Honeycomb Enterprise Reviews

We were building a product for one of the biggest wealth management platforms in the world, an American wealth management platform. For them, it is really important for the product to be reliable and for them to set up KPIs, especially for vendors like us who worked for them.

The debugging process usually involved Splunk Cloud or Honeycomb Enterprise traces. Whenever I was looking at an issue, I probably went through the traces because it was a microservice architecture. Sometimes it really helped to understand the call chain. For example, if there were 10 microservices calling each other in some sort of order, being able to visualize that and look through that was pretty useful.

I would say all of these three are pretty good features of Honeycomb Enterprise.

Honeycomb Enterprise is super useful if you think before this paradigm shift, when it was really important for humans to be able to see things visualized. For me to better understand in this very complex microservice architecture what's going on, Honeycomb Enterprise really helped with that through its good UI. However, the reason it's only five is because it's lagging behind in terms of AI-compatible features.

The major thing that's missing from Honeycomb Enterprise is AI compatibility. As far as I know, it's not really a text-based or code-based tool. It's more of a UI right now, which before this paradigm shift where everyone is using AI agents to work, was pretty useful. However, after this paradigm shift, I think it's really important for a tool like this to be AI-friendly. Honeycomb Enterprise is really lagging behind in this area, and I don't know how they could manage that.

For us it was sometimes pretty slow using Honeycomb Enterprise. I don't know if that can be improved. Although we might use the paid solution or self-host it somewhere because of privacy concerns. Maybe that's not Honeycomb Enterprise's fault. However, the main thing is that I think everything should very hard aim for the direction of being AI compatible because every engineer, or most engineers now use AI to code. If something is not easy to work with AI agents, that will stay in the past.

I used Honeycomb Enterprise at my previous job for around one to one and a half years.

Honeycomb Enterprise is stable.

It's very scalable since we used it for a really big organization and it worked.

If it's a big company, I understand using Honeycomb Enterprise. If it's a small company, I would suggest using some sort of open-source solution or something that is code-based and is easier to use with AI.

The solution is mainly used for stack observability. It observes service behavior or any kind of failure that may be happening. The tool is also related to research. My company is working more on this, but I have been working on my SLOs and defining SLOs for the last seven months.

The solution's most valuable features are the queries for the OpenTelemetry events and all the tracing. The solution is very easy to use, and the dashboard is very intuitive.

We faced some OpenTelemetry metrics lost between the communication from the service and the Honeycomb.io. I can't say if this is a Honeycomb.io issue or if there are some limitations in OpenTelemetry.

Alerts are very helpful in Honeycomb.io, but we don't usually merge because we can compare queries with queries for making alerts. We can make alerts based on static numbers, which may block us from building alerts that could be generic enough or could be serviced.

I have been using Honeycomb.io for two years.

We’ve never had any issues with the solution’s stability.

Honeycomb.io is a scalable solution. The service is very resilient and can handle a lot of data. The quantity of data Honeycomb.io can parse and use to create charts is really good. More than 100 users are using the solution in our tech team.

I rate the solution’s scalability an eight or nine out of ten.

The solution's initial setup is easy. I think the hardest part is to understand OpenTelemetry in general.

We set up Honeycomb.io on all the services so that we can have all the set traces of the communication between all the services inside the company. This helps us understand where it could be failing, which in turn helps with failures and observability.

When we are not thinking about one specific failure but a major one, we can create queries for statistics views like the P99 or P95 behaviors. That's very helpful. I would recommend the solution to other users because it is very helpful.

Overall, I rate the solution a nine out of ten.

There aren't any specific use cases for the solution as such. In our company, we use the solution for SLA and SLO-related work.

Honeycomb.io facilitates the identification of service downtime by integrating with our logging endpoint in Splunk. We log the endpoint in Splunk, and Honeycomb.io scrapes those logs. Honeycomb.io employs a different set of queries to alert the application team whenever it is unable to scrape the logs. This notification informs the team that the app is down.

There aren't currently many features we are using in the solution. The features that we use in the solution are to improve the functioning of our organization.

The process of log scraping gets delayed on Honeycomb.io. At times, it gives false alerts to the application team.

It would be good if Honeycomb.io could integrate with third-party tools or paid services, which can make it easy for us to alert our teams more frequently.

I have been using Honeycomb.io for six months. Also, I don't remember the version of the solution I am using.

Stability-wise, I rate the solution an eight out of ten.

Scalability-wise, I rate the solution a seven out of ten.

We don't have any plans in our company to increase the usage of the solution.

The solution's technical support team is good. We have received a few hours of support directly from the support team. Also, we used to have KT sessions organized by Honeycomb.io, during which we used to get our queries addressed while attempting to get familiar with the tool.

The solution's initial setup process was straightforward since we were getting enough support from Honeycomb.io's team. The solution is deployed on the cloud.

Specifically, I would suggest application teams use Honeycomb.io since it is a good tool. Also, it can be helpful for members of applications teams to identify downtimes and SLO-related issues. Overall, I rate the tool an eight out of ten.