I would rate Honeycomb Enterprise seven out of ten. The reason I gave this rating is because it provides strong distributed tracing and useful query capability, but the learning curve is steep for new users. Regarding Honeycomb Enterprise's AI capabilities, I find it a useful foundation for troubleshooting, but I would want clear controls around data access and user permissions. Honeycomb Enterprise's AI output is sometimes useful but may not always meet my expectations for accuracy and reliability. I find Honeycomb Enterprise's AI output accurate. I find Honeycomb Enterprise useful for understanding issues across services, but I treat AI analysis as a recommendation rather than the final answer. My advice would be to clearly identify your observability needs before adopting Honeycomb Enterprise and ensure the team is comfortable with distributed tracing and OpenTelemetry.
I would rate Honeycomb Enterprise an eight out of ten. I chose eight out of ten because its distributed tracing, powerful querying, and root cause analysis capabilities are impressive, and while it offers detailed observability and powerful root cause analysis, features like BubbleUp and the service map make troubleshooting quite easy.Regarding Honeycomb Enterprise's AI capabilities, it offers a solid foundation for security and governance, particularly through role-based access controls, single sign-ons, and data access management. For its AI capabilities, better transparency into how AI processes data, stronger audits, and more controls over sensitive information would be very valuable. The AI capabilities of Honeycomb Enterprise seem quite promising for identifying anomalies, analyzing performance, and suggesting potential root cause analyses, but accuracy and reliability are critical, especially in complex microservices environments, where AI-generated insights should be validated against actual traces and data to avoid false positives or misleading conclusions. I recommend starting with a small set of microservices at first and then gradually expanding to other microservices, while investing time in proper implementation, defining clear SLOs, configuring meaningful alerts, monitoring ingestion costs, and using sampling strategies where appropriate, as Honeycomb Enterprise is particularly valuable for complex distributed systems needing deep visibility into application performance. My overall rating for Honeycomb Enterprise is eight out of ten.
I rate Honeycomb Enterprise an eight overall. I rate it an eight because the main strengths are distributed tracing, flexible investigation, and the potential to reduce debugging time. The deductions reflect the learning curve. I deduct two points because dashboard customization has limitations, onboarding for OpenTelemetry needs improvement, and clear cost forecasting is required. Additionally, I would appreciate stronger integration with DevOps and other incident management workflows. Regarding Honeycomb Enterprise's governance and security, it is very important for an organization because governance should cover access controls, SSO (single sign-on) operations, role-based permissions, telemetry retention, and the handling of sensitive attributes. Honeycomb offers enterprise-oriented access and security capabilities, but the exact controls depend on the plan and configurations. They are well-versed in security protocols, but we have to ensure that we are not sending secrets or unnecessary personal data in traces and verify the organization's compliance requirements before onboarding production telemetry. Regarding Honeycomb Enterprise's accuracy and reliability of output, the platform provides useful request-level context for troubleshooting when telemetry is correctly instrumented and collected. Its findings have really helped my engineers and me identify bottlenecks. However, data still needs interpretation in context and verification against application behavior. Missing instrumentation, sampling, and incomplete attributes can limit investigation, but overall, Honeycomb Enterprise is accurate and reliable, cutting down 50 to 60% of the troubleshooting time. When advising others looking into using Honeycomb Enterprise, I recommend that when starting with critical applications or microservices, troubleshooting production issues is very important and time-consuming. There will be a number of services, and tracing in and out of each service is very challenging. I suggest integrating with OpenTelemetry, instrumenting important services and APIs, and using distributed tracing and high-cardinality queries to understand where requests are slowing down or failing. Others can assess their requirements and research the features they seek. Training is involved for the engineering team on tracers and queries capabilities, which requires considerable effort, and saving this effort is something I highlight as vital. My rating for this product is eight out of ten.
Some of the best features Honeycomb Enterprise offers include distributed tracing, which is probably the most important feature for my use case, and it helped me answer where the request spent its time. If production troubleshooting is an issue for you, Honeycomb Enterprise is the only solution and is a very good one. I would rate this review 8.5 out of 10.
