Honeycomb Enterprise

Honeycomb Enterprise is used in support of global clients for observability and debugging cloud-native applications. It is utilized for tracing different types of observation of processes and tools as an automated tool. Depending on client requirements at global locations, Honeycomb Enterprise is selected from among other competitors.

For example, work with refineries within the energy space has involved using applications supported by Honeycomb Enterprise to understand and support clients in those projects. This includes understanding their processes and observing the different applications they use within AWS and the enterprise version. Using these tools, numerous different processes within the energy sector have been observed, and Honeycomb Enterprise is used for refineries and other operations. In specific cases, processes and how applications are run are observed, and different metrics like Honeycomb Metrics and other tools within the Canvas and other available options for model content protocol are used for different approaches. Anomalies are detected as part of MCP, and then efforts are made to debug those anomalies and work with the applications. For all clients, the process is generally the same, though specifics vary by application.

Honeycomb Enterprise is generally used to observe processes, understand anomalies, detect them, and work with applications to debug problems that surface. One challenge is that many things have to be defined manually and are not totally automated, while some competitors used for different projects or clients do have automation. Despite this, Honeycomb Enterprise remains a good tool for these purposes.

More automation would be great with Honeycomb Enterprise, as some competitors have more automation in this area. Dynatrace and others that have been used do provide more automation. Frequent evaluations of these tools against each other within the competitive landscape are conducted, so there is definitely room for improvement, and more can be done with Honeycomb Enterprise. However, at the moment, there is happiness with some abilities like natural language interactions and creating live diagrams through the process, which are all advantages that are beneficial.

Further automation of Honeycomb Enterprise and a reduction in the number of manual inputs currently required would be very beneficial. While it is known that they are working on that, some competitors already have automated features, with Dynatrace being one of them, and Grafana providing telemetry standards and other features. There are other things that could be done regarding further automation to reduce manual input, which does create challenges and slows progress.

Debugging could be improved with suggestions for improvement or for solving the problems. If suggestions were provided in the process of debugging, that would also be very helpful with Honeycomb Enterprise.

The AI capabilities of Honeycomb Enterprise are good, and this is considered positive. The interaction with these capabilities has not presented any issues.

In terms of accuracy, there is room for improvement, and for reliability, results are usually compared with other data and other approaches used for specific projects to ascertain that the results obtained are accurate. Since it is still in early stages and early use, full reliance cannot be placed on everything obtained from it. Results have to be compared and common sense and due diligence must be applied with other observations and analytic work to qualify the results. In terms of security, no breaches or issues have been noticed.

Honeycomb Enterprise offers excellent features that include the ability to define different triggers. Within Honeycomb Enterprise, 300 triggers can be defined, and different activities like single sign-on can be performed using various service level objectives available within the enterprise package. Queries can be defined manually, providing significant customization and the ability to customize and define as needed. Since it is cloud-native, it helps because applications supported are that way for global clients. Most importantly, Honeycomb Metrics for debugging and identifying issues and anomalies is a good step, though it is not sufficient because clients require remedies for the challenges encountered. The ability with Honeycomb Metrics in particular to debug those issues is very helpful. These advantages stand out for the enterprise version as opposed to Pro, where only two triggers can be defined, but in Enterprise, 300 or more can be configured.

The feature relied upon most day-to-day is Honeycomb Metrics. As a consulting company solving problems, identifying and solving issues is really important, and this feature facilitates workflows and expedites and provides efficiency within processes. It gives access to tools and techniques that would not otherwise be available.

Efficiency improvements in processes with Honeycomb Enterprise have definitely been seen, and the most representative example is that more projects can be handled than could be done prior to using Honeycomb Enterprise. Issues for clients can be resolved in an expedited fashion, and more projects can be accepted, making these efficiency improvements quite advantageous.

Honeycomb Enterprise has been used for the past five years.

To the degree expected from Honeycomb Enterprise, it has been stable.

In terms of the number of triggers and service level objectives, Honeycomb Enterprise's scalability is good and adequate. In terms of adding team members or multiple team members using it, no challenges have been encountered. Since we are a relatively small consulting practice, only that perspective can be offered.

Dynatrace, New Relic, and some other tools like DataDog have been used for different purposes and some for observability. Honeycomb Enterprise was not necessarily switched to but rather added to the suite of tools used for different projects.

New Relic, Dynatrace, DataDog, and Grafana have been considered as alternate solutions.

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.

