified platforms (e.g., Datadog, Dynatrace, New Relic, etc.) is to provide end-to-end visibility across hybrid/multi-cloud environments by integrating metrics, logs and traces into a single view.
- Grafana is widely considered the most popular open source platform for visualizing and analyzing telemetry data from various sources. The platform offers real-time dashboards that unify data from multiple, disparate sources into a single view of results.
- DataDog offers a software-as-a-service (SaaS) observability platform for full visibility into the health and performance of a hybrid or multi-cloud environment. Administrators can customize this data to gain insights into system behavior and correlate results with more than 900 vendor-backed technologies, all in a single view.
- Dynatrace offers a standalone, all-in-one platform for cloud observability. It can store all types of observability data in context and function as a single pane of glass for full-stack cloud observability while also offering automation choices and AI integration.
- New Relic offers a comprehensive, cloud-based observability platform designed for ingesting, visualizing and alerting on all MELT data. Administrators can use the New Relic platform as a single source of truth for engineering teams to monitor, debug and improve digital performance.
- Honeycomb provides a high-performance observability tool for engineering teams to tackle complex, cloud-native and distributed system issues, particularly favored by developers, SREs and DevOps professionals. It’s designed for developer-heavy and cloud-native organizations that require accelerated, advanced and actionable observability.
Key Adoption Challenges
For enterprise leaders, the trend toward multi-cloud deployments has increased operational complexity, making automation and AI-assisted observability essential. However, the trend also entails significant maintenance overhead that can require significant upskilling, while the current talent gap can present hurdles in terms of new hires.
For example, compared with the ease of CSP implementations, some open source observability solutions require on-site hosting, patching and securing, which demands specialized engineering resources and skill sets. However, aspects of agentic AI observability may help to ease that burden.
“That’s what is so important with OpenTelemetry and a unified platform because enterprises can have this agentic feature plugged right into the object storage to query data and deliver new business insights,” according to Scott.
On the other hand, a lack of IT management proficiency can impact cloud scalability and infrastructure reliability. That’s partly due to cloud-native data volumes, which continue to increase and can overwhelm open source observability solutions. Mismanagement can lead to performance bottlenecks, data gaps or query failures. Moreover, if an organization is not using a unified observability platform (e.g., Datadog, Dynatrace, New Relic, Grafana, etc), creating a holistic view that encompasses diverse open source tools can be time-consuming and lead to new data silos.
Steps To Implementation
Recognizing the synergy that exists between on-site IT operations, cloud deployments and business goals is a crucial prerequisite to successful observability adoption. A key goal is to ensure that the designated tool or platform offers seamless integration and aligns well with current IT skillsets, budget constraints and operational goals. And technology champions within an organization offer the best way to demonstrate the operational advantages of having the right toolset or platform.
“You need champions in different departments who not only can show the value of open source observability, but also demonstrate its effectiveness by starting slow and small, gradually bringing out the value,” Scott adds.
Another key step is telemetry consolidation, in which logs, metrics and traces are gathered into a centralized hub instead of simply existing as a collection of disparate sources. For example, this might consist of IT scenarios in which logs are scattered across multiple services, metrics are stored in Prometheus, and application performance monitoring (APM) resides in New Relic.
The result is frequent context switching and constant alert fatigue due to false positives. Instead, administrators should preconfigure alerts based on event criticality, predefined metrics benchmarks and automated context-aware alerts that ensure quick issue identification and resolution.
The evolution of cloud observability is partly based on the increased volume and complexity of datasets. Organizations can cope with the trend by leveraging machine learning (ML) algorithms and agentic AI to detect anomalies and predict potential issues.
Ultimately, deployment success depends on taking a thoughtful approach to observability adoption, comprehensive coverage and practical implementation. And in some cloud deployment scenarios, the best first step may not be unified platform adoption at all, but rather a rethinking of the pipeline layer responsible for delivering all the necessary contextual data.
About the Author
Kerry Doyle
Contributor
Kerry Doyle focuses primarily on issues relevant to both C-suite and enterprise leaders through technology articles, white papers and analyses. He covers a diverse range of topics, from nanotech to the cloud, open source to AI. Passionate about both the written word and communicating the value of technology, his experience stems from senior editorial positions at PCWeek, PCComputing, ZDNet, and CNet.com. He's a graduate of Boston University with a bachelor's degree in comparative literature.
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