I would say that Honeycomb Enterprise takes governance and security very seriously. I appreciate that it supports enterprise features such as role-based access control, SSO integration, and audit capabilities, which are important for controlling access to observability data. From an AI perspective, I see the AI features more as an assistant for integration rather than something making autonomous decisions, which is the right approach for production environments. I still want engineers to validate recommendations before taking actions. Overall, I would say the governance and security are strong, and I would always prefer to see continuous improvement around access control areas, data privacy options, and transparency into how AI generates insights and processes data, especially for organizations with very strict compliance requirements. The AI capabilities are generally accurate and reliable, especially when used to assist with integration rather than replacing engineering judgment. The suggestions and insights usually surface patterns that I might not notice immediately. That being said, I do not treat AI output as my final answer. I always validate it against distributed traces, telemetry, and application logs before making production decisions. Overall, I rate the AI accuracy high for accelerating root cause analysis, but I still see it as a decision support tool rather than an autonomous troubleshooting solution. My advice would be to invest time in planning an observability strategy before rolling it out and instrument your applications. I would also recommend instrumenting your applications with OpenTelemetry from the beginning and focusing on collecting meaningful telemetry instead of trying to capture everything. Start with your most critical service and build from there. I also recommend training the engineering team on distributed tracing and Honeycomb Enterprise query capability because that is where you will get the most value. Finally, monitor your telemetry volume and retention policy to keep costs under control as your environment grows. If you approach it that way, Honeycomb Enterprise becomes a very powerful tool for troubleshooting, performance optimization, and improving the reliability of your cloud-native applications. I rate this product a 9 out of 10 overall.
If we speak about whether Honeycomb Enterprise is worth the money, I would say if we speak about enterprises or companies such as mine, we have dozens of applications we have developed or deployed or are running live. It provides a single dashboard for all of them. We can select projects and see their logs and debug from there. Basically, it is based on OpenTelemetry. So, OpenTelemetry provides great graphical analytics. We can trace each and every call from there, historic calls also, from two or three years ago. So, it helps a lot in debugging. What does a developer need? If a bug came out, if a service fails, then we should work on it immediately. Before, we had to watch AWS CloudWatch logs and see terminals of the EC2 instance machines. So, that is quite time-consuming and very irritating. This resolves all of that because we have a dashboard and we can run queries to get these error logs. For debugging, we can just run queries such as what API is taking more than five milliseconds. It is just amazing working with it. We can get directly what we want within the time frame, and it helps debug the code and the terminal. I gave this review a rating of nine out of ten.
About 30% more projects can be handled than prior to the use of observability tools as a whole. Although more than one type of tool is used because diverse clients with different requirements are served, in general, 30% improvement has been observed. This is measured based on the number of new projects that can be taken and the revenues that can be generated as a result. Due diligence and evaluations should be conducted before choosing if Honeycomb Enterprise is optimum for what is required. The review rating for this product is 6 out of 10.
Effectiveness means, Honeycomb Enterprise is very effective. We have all these kind of things structured tracing, collaborating feature, and data analysis. Honeycomb Enterprise is more effective than Splunk. When we are working on a large data set, we have a lot of values. High cardinality data support is offered by Honeycomb Enterprise, which means we can analyze lots of unique values. It helps in fast debugging. This is a major win. Fast debugging is what it offers. In Splunk, tracing becomes very slow if we have complex queries. That is something which we cannot afford to have when we are having immediate deadlines, very tight deadlines. At that time we need a very fast debug. If we build new dashboards, then even it helps in tracking these issues very fast. You have graphs also, you have a tabular format of analysis. BubbleUp anomalies again, because they highly, if any issue occurred in any of the request, it will be shooting it up directly on the dashboard and you can see from there that this is the issue, it is highlighted. We need to address it quickly because the deadline is nearing and it is a very close call. It has a good tracing support. It helps tracking the request across the services. It offers better collaboration because teams can investigate the incident together by this collaboration feature of Honeycomb Enterprise. Honeycomb Enterprise is effective because it helps teams quickly find, understand and debug unusual behavior in the complex systems. Whenever we make a request, there is an inter-service communication. One service makes a request to another service. In that journey, we have a trace ID, we have a span ID, we have a service name, a status and duration. This gives us proper fields and the format to record the behavior of each request while it is making the transition from one service to another service. This is a transit. During this transit, we need to maintain the logs. Honeycomb Enterprise has given us a very good facility where we have a few fields. The trace ID is the main, span ID comes under it and the service name, status and the duration for which this request happened. This is one thing that we have these kind of format wherein these kind of parameters are there. But ideally, everybody looks for the benefit. That is one aspect that we have this kind of format, but what will this help in? It helps in finding the problem a bit faster. Anomalies, requests, wherever it went slower, wherever we found some bug or maybe the request got stuck somewhere. It is helpful in identifying it a bit easily and maybe faster. It is helpful in understanding that flow, how we are navigating from one request to the other request, what are the intermediate processes happening in between and whatever operations are happening between this one request, we have each thing properly formatted with the help of these parameters like the trace ID and span ID. That way we can probably identify the root causes of our if any issues occur. Even if the issues don't occur, then it is a good practice to keep the logs so that some or another day, maybe if you want to track yourself back to the previous logs, you can basically possibly track it. You can address the issue or maybe that aspect or behavior which you want to monitor in some time in the future. My overall review rating for Honeycomb Enterprise is 9 out of 10.