We generally use it to create dashboards through Honeycomb Enterprise. What we do is we have Splunk for separate things for getting the traces and debugging and examining what are the errors or if all our services are up to date. The tracking of the logs and tracing them through the trace IDs is separately done through Splunk logs. However, Honeycomb Enterprise we use for observing and debugging. We don't need any pre-built dashboards in Honeycomb Enterprise. It helps us in addressing the production issues very quickly and identifying the current outliers. That is the reason why we are using Honeycomb Enterprise. It is not just for observability and debugging, but also for health checks of the services to ensure that all the services are up and running steadily and what are the statistics so far.

We are utilizing Honeycomb Enterprise because it addresses multiple concerns at the same time, not just debugging and observability alone, but also checking the health checks of the services. We can even create our own custom dashboards utilizing whatever kind of graphs we want, a pie chart or even bar graphs for analyzing different aspects of our services to ensure they are correctly satisfying our parameters, which is the basis for which we are trying to root our observations. If you go for Splunk, then maybe you can just use it for observing and debugging using their log traces. That is quite limited. You have to use the queries also which can get complex when there is so much nesting in the queries or it has to handle a large amount of data. When we scale up the data set, at that time it becomes a challenge in Splunk. Whereas in Honeycomb Enterprise, it is a little bit made easier.

In Splunk we write custom queries and then we try to find the log traces, but in Honeycomb Enterprise it is very fast querying. It doesn't need many pre-built dashboards. It serves us by offering great support for high cardinality data. It is also very useful for distributed tracing.

In our enterprises we are using cloud native systems. We are using applications which have support for cloud technology. Honeycomb Enterprise is designed for modern cloud native systems. Honeycomb Enterprise has that cutting edge technology that we actually want nowadays. Observability and debugging, it is excellent for that. Splunk can at times maybe slow speed can be the cause which we will not prefer Splunk for that because if we have a large data set then maybe we will not prefer Splunk. We can go for Honeycomb Enterprise because the speed which is offered by Honeycomb Enterprise is much more than what we get in Splunk. That is why we say that we don't need to write the complex queries which we have been doing so far in Splunk. Here the advantage is the speed. It is less focused on traditional log management than Splunk.

There are a lot many negatives that Honeycomb Enterprise is having. One of them is that it needs good instrumentation and instrumentation to work properly. It is not as strong as what a traditional log management. It may not suit security compliance heavy teams. It can feel expensive when we have a large data set and when we want to engage a larger data set, and when many people want to be engaged for analyzing a particular issue. It is less familiar if your team is used to the dashboard first tools. Here we don't prefer pre-built dashboards. If the team is accustomed to using the kind of tools wherein the dashboards are already in use, then at that time it can appear as if it is less familiar. Honeycomb Enterprise's downside is that it is powerful for observability, but it can be harder to learn and maybe not fit every use case.

I have been using Honeycomb Enterprise for two years.

Glitches are a part of every tool and they do keep on happening. Mostly it is reliable, but at times, maybe one or two times in two to three months, these issues do happen. Sometimes errors happen in Honeycomb Enterprise or sometimes latency comes. That kind of issues sometimes happen maybe in three months, around one or two times. I won't say that it is fully robust, but at times it does give us some problems.

I think it can be expensive because if we want to scale it up, they are having some charges on that. At times we can be shocked to see that this price is too high for involving too many developers on one peak or having a much bigger data set or more advanced features for our use. At times I have seen that there are very high incurring costs noticed in Honeycomb Enterprise. Sometimes when our requirement is not too high, we may feel that it is a decent cost, but at times when we want more features, the cost sometimes is higher.

BubbleUp anomalies is actually a very important feature and it is very much used in our organization. Assume that you are having a total of 100 requests. One of them is very, very slow due to some cause, maybe there are some errors, some maybe downstream service might be failing due to which it is getting slow. But now the cause is, should we let all the other 100 requests suffer because of just this one request? The answer is no. To highlight what is the issue going on in our currently running 100 requests, we just highlight that one request which is very slow or maybe we just move it to the top so that we can alert everybody that this is the problem. All the 99 are okay, but the only issue which we are facing is with this single request which is very slow. What is the reason? We can identify that through the log traces again. If we see any odd patterns, then it automatically highlights them. Any outliers are there, so it makes them a bit visible so that we can make the investigation a bit fast paced. Unusual behavior can be highlighted to everyone so that we could be exploiting the solution that we might have, the developers we can engage so that they can rectify that problem and all the 100 requests are in shape again and do not cause the unwanted latency which we sometimes do experience just because of maybe one or two requests.

