Open Source Observability Tools

What is Observability

When something breaks in a modern cloud system, the hardest part is often not fixing the problem, but understanding what actually went wrong.

Observability in modern IT and cloud environments means understanding how a system behaves by using the data it continuously produces: logs, metrics, and traces. Metrics show system performance and availability, logs record events, and traces show how requests move across services. In today’s cloud-native and distributed systems, problems are often complex and involve many components. 

Because of this, observability is essential. It helps teams quickly find the root cause of issues, reduce downtime, and improve reliability. Observability is not optional; it is a core requirement for running modern, scalable, and reliable systems.

The Cost Advantage of Open Source Observability

In practice, open-source observability tools are very cost-efficient because the main costs come from storage, compute, and data transfer. For example, storing about 1 TB of logs, metrics, and traces per month in object storage may cost around $20–30, while running the platform on a few Kubernetes nodes may add $50–100 per month. 

Unlike enterprise tools, there are no license, host-based, or ingestion-based fees, which makes open-source observability much more predictable and affordable at scale.

Metrics

Metrics are numeric values that show how a system behaves over time. Examples include request count, error rate, response time, and resource usage. These values help teams assess a system's overall performance and stability. Monitoring collects and displays metrics to show what is happening in the system. To handle these metrics efficiently, Prometheus, VictoriaMetrics, Mimir, and Thanos are commonly used.

Prometheus is the most common choice for collecting metrics and works very well for small to medium environments. For larger or multi-cluster setups, tools like Thanos and Grafana Mimir add long-term storage, high availability, and global querying. VictoriaMetrics is a strong alternative that offers better performance and lower resource usage, especially at high metric volumes.

Prometheus Docs

Prometheus is an open-source monitoring tool used to collect and process time-series metrics. It gathers numeric data together with labels and timestamps, then stores them for querying and analysis. Prometheus collects metrics by scraping HTTP endpoints that expose metrics data. These endpoints, called targets, can be infrastructure platforms such as Kubernetes, applications, or services like databases. It supports metric collection, querying, and alerting, and is commonly used together with Alertmanager to send and manage alerts. In Kubernetes environments, Prometheus is often set up with the kube-prometheus-stack to simplify installation and configuration.

Thanos Doc

Thanos is an open-source system that extends Prometheus to support long-term storage, high availability, and global querying. It is designed to work together with Prometheus, not replace it. Thanos stores Prometheus metrics in object storage, such as S3-compatible systems, allowing them to be retained for a long time. In Kubernetes environments, Thanos is commonly deployed to improve Prometheus scalability and reliability, especially in large or multi-cluster setups. It helps centralize metrics, reduce data loss, and support long-term analysis and alerting.

VictoriaMetrics Docs

VictoriaMetrics is an open-source, fast, and scalable time-series database and monitoring solution. It is designed to help build monitoring platforms that can handle large amounts of data without scalability issues. In Kubernetes environments, VictoriaMetrics is commonly deployed using the VictoriaMetrics Kubernetes Stack, which provides a complete, scalable solution for metric collection, storage, and alerting.

Grafana Mimir Docs

Grafana Mimir is an open-source, scalable, and highly available time-series database used to store and query Prometheus metrics at large scale. It is designed to run across multiple nodes and handle high-volume metrics reliably. Mimir extends Prometheus by providing long-term storage, faster and more efficient queries, and multi-tenancy support. In Kubernetes environments, Grafana Mimir can be deployed using Helm charts, which makes installation and configuration easier and more consistent.

Logs

Logs are text records that describe events happening inside a system. They usually include details such as timestamps, log levels, and messages generated by applications or infrastructure components. Logging collects and stores these records so they can be searched and analyzed. To manage and analyze logs efficiently at scale, tools such as Grafana Loki, Elasticsearch, VictoriaLogs, and OpenSearch are commonly used.

Grafana Loki focuses on simplicity and low cost by indexing only labels rather than full log content, making it very efficient for cloud-native environments. Elasticsearch and OpenSearch provide powerful full-text search and analytics features, but require more resources and operational effort. VictoriaLogs offers a lightweight, fast option with much lower CPU, memory, and disk usage than traditional log platforms.

Grafana Loki Doc

Grafana Loki is a log aggregation system used to collect, store, and query logs. It is designed to be simple and cost-efficient. Loki stores logs in object storage and uses labels to organize and filter log data. Loki works closely with Grafana, which makes it easy to explore and visualize logs. It is built to scale well in cloud-native and multi-tenant environments, making log management easier for monitoring applications and systems.

Loki Deployment Modes

Monolithic mode: Monolithic mode is useful for getting started quickly to experiment with Loki, as well as for small read/write volumes of up to approximately 20GB per day.

Simple Scalable: Loki’s simple scalable deployment mode separates execution paths into read, write, and backend targets. The simple, scalable deployment mode can scale close to a TB of logs per day.