Customer Support Engineer at a insurance company with 10,001+ employees
Real User
Top 10
Apr 28, 2026
I have read about Honeycomb Enterprise's query engine and the visualization part, which is very interesting. However, those decisions were made by the top leads, so I am not part of that decision. According to me, that is interesting and we can use it. Grafana has a visualization technique in which it shows the spikes and different structures if some bugs came up and sudden changes happened in the visualization. We can directly identify that this could be happening and this is the bug which we can implement. If it provides us the report in real time, then that would be better so we can proactively inform the clients and other clients who are using the applications and we can improve our methods and systems. Honeycomb Enterprise and Grafana are tools for similar techniques, but I can say a few things which can be implemented in Honeycomb Enterprise. The feedback which I have given for Honeycomb Enterprise is relatable for Grafana as well because I was using the same technique with Grafana and we can implement the same technique in Honeycomb Enterprise, then that will be great. I have not used certain features, so I cannot comment on those. I believe I have shared feedback which I think is good from my perspective, and I have shared that feedback accordingly. My review rating for Honeycomb Enterprise is 9.5 out of 10.
In those scenarios where you are not getting the complete data to the customer, it will cap the data to one megabyte. For tracing solution, definitely, I will always suggest Honeycomb Enterprise is a better solution. Other observability solutions out there are expensive and they are not that reliable in terms of tracing, to be honest. If there is a genuine bug in the code, then Honeycomb Enterprise is also very useful for debugging the issues. Even a data-driven issue can be fixed using it because we get all the data when a user is providing any request. We actually get the input data in Honeycomb Enterprise and that way we can solve it very fast. We can see what kind of query they hit or what kind of workflow they have done. I think people are very resistant to adopting Honeycomb Enterprise. If you see the community, I am in the Honeycomb Enterprise community right now. I usually ask a lot of questions there. Now that AI came, the questions volume has decreased significantly. However, previously it was a very healthy community where a lot of questions came in. If you go through those questions, the major things are around how to set up things. It is not always about the metrics cardinality or how things are not working around it or if there are any solutions. I would say it worked pretty well for mid-tier or similar tier companies. I do not have any complaints. For enterprise solutioning, probably when a lot of data points come in, DataDog obviously has an edge there. They are actually the market leaders around it. They have better metrics, APM, and other aspects there. My overall review rating for Honeycomb Enterprise is eight out of ten.
Another thing is that for microservices, when you have multiple microservices installed, that is also required. There are tools where you install on a single microservice, but then these microservices interact with multiple microservices. That kind of picture, I have seen that in AppDynamics; they do give a picture showing that a particular request which arrived here had interaction with these other third-party services or microservices and databases. That is what we need. My overall rating for Honeycomb Enterprise is five out of ten.
Having those alerts and metrics in Slack helps with our SLAs. If we ever have downtime in a service, we can get back to it straight away. I rate Honeycomb Enterprise a seven out of ten because I feel a lot of the journeys could be made cleaner. Some of the dashboards and the insights could be a bit more well-refined. For example, Grafana has a lot better visualization tools, but it needs to be a bit more involved. Honeycomb Enterprise is deployed in our organization in a public cloud and is hosted by Honeycomb, who is the provider. My experience with pricing, setup cost, and licensing is that the number of samples would be good. The number of events, really, so we can pay less. I do not have the metrics for a return on investment, but it has been very useful. It is quite expensive, but I think we get value out of it. My overall rating for Honeycomb Enterprise is seven out of ten.
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.
Software Engineer at a financial services firm with 10,001+ employees
Real User
Top 20
Apr 6, 2023
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.
