Prometheus vs Stackdriver

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Prometheus vs Stackdriver: What are the differences?

Prometheus vs Stackdriver

Prometheus and Stackdriver are two popular monitoring and observability tools used to gain insights into the performance and health of applications and infrastructure. While they share some similarities, there are several key differences between the two.

  1. Data Collection: Prometheus operates on a pull-based model where it scrapes metrics from various targets, such as applications and servers, at regular intervals. On the other hand, Stackdriver follows a push-based model where applications and services send their metrics to Stackdriver for processing.

  2. Data Storage: Prometheus stores data in a time-series database, allowing for efficient querying and analysis. It provides a rich query language and retains data for a configurable period. In contrast, Stackdriver relies on Google Cloud storage and BigQuery for the storage and analysis of its data, making it more suitable for users already operating within the Google Cloud ecosystem.

  3. Alerting and Notifications: Prometheus offers a flexible and powerful alerting system that allows users to define custom alerting rules and send notifications based on metric thresholds. Stackdriver also provides alerting capabilities but integrates more seamlessly with other Google Cloud services, such as Cloud Monitoring and Cloud Pub/Sub, for advanced alerting and notification mechanisms.

  4. Integration and Ecosystem: Prometheus has a thriving open-source community, which has led to the creation of numerous integrations and exporters supporting various technologies and frameworks. Stackdriver, being a Google Cloud product, offers seamless integration with other Google Cloud services, including log management, tracing, and error reporting, providing a unified observability experience within the Google Cloud Platform.

  5. Pricing: Prometheus is an open-source project, making it free to use and deploy. Users have full control over their infrastructure and incur only the costs associated with running and managing their Prometheus servers. Stackdriver, on the other hand, offers different pricing tiers based on the features and usage, with some basic functionality being available for free. Additional costs may be incurred for advanced features, increased storage, and usage beyond the free tier.

  6. Scalability and Performance: Prometheus is known for its scalability, allowing users to scale horizontally by adding multiple instances. It can handle vast amounts of data and high ingestion rates efficiently. Stackdriver also offers scalability, leveraging the Google Cloud infrastructure, allowing users to handle large volumes of data. Stackdriver benefits from Google's extensive network and capacity for processing vast amounts of data in real-time.

In summary, the key differences between Prometheus and Stackdriver lie in their data collection models, storage mechanisms, alerting capabilities, integrations, pricing, and scalability. Prometheus provides users with greater flexibility and control, while Stackdriver offers tight integration with the Google Cloud Platform and advanced features suited for users already operating within that ecosystem.

Advice on Prometheus and Stackdriver
Susmita Meher
Senior SRE at African Bank · | 4 upvotes · 785.7K views
Needs advice
on
GrafanaGrafanaGraphiteGraphite
and
PrometheusPrometheus

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 · 571K views
Recommends
on
GrafanaGrafanaPrometheusPrometheus

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.

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Sunil Chaudhari
Needs advice
on
MetricbeatMetricbeat
and
PrometheusPrometheus

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
Recommends
on
PrometheusPrometheus

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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Recommends
on
InstanaInstana

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

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Mat Jovanovic
Head of Cloud at Mats Cloud · | 3 upvotes · 714.9K views
Needs advice
on
DatadogDatadogGrafanaGrafana
and
PrometheusPrometheus

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
Recommends
on
InstanaInstana

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/

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Recommends
on
DatadogDatadog

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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Pros of Prometheus
Pros of Stackdriver
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 22
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring
  • 19
    Monitoring
  • 11
    Logging
  • 8
    Alerting
  • 7
    Tracing
  • 6
    Uptime Monitoring
  • 5
    Error Reporting
  • 4
    Multi-cloud
  • 3
    Production debugger
  • 2
    Many integrations
  • 1
    Backed by Google
  • 1
    Configured basically with GAE

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Cons of Prometheus
Cons of Stackdriver
  • 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
  • 2
    Not free

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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 Stackdriver?

Google Stackdriver provides powerful monitoring, logging, and diagnostics. It equips you with insight into the health, performance, and availability of cloud-powered applications, enabling you to find and fix issues faster.

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What are some alternatives to Prometheus and Stackdriver?
Datadog
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
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
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.
Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
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