Prometheus vs Thanos: Key Differences & Best Practices

When it comes to monitoring cloud-native applications, Prometheus is one of the go-to tools. It's powerful, open-source, and widely used for collecting and querying time-series data.

However, as your system grows and your metrics scale, Prometheus starts to show some limitations. That’s where Thanos comes in. So, how do Prometheus and Thanos compare, and why should you consider using them together? Let’s break it down.

What is Prometheus?

Prometheus is an open-source time-series database (TSDB) designed for monitoring and alerting in cloud-native environments. It collects metrics from various endpoints via its powerful query language, PromQL, and stores them in a time-series format.

Prometheus offers excellent integration with Kubernetes and is often deployed using the Prometheus Operator to manage Prometheus instances and configurations.

However, Prometheus' default setup has its challenges, especially when you're dealing with large-scale deployments or need highly available Prometheus setups. That’s where Thanos steps in.

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What is Thanos?

Thanos is an open-source project that extends Prometheus' functionality to help overcome its limitations, particularly around long-term storage, scalability, and high availability. I

Integrating with Prometheus, Thanos adds a set of components that allow you to store and query historical metrics efficiently, even across multiple clusters or Prometheus deployments.

Thanos provides long-term storage capabilities by using object storage buckets (like AWS S3 or GCP) to keep metric data. Components like the Thanos Sidecar assist in replicating, deduplicating, and storing data in object stores.

The Thanos Compactor optimizes storage and retention policies by compacting older data, while the Thanos Querier enables global querying across multiple Prometheus instances.

Prometheus vs Thanos: A Comparison

Here’s a quick comparison between Prometheus and Thanos, highlighting their core features and use cases:

Prometheus Components

Prometheus has several key components that make it a powerful monitoring solution:

1. Prometheus Server

The heart of Prometheus, responsible for scraping metrics from configured endpoints and storing them in its time-series database.

2. PromQL

The query language used to extract and analyze time-series data, enabling powerful and flexible queries.

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3. Prometheus Scraping

Prometheus collects metrics by scraping endpoints at defined intervals, configured via a YAML file.

4. Alertmanager

Handles alerts triggered by Prometheus, managing routing, grouping, and de-duplication, sending notifications to external systems like Slack or email.

5. Exporters

Software components that expose metrics from third-party services (e.g., databases, hardware), so Prometheus can scrape them.

6. Pushgateway

Used when services can’t be scraped directly by Prometheus, allowing them to push metrics to Prometheus via a central gateway.

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7. Prometheus Operator

A Kubernetes-native tool for automating the deployment and management of Prometheus and Alertmanager instances within Kubernetes environments.

8. Prometheus Storage

The internal time-series database (TSDB) used to store scraped metrics, designed for efficient reads and writes but not long-term storage.

Why Use Thanos with Prometheus?

While Prometheus excels at collecting and querying real-time metrics, there are several reasons why Thanos is an excellent complement:

1. Scalability

Prometheus can be scaled horizontally by running multiple instances, but when you need to aggregate data from different Prometheus instances, it becomes challenging.

Thanos solves this by allowing you to query multiple Prometheus servers globally. The Thanos Query component provides a global query view for all your Prometheus instances, making it easier to scale across larger infrastructures.

2. High Availability

Prometheus by itself doesn’t have built-in support for high availability. If your Prometheus instance fails, you may lose critical metrics.

Thanos solves this by ensuring that data is stored redundantly, using the Thanos Sidecar to sync data to object storage, which provides highly available Prometheus setups.

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3. Long-Term Storage

Prometheus is great for short-term data retention, but when you need to store metrics for longer periods, Thanos shines.

Thanos allows you to store historical data in cloud storage, preventing local storage from becoming overwhelmed. This approach enables long-term data retention without sacrificing performance or scalability.

This is especially helpful for DevOps teams that need to retain data over long periods for analysis and compliance.

4. Downsampling & Deduplication

Thanos supports downsampling, which reduces the granularity of older data to save on storage space while still retaining useful insights.