Microservices mode: The microservices deployment mode runs components of Loki as distinct processes. Microservices mode deployments can be more efficient for Loki installations. However, they are also the most complex to set up and maintain.

In Kubernetes environments, Loki can be easily deployed using the Loki Helm chart.

ElasticSearch Doc

Elasticsearch is an open-source and distributed search and analytics engine built on Apache Lucene. It is written in Java and is used to store, search, and analyze large amounts of data quickly, often in near real time. At a basic level, Elasticsearch works as a server that receives JSON requests and returns JSON responses, making it suitable for log analysis, search use cases, and observability platforms.

What is Elastic Stack (Formerly ELK Stack)?

Elasticsearch is the central component of the Elastic Stack, a set of open-source tools for data ingestion, enrichment, storage, analysis, and visualization. It is commonly referred to as the “ELK” stack after its components, Elasticsearch, Logstash, and Kibana, and now also includes Beats. Although a search engine at its core, users began using Elasticsearch for log data and wanted a way to easily ingest and visualize it.

VictoriaLogs Doc

VictoriaLogs is an open-source and user-friendly log database developed by the VictoriaMetrics team. It is designed to efficiently store, search, and analyze logs with minimal resource usage. It uses significantly less CPU, memory, and disk space compared to traditional log systems such as Elasticsearch and Grafana Loki. In Kubernetes environments, VictoriaLogs is commonly installed using Helm charts, which simplify deployment and management.VictoriaLogs can be deployed in single-node or cluster modes.

OpenSearch Doc

OpenSearch is an open-source and distributed search and analytics engine. It is developed as a community-driven project and is used to store, search, and analyze large amounts of data at scale. It also includes OpenSearch Dashboards, a built-in visualization tool that helps users view, explore, and analyze data in real time through dashboards and charts.

What Is the Difference Between OpenSearch and Elasticsearch?

Elasticsearch is a popular search and analytics engine developed by Elastic NV. It was originally released as open-source software under the Apache 2.0 license. However, in early 2021, Elastic NV changed its licensing model and stopped releasing new versions under the Apache 2.0 license. Because of this change, Amazon Web Services (AWS) and several other companies decided to create a fork of the latest Apache 2.0-licensed Elasticsearch code. This fork, named OpenSearch, is fully open source, community-driven, and released under the Apache 2.0 license.

Traces

Traces show how a request flows through a system from start to end. They record each step of a request as it moves across services, including timing information and component dependencies. Traces help explain how different services interact and where latency or failures occur. To collect, store, and analyze traces efficiently at scale, tools such as Grafana Tempo and VictoriaTraces are commonly used.

Grafana Tempo is designed to be simple and cost-effective by storing traces only in object storage and relying on Grafana for visualization. It scales very well and fits naturally into Kubernetes and cloud-native environments. VictoriaTraces provides a more resource-efficient tracing backend and supports both single-node and clustered deployments, making it suitable for teams looking for lower operational overhead.

Grafana Tempo Doc

Grafana Tempo is an open-source and scalable distributed tracing backend designed to be simple and cost-effective. It works with object storage only, which helps reduce operational cost and system complexity. Grafana Tempo makes it easier to follow request flows, find performance bottlenecks, and troubleshoot problems in complex microservice architectures.

Tempo Deployments Mode

Tempo can be deployed in either monolithic or microservice mode.

Monolithic mode: Monolithic mode uses a single Tempo binary that is executed, which runs all of the separate components within a single running process. This means that a single instance both ingests, stores, compacts, and queries trace data.

Microservices mode: In microservices mode, components are deployed in distinct processes. Scaling is per component, allowing greater flexibility and more granular failure domains. This is the preferred method for a production deployment, but it’s also the most complex.

In Kubernetes environments, Grafana Tempo can be deployed using Helm charts to simplify installation and configuration.

VictoriaTrace Doc

VictoriaTraces is an open-source distributed tracing database designed to efficiently store and query trace data. It is built to be fast, resource-efficient, and easy to operate in cloud-native and Kubernetes environments. VictoriaTraces uses fewer CPU and memory resources compared to many other tracing solutions and scales linearly as resources increase. It can run as a single instance or scale horizontally in cluster mode for larger workloads. VictoriaTraces also supports alerting based on trace data, helping teams detect performance issues and failures in distributed systems more effectively.

In Kubernetes environments, VictoriaTraces can be easily deployed using Helm charts for installation and configuration.

This article introduced the core concepts of open-source observability and the main tools used for metrics, logs, and traces in modern cloud-native systems.

In the next article of this series, we will move one step closer to real-world implementations by focusing on agent and collector architectures. We will explore how logs, metrics, and traces are collected and forwarded in practice, and take a deeper dive into tools such as Filebeat, Fluent Bit, Fluentd, OpenTelemetry Collector, and Grafana Alloy.

This will help clarify how these tools work together in production environments and how to choose the right components for scalable and cost-efficient observability platforms.