Honeycomb Enterprise delivers distributed tracing, real-time performance monitoring, and high-cardinality data analysis to enhance observability, scalability, and incident response.Honeycomb Enterprise provides organizations with the tools needed for debugging and performance optimization in complex environments. Utilizing features like BubbleUp and powerful querying, users gain end-to-end visibility and reduce troubleshooting time. Integrations with DevOps tools and customizable dashboards...
I would rate Honeycomb Enterprise seven out of ten. The reason I gave this rating is because it provides strong distributed tracing and useful query capability, but the learning curve is steep for new users. Regarding Honeycomb Enterprise's AI capabilities, I find it a useful foundation for troubleshooting, but I would want clear controls around data access and user permissions. Honeycomb Enterprise's AI output is sometimes useful but may not always meet my expectations for accuracy and reliability. I find Honeycomb Enterprise's AI output accurate. I find Honeycomb Enterprise useful for understanding issues across services, but I treat AI analysis as a recommendation rather than the final answer. My advice would be to clearly identify your observability needs before adopting Honeycomb Enterprise and ensure the team is comfortable with distributed tracing and OpenTelemetry.
I would rate Honeycomb Enterprise an eight out of ten. I chose eight out of ten because its distributed tracing, powerful querying, and root cause analysis capabilities are impressive, and while it offers detailed observability and powerful root cause analysis, features like BubbleUp and the service map make troubleshooting quite easy.Regarding Honeycomb Enterprise's AI capabilities, it offers a solid foundation for security and governance, particularly through role-based access controls, single sign-ons, and data access management. For its AI capabilities, better transparency into how AI processes data, stronger audits, and more controls over sensitive information would be very valuable. The AI capabilities of Honeycomb Enterprise seem quite promising for identifying anomalies, analyzing performance, and suggesting potential root cause analyses, but accuracy and reliability are critical, especially in complex microservices environments, where AI-generated insights should be validated against actual traces and data to avoid false positives or misleading conclusions. I recommend starting with a small set of microservices at first and then gradually expanding to other microservices, while investing time in proper implementation, defining clear SLOs, configuring meaningful alerts, monitoring ingestion costs, and using sampling strategies where appropriate, as Honeycomb Enterprise is particularly valuable for complex distributed systems needing deep visibility into application performance. My overall rating for Honeycomb Enterprise is eight out of ten.
I rate Honeycomb Enterprise an eight overall. I rate it an eight because the main strengths are distributed tracing, flexible investigation, and the potential to reduce debugging time. The deductions reflect the learning curve. I deduct two points because dashboard customization has limitations, onboarding for OpenTelemetry needs improvement, and clear cost forecasting is required. Additionally, I would appreciate stronger integration with DevOps and other incident management workflows. Regarding Honeycomb Enterprise's governance and security, it is very important for an organization because governance should cover access controls, SSO (single sign-on) operations, role-based permissions, telemetry retention, and the handling of sensitive attributes. Honeycomb offers enterprise-oriented access and security capabilities, but the exact controls depend on the plan and configurations. They are well-versed in security protocols, but we have to ensure that we are not sending secrets or unnecessary personal data in traces and verify the organization's compliance requirements before onboarding production telemetry. Regarding Honeycomb Enterprise's accuracy and reliability of output, the platform provides useful request-level context for troubleshooting when telemetry is correctly instrumented and collected. Its findings have really helped my engineers and me identify bottlenecks. However, data still needs interpretation in context and verification against application behavior. Missing instrumentation, sampling, and incomplete attributes can limit investigation, but overall, Honeycomb Enterprise is accurate and reliable, cutting down 50 to 60% of the troubleshooting time. When advising others looking into using Honeycomb Enterprise, I recommend that when starting with critical applications or microservices, troubleshooting production issues is very important and time-consuming. There will be a number of services, and tracing in and out of each service is very challenging. I suggest integrating with OpenTelemetry, instrumenting important services and APIs, and using distributed tracing and high-cardinality queries to understand where requests are slowing down or failing. Others can assess their requirements and research the features they seek. Training is involved for the engineering team on tracers and queries capabilities, which requires considerable effort, and saving this effort is something I highlight as vital. My rating for this product is eight out of ten.
Some of the best features Honeycomb Enterprise offers include distributed tracing, which is probably the most important feature for my use case, and it helped me answer where the request spent its time. If production troubleshooting is an issue for you, Honeycomb Enterprise is the only solution and is a very good one. I would rate this review 8.5 out of 10.
