Sentry and Elastic Observability offer robust monitoring solutions. Users are happier with Sentry's ease of use and customer support, but Elastic Observability excels in feature depth and performance, making it a favorable choice despite the higher cost.
Features: Sentry provides real-time error tracking, intuitive alerts, and a user-friendly interface. Elastic Observability offers comprehensive log analytics, multi-source data integration, and extensive data visualization capabilities.
Room for Improvement: Sentry needs enhanced integration with other DevOps tools, better performance monitoring, and scalability improvements. Elastic Observability requires a simpler setup process, more intuitive error tracking, and enhanced user documentation.
Ease of Deployment and Customer Service: Sentry has straightforward deployment and responsive support. Elastic Observability has a steeper learning curve but benefits from good customer support, though users find Sentry's deployment ease an advantage.
Pricing and ROI: Sentry is cost-effective with quick ROI and lower setup costs. Elastic Observability is more expensive but justifies the cost with its extensive features, leading to satisfactory ROI. Users feel Elastic Observability's higher costs are worthwhile due to its powerful capabilities.
Elastic support really struggles in complex situations to resolve issues.
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.
What is not scalable for us is not on Elastic's side.
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.
Elastic Observability is really stable.
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.
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.
At this time, I focus on finding and fixing bugs.
Sentry provides real-time error tracking which is invaluable for identifying and resolving issues quickly.
| Product | Market Share (%) |
|---|---|
| Elastic Observability | 3.9% |
| Sentry | 5.8% |
| Other | 90.3% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 3 |
| Large Enterprise | 3 |
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.
Sentry is a robust error management system known for real-time error tracking and integration with tools like Slack, GitLab, and Jira, benefiting those seeking comprehensive application performance insights.
Sentry offers a seamless platform to monitor errors in both front-end and back-end applications, providing real-time alerts and comprehensive event log context. With its integration capabilities, teams effectively track application metrics and access performance data without direct production access, ensuring enhanced reliability. Sentry's features such as event grouping and code trace logs linked to Git repositories highlight its utility in maintaining application efficiency. Enhanced security and regular updates make it a preferred choice over competitors. Despite some requests for improvements in automation and UI enhancements, Sentry remains invaluable for error management and application performance monitoring.
What are the key features of Sentry?In industries like technology, Sentry is crucial for monitoring errors in web applications, offering real-time alerts and performance tracking. It is frequently used in ETL processes to detect failures without direct developer access, benefiting teams who manage large-scale applications and databases efficiently.
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