Additionally, Thanos handles deduplication by ensuring that you don't end up with redundant metrics when multiple Prometheus instances are running.

5. Prometheus API & Store Gateway

Thanos extends Prometheus' API and provides a store gateway that connects Prometheus with remote object storage, allowing for efficient queries and retrieval of metric data.

This feature makes it easier to integrate Prometheus and Thanos into your existing monitoring system.

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Thanos Components Overview

Thanos consists of several components that help extend Prometheus' functionality.

Here’s a quick look at each one:

Thanos Sidecar

A companion component to Prometheus that handles uploading metrics to object storage and allows Prometheus to integrate seamlessly with Thanos.

Thanos Querier

The component that allows you to query data from multiple Prometheus instances globally.

Thanos Store

This component is responsible for reading and storing data from object storage.

Thanos Compactor

Optimizes data storage by downsampling and compacting old data.

Thanos Store Gateway

Connects with object storage to serve historical metric data.

Thanos Frontend

A component that allows for efficient query processing, improving the performance of large-scale queries.

How to Migrate from Prometheus to Thanos

Migrating from Prometheus to Thanos is relatively straightforward. You can deploy Thanos alongside Prometheus by adding the Thanos Sidecar to your existing Prometheus deployment.

The Sidecar will push your data to object storage and enable remote write functionality. You’ll also want to use Prometheus HA for high availability and ensure that your configuration files (YAML) are updated to reflect Thanos components.

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Best Practices for Using Thanos with Prometheus

Use Object Storage

Choose a reliable object storage bucket (like AWS S3 or GCP buckets) for your Thanos setup to ensure scalability and reliability.

Optimize Compaction

Make use of the Thanos Compactor to manage data retention policies and reduce storage costs.

Monitor Latency

Keep an eye on the latency of global queries. Thanos helps minimize this, but it's still important to fine-tune your setup.

Deploy with Helm

Using Helm for Kubernetes deployments simplifies the installation and configuration of both Prometheus and Thanos components.

Conclusion

Prometheus and Thanos each play a crucial role in modern observability. Prometheus is perfect for real-time monitoring, providing quick insights into system performance.

Thanos, on the other hand, complements Prometheus by offering long-term storage, scalability, and high availability — ensuring you can manage large volumes of data seamlessly.

At Last9, we’re committed to helping you optimize your systems. We can reduce your total cost of ownership (TCO) by about 50%. If this sounds interesting, reach out to us — we’d love to chat!

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FAQs

What is Thanos for Prometheus?

Thanos is an open-source tool that extends Prometheus by adding features like long-term storage, high availability, and global querying. It allows Prometheus to scale and provide better performance across large infrastructures.

What is the difference between Prometheus, Thanos, and Cortex?

Prometheus focuses on short-term data collection, while Thanos and Cortex provide scalability and long-term storage for Prometheus data. Thanos uses object storage for data retention, while Cortex uses a different approach for scaling.

How do I migrate from Prometheus to Thanos?

To migrate, deploy Thanos alongside Prometheus by adding the Thanos Sidecar and configuring remote write to upload your metrics to object storage. Use Prometheus HA to ensure high availability across your setup.

How many metrics can Prometheus handle?

Prometheus can handle millions of time-series metrics depending on the resources available. Scaling can be achieved by running multiple Prometheus servers or using Thanos for aggregation.

What is Prometheus?

Prometheus is an open-source monitoring and alerting system that collects time-series metrics, which can be queried using PromQL. It is commonly used in Kubernetes environments and integrates with tools like Grafana for creating real-time dashboards.

What if I have more than one instance of Prometheus running?

If you have multiple instances, Thanos allows you to aggregate metrics and query them globally using the Thanos Querier.

How is Prometheus different than other monitoring tools?

Prometheus focuses specifically on time-series data and integrates well with Kubernetes. Its Prometheus operator simplifies deployment, and its powerful query language, PromQL, allows for detailed metric analysis.