blackfire.io vs. Elastic Observability

Blackfire.io is super helpful in allowing proactive testing of applications before releasing on the production environment. Blackfire.io provides actionable insights to improve code proactively by integration with the development software. Also tracks the live production environment and is able to highlight any loading issues or unusual bounce rates. Blackfire.io also monitors HTTP requests and SQL queries which again help improve efficiency for developers and reduce development time.

Incentivized

We can use this Elastic Observability in our business problems such as Creating internal/operational efficiencies issues, customer relations/service, and business process outcomes issues. This product has a lot of features for the above problems. But this product may be having some issues when charting purposes. But it can adjust for that purpose.

Incentivized

• Provide details on processes eating up valuable server resources
• Alert against bottlenecks and find bottlenecks
• Performs load tests in code before moving to production environments

Incentivized

• Open source code base
• Community support
• Is fast in processing

Incentivized

• They count same API endpoint AND same method (e.g. POST, OPTIONS, GET etc) as different profiles
• Customer service needs a lot of improvement
• The documentation can be a bit more detailed to remove any scope of doubts

Incentivized

• Difficult to setup/maintain
• Search pattern bar could be more user-friendly
• Premium subscription features are very expensive

Incentivized

blackfire.io definitely provides an edge over competing products such as Datadog due to the level of monitoring and analysis it provides. Datadog is a great tool however when evaluating and catching inefficiencies in lower non production environments, blackfire.io provides a level of reporting that were did not find any competing products.

Incentivized

Splunk is a very good product but the licensing costs are high; we utilise the best of both worlds by using both products for slightly different purposes. We put the voluminous data with simple use cases in Elastic where it doesn't cost too much and can be searched quickly while putting the less voluminous data with more complex use cases in Splunk so we can take advantage of Splunk's very comprehensive but often much slower SPL search query language

Incentivized

• Reduce in bugs by 50%, more happy customers
• 2x decrease in load time for the website
• 100% performance validation before production release

Incentivized

• Cost management.
• Good customer increment.
• Time management.

Incentivized