I would say that Honeycomb Enterprise takes governance and security very seriously. I appreciate that it supports enterprise features such as role-based access control, SSO integration, and audit capabilities, which are important for controlling access to observability data. From an AI perspective, I see the AI features more as an assistant for integration rather than something making autonomous decisions, which is the right approach for production environments. I still want engineers to validate recommendations before taking actions. Overall, I would say the governance and security are strong, and I would always prefer to see continuous improvement around access control areas, data privacy options, and transparency into how AI generates insights and processes data, especially for organizations with very strict compliance requirements. The AI capabilities are generally accurate and reliable, especially when used to assist with integration rather than replacing engineering judgment. The suggestions and insights usually surface patterns that I might not notice immediately. That being said, I do not treat AI output as my final answer. I always validate it against distributed traces, telemetry, and application logs before making production decisions. Overall, I rate the AI accuracy high for accelerating root cause analysis, but I still see it as a decision support tool rather than an autonomous troubleshooting solution. My advice would be to invest time in planning an observability strategy before rolling it out and instrument your applications. I would also recommend instrumenting your applications with OpenTelemetry from the beginning and focusing on collecting meaningful telemetry instead of trying to capture everything. Start with your most critical service and build from there. I also recommend training the engineering team on distributed tracing and Honeycomb Enterprise query capability because that is where you will get the most value. Finally, monitor your telemetry volume and retention policy to keep costs under control as your environment grows. If you approach it that way, Honeycomb Enterprise becomes a very powerful tool for troubleshooting, performance optimization, and improving the reliability of your cloud-native applications. I rate this product a 9 out of 10 overall.
If we speak about whether Honeycomb Enterprise is worth the money, I would say if we speak about enterprises or companies such as mine, we have dozens of applications we have developed or deployed or are running live. It provides a single dashboard for all of them. We can select projects and see their logs and debug from there. Basically, it is based on OpenTelemetry. So, OpenTelemetry provides great graphical analytics. We can trace each and every call from there, historic calls also, from two or three years ago. So, it helps a lot in debugging. What does a developer need? If a bug came out, if a service fails, then we should work on it immediately. Before, we had to watch AWS CloudWatch logs and see terminals of the EC2 instance machines. So, that is quite time-consuming and very irritating. This resolves all of that because we have a dashboard and we can run queries to get these error logs. For debugging, we can just run queries such as what API is taking more than five milliseconds. It is just amazing working with it. We can get directly what we want within the time frame, and it helps debug the code and the terminal. I gave this review a rating of nine out of ten.
About 30% more projects can be handled than prior to the use of observability tools as a whole. Although more than one type of tool is used because diverse clients with different requirements are served, in general, 30% improvement has been observed. This is measured based on the number of new projects that can be taken and the revenues that can be generated as a result. Due diligence and evaluations should be conducted before choosing if Honeycomb Enterprise is optimum for what is required. The review rating for this product is 6 out of 10.
Effectiveness means, Honeycomb Enterprise is very effective. We have all these kind of things structured tracing, collaborating feature, and data analysis. Honeycomb Enterprise is more effective than Splunk. When we are working on a large data set, we have a lot of values. High cardinality data support is offered by Honeycomb Enterprise, which means we can analyze lots of unique values. It helps in fast debugging. This is a major win. Fast debugging is what it offers. In Splunk, tracing becomes very slow if we have complex queries. That is something which we cannot afford to have when we are having immediate deadlines, very tight deadlines. At that time we need a very fast debug. If we build new dashboards, then even it helps in tracking these issues very fast. You have graphs also, you have a tabular format of analysis. BubbleUp anomalies again, because they highly, if any issue occurred in any of the request, it will be shooting it up directly on the dashboard and you can see from there that this is the issue, it is highlighted. We need to address it quickly because the deadline is nearing and it is a very close call. It has a good tracing support. It helps tracking the request across the services. It offers better collaboration because teams can investigate the incident together by this collaboration feature of Honeycomb Enterprise. Honeycomb Enterprise is effective because it helps teams quickly find, understand and debug unusual behavior in the complex systems. Whenever we make a request, there is an inter-service communication. One service makes a request to another service. In that journey, we have a trace ID, we have a span ID, we have a service name, a status and duration. This gives us proper fields and the format to record the behavior of each request while it is making the transition from one service to another service. This is a transit. During this transit, we need to maintain the logs. Honeycomb Enterprise has given us a very good facility where we have a few fields. The trace ID is the main, span ID comes under it and the service name, status and the duration for which this request happened. This is one thing that we have these kind of format wherein these kind of parameters are there. But ideally, everybody looks for the benefit. That is one aspect that we have this kind of format, but what will this help in? It helps in finding the problem a bit faster. Anomalies, requests, wherever it went slower, wherever we found some bug or maybe the request got stuck somewhere. It is helpful in identifying it a bit easily and maybe faster. It is helpful in understanding that flow, how we are navigating from one request to the other request, what are the intermediate processes happening in between and whatever operations are happening between this one request, we have each thing properly formatted with the help of these parameters like the trace ID and span ID. That way we can probably identify the root causes of our if any issues occur. Even if the issues don't occur, then it is a good practice to keep the logs so that some or another day, maybe if you want to track yourself back to the previous logs, you can basically possibly track it. You can address the issue or maybe that aspect or behavior which you want to monitor in some time in the future. My overall review rating for Honeycomb Enterprise is 9 out of 10.
