Elastic Observability and Honeycomb.io compete in the observability space. Elastic Observability seems to have the upper hand due to a broader range of integrations and community support, while Honeycomb.io offers strong tracing and visualization.
Features: Elastic Observability is known for its extensive integration options, robust logging capabilities, and wide community support. Honeycomb.io is recognized for its strong distributed tracing features, insightful data analytics, and intuitive data visualization.
Room for Improvement: Elastic Observability could improve by reducing complexity, enhancing scalability, and simplifying the system. Honeycomb.io users find its learning curve challenging and desire more comprehensive documentation and easier onboarding.
Ease of Deployment and Customer Service: Elastic Observability is noted for flexible deployment options and efficient customer support. Honeycomb.io allows for swift deployment, though users experience mixed reviews regarding customer assistance and responsiveness.
Pricing and ROI: Elastic Observability offers cost-effective scalability but might involve higher initial setup costs. Honeycomb.io provides transparent pricing and focuses on delivering value through its features, leading to a higher perceived return on investment.
One example is the inability to monitor very old databases with the newest version.
Elastic Observability could improve asset discovery as the current requirement to push the agent is not ideal.
Elastic Observability seems to have a good scale-out capability.
What is not scalable for us is not on Elastic's side.
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.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
Elastic Observability is really stable.
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.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
Elastic Observability is primarily used for monitoring login events, application performance, and infrastructure, supporting significant data volumes through features like log aggregation, centralized logging, and system metric analysis.
Elastic Observability employs Elastic APM for performance and latency analysis, significantly aiding business KPIs and technical stability. It is popular among users for system and server monitoring, capacity planning, cyber security, and managing data pipelines. With the integration of Kibana, it offers robust visualization, reporting, and incident response capabilities through rapid log searches while supporting machine learning and hybrid cloud environments.
What are Elastic Observability's key features?Companies in technology, finance, healthcare, and other industries implement Elastic Observability for tailored monitoring solutions. They find its integration with existing systems useful for maintaining operation efficiency and security, particularly valuing the visualization capabilities through Kibana to monitor KPIs and improve incident response times.
Many offerings tout observability. How is Honeycomb different? We have defined what Observability is and have built a tool to help modern Dev, DevOps and Site Reliability Engineering teams operate more efficiently. Because it’s all about delivering high quality code, maintaining reliability and getting precious time back.
We made a critical decision to provide a seamless, current view of your system (from logs to events and traces) in a single data store, regardless of how complex your architecture is. This means you no longer have to toil with multiple tools or stitched-together solutions – burning time as you address issues affecting users.
We also believe strongly that optimizing systems and debugging should not be difficult and draining, freeing up more time to ship new code. Our approach emphasizes efficiency and knowledge sharing, thereby elevating everyone’s game and ultimately business outcomes.
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