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Cachet vs Prometheus: What are the differences?
Introduction: Cachet and Prometheus are both popular monitoring tools used in the field of IT operations. However, despite serving similar purposes, they have key differences that set them apart from each other.
Data Collection Methodology: Cachet primarily focuses on providing status updates and incident management for services, while Prometheus is designed for metrics collection, storage, and query. Cachet is more service-oriented, while Prometheus is more metric-focused.
Alerting Capabilities: Prometheus has a robust alerting system that allows users to set up and manage alerts based on specific metrics and thresholds. On the other hand, Cachet lacks built-in alerting capabilities and relies on integration with other tools for this functionality.
Scalability and Performance: While both tools are scalable, Prometheus is known for its ability to handle large amounts of data and perform efficiently in high-traffic environments. Cachet, on the other hand, may face performance issues with excessive data loads.
Data Visualization and Dashboarding: Prometheus provides powerful visualization and dashboarding capabilities through tools like Grafana, making it easy for users to create custom dashboards. Cachet, on the other hand, has more limited options for data visualization and dashboarding.
Support for Monitoring Types: Cachet is primarily geared towards service monitoring and incident management, while Prometheus is more versatile in terms of the types of systems and services it can monitor. Prometheus supports a wide range of monitoring types beyond just services.
Community and Ecosystem: Prometheus has a larger and more active community, leading to a wide range of integrations, plugins, and resources available for users. Cachet, while still having a supportive community, may have a more limited ecosystem compared to Prometheus.
In Summary, Cachet and Prometheus differ in data collection methodology, alerting capabilities, scalability, data visualization, support for monitoring types, and community engagement.
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.
You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.
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.
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.
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 (https://www.instana.com/supported-technologies/pivotal-cloud-foundry/). Let me know if you have further questions! Regards Jose
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.
this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/
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.
Pros of Cachet
- Open Source18
- Looks beautiful7
- RESTful API7
- Free to use5
- Scheduled maintenance5
- Easy and efficient3
- Easy to use dashboard3
Pros of Prometheus
- Powerful easy to use monitoring47
- Flexible query language38
- Dimensional data model32
- Alerts27
- Active and responsive community23
- Extensive integrations22
- Easy to setup19
- Beautiful Model and Query language12
- Easy to extend7
- Nice6
- Written in Go3
- Good for experimentation2
- Easy for monitoring1
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Cons of Cachet
Cons of Prometheus
- Just for metrics12
- Bad UI6
- Needs monitoring to access metrics endpoints6
- Not easy to configure and use4
- Supports only active agents3
- Written in Go2
- TLS is quite difficult to understand2
- Requires multiple applications and tools2
- Single point of failure1