I have read about Honeycomb Enterprise's query engine and the visualization part, which is very interesting. However, those decisions were made by the top leads, so I am not part of that decision. According to me, that is interesting and we can use it. Grafana has a visualization technique in which it shows the spikes and different structures if some bugs came up and sudden changes happened in the visualization. We can directly identify that this could be happening and this is the bug which we can implement. If it provides us the report in real time, then that would be better so we can proactively inform the clients and other clients who are using the applications and we can improve our methods and systems. Honeycomb Enterprise and Grafana are tools for similar techniques, but I can say a few things which can be implemented in Honeycomb Enterprise. The feedback which I have given for Honeycomb Enterprise is relatable for Grafana as well because I was using the same technique with Grafana and we can implement the same technique in Honeycomb Enterprise, then that will be great. I have not used certain features, so I cannot comment on those. I believe I have shared feedback which I think is good from my perspective, and I have shared that feedback accordingly. My review rating for Honeycomb Enterprise is 9.5 out of 10.
In those scenarios where you are not getting the complete data to the customer, it will cap the data to one megabyte. For tracing solution, definitely, I will always suggest Honeycomb Enterprise is a better solution. Other observability solutions out there are expensive and they are not that reliable in terms of tracing, to be honest. If there is a genuine bug in the code, then Honeycomb Enterprise is also very useful for debugging the issues. Even a data-driven issue can be fixed using it because we get all the data when a user is providing any request. We actually get the input data in Honeycomb Enterprise and that way we can solve it very fast. We can see what kind of query they hit or what kind of workflow they have done. I think people are very resistant to adopting Honeycomb Enterprise. If you see the community, I am in the Honeycomb Enterprise community right now. I usually ask a lot of questions there. Now that AI came, the questions volume has decreased significantly. However, previously it was a very healthy community where a lot of questions came in. If you go through those questions, the major things are around how to set up things. It is not always about the metrics cardinality or how things are not working around it or if there are any solutions. I would say it worked pretty well for mid-tier or similar tier companies. I do not have any complaints. For enterprise solutioning, probably when a lot of data points come in, DataDog obviously has an edge there. They are actually the market leaders around it. They have better metrics, APM, and other aspects there. My overall review rating for Honeycomb Enterprise is eight out of ten.
Another thing is that for microservices, when you have multiple microservices installed, that is also required. There are tools where you install on a single microservice, but then these microservices interact with multiple microservices. That kind of picture, I have seen that in AppDynamics; they do give a picture showing that a particular request which arrived here had interaction with these other third-party services or microservices and databases. That is what we need. My overall rating for Honeycomb Enterprise is five out of ten.
Having those alerts and metrics in Slack helps with our SLAs. If we ever have downtime in a service, we can get back to it straight away. I rate Honeycomb Enterprise a seven out of ten because I feel a lot of the journeys could be made cleaner. Some of the dashboards and the insights could be a bit more well-refined. For example, Grafana has a lot better visualization tools, but it needs to be a bit more involved. Honeycomb Enterprise is deployed in our organization in a public cloud and is hosted by Honeycomb, who is the provider. My experience with pricing, setup cost, and licensing is that the number of samples would be good. The number of events, really, so we can pay less. I do not have the metrics for a return on investment, but it has been very useful. It is quite expensive, but I think we get value out of it. My overall rating for Honeycomb Enterprise is seven out of ten.
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.
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.