Airbrake vs. Elastic Observability

Airbrake is very good at what it does, I don’t really have any criticism at all on that front. It’s less well-suited when bugfixing goes beyond the immediate error and means looking at a lot of context (particularly asynchronous context) like logs.

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

• Integration with most of languages and frameworks as well support via existing logging configs.
• Aggregations on occurrences which helps to understand and analyse issues better.
• Web UI displays all error related parameters which I never could have figured out in manual debugging.

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

Incentivized

• We use Airbrake in conjunction with OpsGenie, but I feel like there could be more room for integration between the two.
• I think it would also be nice if there was a GitHub integration that would comment on recently merged error-prone PRs, currently, we need to dig into the error to find the commit.
• Generally, more integrations would be nice as people often forget about Airbrake when they are stressed out about an issue.

Incentivized

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

Incentivized

I love to use Airbrake and New Relic in conjunction. New Relic has better metrics and data that you can really dig into (especially for optimizations), but the error part has always been kinda meh. I fee like Airbrake has done an awesome job at this

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

• Hugely reduced frontend errors in devices we don’t often test: means more conversions and sales
• Lets us know immediately when there is an error: shorter error durations also means higher sales
• Ultimately once we’ve solved all the most common bugs: more time for developers to focus on other things

Incentivized

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

Incentivized