Elastic Observability and Prometheus-AI Platform cater to monitoring and analytics needs, each excelling in different areas; Elastic Observability holds an advantage with its comprehensive platform capabilities and favorable licensing model.
Features: Elastic Observability provides advanced machine learning, diverse integration capabilities, and a scalable architecture, featuring robust central logging and seamless end-to-end platform integration. Prometheus stands out with robust metric collection, excellent scraping mechanisms, and seamless Grafana integration, focusing on flexibility and high availability.
Room for Improvement: Elastic Observability could improve automation, visualization tools, strategic AI integration, and role management. Prometheus needs enhancements in visualization, user interface, and PromQL documentation, alongside expanded exporter support and better integration solutions.
Ease of Deployment and Customer Service: Elastic Observability is adaptable to hybrid and on-premises deployment, with strong but occasionally cumbersome support. Prometheus offers easy customization through its open-source model, with support primarily relying on community forums, which may be insufficient in complex scenarios.
Pricing and ROI: Elastic Observability uses variable pricing based on features and infrastructure, posing cost challenges for small startups but offering high ROI for larger enterprises. Prometheus, being open-source, is cost-effective and flexible, though hidden costs in expertise and integrations can arise.
Elastic Observability has saved us time as it's much easier to find relevant pieces across the system in one screen compared to our own software, and it has saved resources too since the same resources can use less time.
Using open-source Prometheus saves me money compared to AWS native services.
Elastic support really struggles in complex situations to resolve issues.
Their excellent documentation typically helps me solve any issues I encounter.
Prometheus does not offer traditional technical support.
I rate the scalability of Elastic Observability as a ten, as we have never seen issues even with a lot of data coming in from more customers, provided we have the appropriate configuration.
Elastic Observability seems to have a good scale-out capability.
Elastic Observability is easy in deployment in general for small scale, but when you deploy it at a really large scale, the complexity comes with the customizations.
Prometheus is scalable, with a rating of ten out of ten.
There are some bugs that come with each release, but they are keen always to build major versions and minor versions on time, including the CVE vulnerabilities to fix it.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
I would rate the stability of Elastic Observability as a ten, as we don't experience any issues.
Deploying it on multiple instances or using Kubernetes for automatic management has enhanced its stability.
For instance, if you have many error logs and want to create a rule with a custom query, such as triggering an alert for five errors in the last hour, all you need to do is open the AI bot, type this question, and it generates an Elastic query for you to use in your alert rules.
It lacked some capabilities when handling on-prem devices, like network observability, package flow analysis, and device performance data on the infrastructure side.
Some areas such as AI Ops still require data scientists to understand machine learning and AI, and it doesn't have a quick win with no-brainer use cases.
The license is reasonably priced, however, the VMs where we host the solution are extremely expensive, making the overall cost in the public cloud high.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
Observability is actually cheaper compared to logs because you're not indexing huge blobs of text and trying to parse those.
Prometheus is cost-effective for me as it is free.
The most valuable feature is the integrated platform that allows customers to start from observability and expand into other areas like security, EDR solutions, etc.
the most valued feature of Elastic is its log analytics capabilities.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
It allows me to save money by avoiding costs associated with AWS native services like CloudWatch or Amazon Prometheus.
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 12 |
Elastic Observability offers a comprehensive suite for log analytics, application performance monitoring, and machine learning. It integrates seamlessly with platforms like Teams and Slack, enhancing data visualization and scalability for real-time insights.
Elastic Observability is designed to support production environments with features like logging, data collection, and infrastructure tracking. Centralized logging and powerful search functionalities make incident response and performance tracking efficient. Elastic APM and Kibana facilitate detailed data visualization, promoting rapid troubleshooting and effective system performance analysis. Integrated services and extensive connectivity options enhance its role in business and technical decision-making by providing actionable data insights.
What are the most important features of Elastic Observability?Elastic Observability is employed across industries for critical operations, such as in finance for transaction monitoring, in healthcare for secure data management, and in technology for optimizing application performance. Its data-driven approach aids efficient event tracing, supporting diverse industry requirements.
Prometheus-AI Platform offers flexible solutions for collecting, visualizing, and comparing metrics, appreciated for its scalability, rich integrations, and open-source adaptability.
Prometheus-AI Platform provides a reliable framework for monitoring and analyzing metrics across diverse environments. With extensive API support, it supports data collection, querying, and visualization, integrating seamlessly with tools like Grafana. High availability, scalability, and lightweight configuration make it suitable for traditional and microservice environments, while community support enhances its utility. Though its query language and interface require improvements for better ease of use, and with calls for stronger integration options, the platform remains a leading choice for comprehensive metric analysis.
What are Prometheus-AI Platform's main features?Companies leverage Prometheus-AI Platform across various industries, utilizing it to monitor and analyze metrics from applications and infrastructure. It is extensively used in financial services and IT sectors for collecting, scraping logs, and monitoring Kubernetes deployments. Deployed both on-premise and in cloud environments like Azure and Amazon, it supports system and application metrics analysis, ensuring a comprehensive view for developers.
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