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Prometheus vs Sensu: What are the differences?

Key Differences Between Prometheus and Sensu


In the world of monitoring and observability, Prometheus and Sensu are two widely used tools that serve different purposes and offer distinct features. While both tools provide solutions for monitoring systems, applications, and infrastructure, there are several key differences that set them apart from each other.

1. Data Model and Storage:

Prometheus has a highly specific data model based on a time-series database. It collects metrics in a pull-based manner, where the Prometheus server scrapes metrics from the configured endpoints. It stores the data in its own storage engine, allowing for efficient querying using its PromQL language. On the other hand, Sensu does not have its own storage backend. Instead, it relies on external time-series databases, such as InfluxDB or Graphite, to store the collected metrics. This allows Sensu to be more flexible and integrate with different storage solutions.

2. Alerting and Notification:

Prometheus has built-in support for alerting and notification. It allows you to define and configure alerting rules based on metrics and send notifications via various channels like email, Slack, PagerDuty, etc. It also provides a powerful alert manager component for managing, grouping, silencing, and deduplicating alerts. In contrast, Sensu does not have a built-in alerting and notification system. It is designed to work with external systems, such as PagerDuty or OpsGenie, for alerting and notification purposes. Sensu focuses more on monitoring and leaves the alerting part to external tools.

3. Scalability and Federation:

Prometheus is built with scalability in mind. It allows you to set up a federated architecture, where multiple Prometheus servers can be deployed and data can be aggregated and queried from a central Prometheus instance. This enables horizontal scalability and distributed monitoring setup. Sensu, on the other hand, does not provide a native federated architecture. However, it can be integrated with other monitoring tools like Nagios or Icinga for distributed monitoring, allowing for a similar level of scalability.

4. Use Case and Philosophy:

Prometheus is primarily designed for monitoring containers and microservices architectures. It provides excellent support for monitoring dynamic environments, auto-discovery of services, and has strong support for Kubernetes. Prometheus follows a pull-based model to collect data, which allows it to be well-suited for cloud-native applications. Sensu, on the other hand, is a more general-purpose monitoring tool that can be used for monitoring various types of systems and infrastructures. It supports both push and pull-based models but leans towards a push-based model in its architecture.

5. Ecosystem and Integrations:

Prometheus has a rich ecosystem and extensive community support. It offers various libraries, exporters, and integrations with other tools, making it easy to gather metrics from different sources. It integrates well with Grafana for visualization and has native support for Kubernetes metrics. Sensu also has a decent ecosystem with support for different plugins and integrations, but it may not be as extensive as Prometheus. Sensu can integrate with various monitoring and data processing tools like Elasticsearch, Logstash, Splunk, etc., to enhance its capabilities.

6. Ease of Setup and Configuration:

Prometheus aims to be a simple and easy-to-use monitoring solution. It provides a single binary deployment and has a relatively straightforward setup process. The configuration is done using YAML files, which are easy to understand and manage. Sensu, on the other hand, can be a bit more complex to set up and configure. It requires additional components like RabbitMQ or Redis for distributed message queuing and requires a more detailed configuration setup. Although it offers more flexibility, it might take more effort to get started with Sensu compared to Prometheus.

In Summary, Prometheus and Sensu differ in their data model and storage, alerting and notification capabilities, scalability and federation options, target use case and philosophy, ecosystem and integrations, and the ease of setup and configuration. Each tool has its strengths and weaknesses, and the choice between them depends on the specific requirements and preferences of the monitoring environment.

Advice on Prometheus and Sensu
Susmita Meher
Senior SRE at African Bank · | 4 upvotes · 801.8K views
Needs advice

Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:

  • Must be able to get custom data from AS400,
  • Able to display automation test results,
  • System monitoring / Nginx API,
  • Able to get data from 3rd parties DB.

Grafana is almost solving all the problems, except AS400 and no database to get automation test results.

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Replies (1)
Sakti Behera
Technical Specialist, Software Engineering at AT&T · | 3 upvotes · 587.1K views

You can look out for Prometheus Instrumentation ( Client Library available in various languages to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.

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Sunil Chaudhari
Needs advice

Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.

Is it OK to use Metricbeat for Linux server or can we use Prometheus?

What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?

Regards, Sunil.

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Replies (2)
Matthew Rothstein

If you're already using Prometheus for your system metrics, then it seems like standing up Elasticsearch just for Linux host monitoring is excessive. The node_exporter is probably sufficient if you'e looking for standard system metrics.

Another thing to consider is that Metricbeat / ELK use a push model for metrics delivery, whereas Prometheus pulls metrics from each node it is monitoring. Depending on how you manage your network security, opting for one solution over two may make things simpler.

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Hi Sunil! Unfortunately, I don´t have much experience with Metricbeat so I can´t advise on the diffs with Prometheus...for Linux server, I encourage you to use Prometheus node exporter and for PCF, I would recommend using the instana tile ( Let me know if you have further questions! Regards Jose

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Mat Jovanovic
Head of Cloud at Mats Cloud · | 3 upvotes · 730.5K views
Needs advice

We're looking for a Monitoring and Logging tool. It has to support AWS (mostly 100% serverless, Lambdas, SNS, SQS, API GW, CloudFront, Autora, etc.), as well as Azure and GCP (for now mostly used as pure IaaS, with a lot of cognitive services, and mostly managed DB). Hopefully, something not as expensive as Datadog or New relic, as our SRE team could support the tool inhouse. At the moment, we primarily use CloudWatch for AWS and Pandora for most on-prem.

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Replies (2)
Lucas Rincon

this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here

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I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.

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Decisions about Prometheus and Sensu
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 16 upvotes · 999.4K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

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Leonardo Henrique da Paixão
Junior QA Tester at SolarMarket · | 15 upvotes · 364.5K views

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

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Pros of Prometheus
Pros of Sensu
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
  • 23
    Active and responsive community
  • 22
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring
  • 13
    Support for almost anything
  • 11
    Easy setup
  • 9
    Message routing
  • 7
    Devs can code their own checks
  • 5
    Ease of use
  • 4
  • 3
    Nagios plugin compatibility
  • 3
    Easy configuration, scales well and performance is good
  • 1
    Written in Go

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Cons of Prometheus
Cons of Sensu
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
  • 2
    Written in Go
  • 2
    TLS is quite difficult to understand
  • 2
    Requires multiple applications and tools
  • 1
    Single point of failure
  • 1
  • 1
    Written in Go

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What is Prometheus?

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

What is Sensu?

Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.

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Dec 8 2020 at 5:50PM


May 21 2020 at 12:02AM

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Jun 26 2018 at 3:26AM

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What are some alternatives to Prometheus and Sensu?
Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.
New Relic
The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too.
InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
See all alternatives