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Kibana vs logz.io: What are the differences?
Introduction:
Kibana and logz.io are both popular tools used for analyzing and visualizing large volumes of data, particularly log data. While they both serve similar purposes, there are several key differences between the two.
Integration: Kibana is an open-source data visualization and exploration tool that works alongside Elasticsearch, a search and analytics engine. It is part of the Elastic Stack and is often used in conjunction with it. On the other hand, logz.io is a cloud-based log management platform that offers a comprehensive stack of tools, including log analytics, visualization, searching, monitoring, and more. It is built around the ELK (Elasticsearch, Logstash, and Kibana) stack but provides a fully managed and hosted solution.
Managed Service vs. Self-Hosted: Kibana is typically self-hosted, which means that organizations need to set up and manage their own infrastructure, including Elasticsearch and Kibana instances. This allows for greater customization and control but can require significant resources and expertise. In contrast, logz.io is a managed service, taking away the burden of infrastructure management, maintenance, and scalability from users. Organizations can simply send their logs to logz.io and leverage its fully managed and scalable platform.
Ease of Use: Kibana, being an open-source tool, requires some level of technical expertise to set up, configure, and use effectively. It offers a wide range of features and flexibility but may have a steeper learning curve for beginners. Logz.io, with its managed service approach, focuses on providing a user-friendly interface and ease of use. It simplifies the setup process and offers intuitive navigation and visualizations, making it more accessible to users with varying levels of technical expertise.
Security and Compliance: Kibana provides a range of security features, including role-based access control (RBAC), encryption, and authentication options. However, the responsibility of implementing and managing these security measures lies with the users. Logz.io, as a managed service, offers built-in security and compliance features, including encryption at rest and in transit, access control, and compliance with various industry standards such as SOC 2, GDPR, and ISO 27001. This makes it a suitable choice for organizations with strict security and compliance requirements.
Pricing Model: Kibana is an open-source tool, meaning it is available for free to download and use. However, organizations need to bear the costs associated with setting up and managing their own infrastructure. Logz.io, being a managed service, follows a subscription-based pricing model. The cost is based on factors such as data volume, retention, and specific features required. This provides organizations with predictable pricing and eliminates the need for upfront hardware or infrastructure investments.
Additional Features: While Kibana primarily focuses on data visualization and exploration, logz.io offers a more comprehensive set of features tailored specifically for log management. These additional features include log parsing, automated parsing updates, anomaly detection, machine learning-based insights, alerting, integrations with popular platforms such as Slack and PagerDuty, and more. These features enhance the log analysis capabilities of logz.io and provide a more holistic log management solution.
In summary, while Kibana is an open-source tool designed for data visualization and exploration, logz.io offers a fully managed log management platform with additional features, enhanced ease of use, security and compliance measures, and a subscription-based pricing model.
From a StackShare Community member: “We need better analytics & insights into our Elasticsearch cluster. Grafana, which ships with advanced support for Elasticsearch, looks great but isn’t officially supported/endorsed by Elastic. Kibana, on the other hand, is made and supported by Elastic. I’m wondering what people suggest in this situation."
For our Predictive Analytics platform, we have used both Grafana and Kibana
- Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
- Kibana based reporting screenshot: https://imgur.com/vuVvZKN
Kibana has predictions
and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).
For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:
- Creating and organizing visualization panels
- Templating the panels on dashboards for repetetive tasks
- Realtime monitoring, filtering of charts based on conditions and variables
- Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics
After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .
I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.
Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).
@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.
Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.
Pros of Kibana
- Easy to setup88
- Free64
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- Can build dashboards3
- More "user-friendly"3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
Pros of logz.io
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Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3