Since our teams involve multiple users and this is not just a situation where we are in one location, somebody might be from abroad. The team is quite vast and has everybody from every corner of the globe. What this means is that it enables everybody to log in and collaborate together and work on the single task. Sometimes when the major issue happens, just a single developer is not given the priority to be doing the same task and multiple users are required to be involved in this kind of issues at the same time. They can address the different aspects of the same, but the logs are common. The collaboration feature, which is a very highly rated feature of Honeycomb Enterprise is offered by it for keeping multiple logins for all the users across the team. That way we can share the data amongst each other. We can add the comments so that everybody can view what are the observations of some other person from some other aspect of the issue. That way everybody is on the same page. Dashboards can be viewed together. We can even sometimes see multiple people viewing the same dashboard at the same time. This is a part of this collaboration feature. This enables the team, the global team for investigating the issues together and not just investigating it, but also collaborating with each other to help each other and to resolve this issue in a lesser amount of time. That way everybody could trace the logs and keep tracking simultaneously. Suppose that if we are working on one issue, maybe some other issue also comes up, but few developers couldn't notice it, but the third one could notice that and can bring it to the forum and everybody could then work on that.

The installation and deployment procedure is quite easier. Basically what happens is we just want to put a tool into the system and it's easy for Honeycomb Enterprise. It's not that complex. I don't find it complex.

Honeycomb Enterprise helps the ROI by reducing debugging time, speeding up the incident resolution, and helping teams find production issues faster. If you do ask for cost, then I would say I am also somewhere in that much amount, approximately 10 to 20 percent.

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 received information from your team regarding a peer review of Honeycomb Enterprise. As an observability engineer using Honeycomb Enterprise extensively, I can provide substantial input. My primary tasks involve OpenTelemetry, where I send numerous traces and metrics to Honeycomb Enterprise and use it for visualizations of how our code is functioning and how data moves through our systems.

We have identified many problems using Honeycomb Enterprise, such as delays in the system where latency is observed by Honeycomb Enterprise traces, which we then investigate further and resolve. We have our SLA and SLOs configured on Honeycomb Enterprise, allowing us to monitor our reliability score. Additionally, we send many metrics to the platform. In my previous organization, we used it only for traces, but in my current organization, I use it extensively for traces, metrics, SLA, and SLO work. We also use Signoz for infrastructure-related APM metrics and other things, but we cannot use it for Honeycomb Enterprise capabilities.

Honeycomb Enterprise has genuinely helped us. We are not using Honeybee command line, which is a native solution by Honeycomb Enterprise. Instead, we use OpenTelemetry so that we can switch vendors easily. Our complete codebase now has proper telemetry data sending into Honeycomb Enterprise.

We have provided traces for each of the functions we are using. If we need more in-depth insights into what happens inside the code, we set up additional telemetry data points in the code. When you set those telemetry data points there in the code, you send those traces to Honeycomb Enterprise and then we can examine them.

In terms of customer usage, observing how customers are using our solution is very helpful in debugging aspects and also helpful in cost prioritization aspects. If a customer is not using our solution extensively, we can scale it down a bit in terms of servers where our solution is running. This has given us a better approach, particularly for Lambdas where Lambdas are deployed, and we can save considerable costs.

If traffic is not very high and we are able to see metrics where traces are not falling a lot, we can scale down our instances and save our AWS billing. If there is a sudden spike, we can get that monitored via the SLI and SLOs that I have configured. If there are sudden errors, I can scale the instance up.

If there is a genuine bug in the code, Honeycomb Enterprise is 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 by seeing what query they hit or what workflow they have done.

Tracing is undoubtedly one of the best features they have developed. Tracing is basically the story in the code showing how the data is moving through. That is something which is extremely good. I can vouch for Honeycomb Enterprise, and that is the reason I brought Honeycomb Enterprise as our solution in my current organization.

That is the best thing Honeycomb Enterprise has done. In the past, in my previous organization, we were using something called GCID where if you send data, GCID is basically each document we are receiving. We were using an enterprise solution there called Smarsh. In that company, we were getting almost five petabytes of data per day, so that is the volume I was dealing with. Honeycomb Enterprise was actually really great in terms of tracing. We had a sampling rate and we were not sending all the traces, but the best part is we were able to add more cardinal events, such as GCID, which was getting mapped in each and every document. We were sending those metrics blindly and we did not have to think more about it. The amount of engineering that Honeycomb Enterprise has done is actually handling it very well. At that scale also, I can vouch for Honeycomb Enterprise that it manages traces beautifully.

That money is well spent. We do not have any complaints because tracing is something which is extremely good. How they cater to it and how they manage it is excellent. It is an extremely cost-effective solution right now in the market as compared to its peers like DataDog and other companies.

Throughput is something which I was using previously. There were around twenty million ingestions in my previous organization, obviously. Here the scale is not very high where I can challenge the engine. In my previous organization, when there were twenty plus million ingestions of data every day, in those kinds of scenarios, Honeycomb Enterprise actually stood very well. The major solutioning we were using was for traces. We were not using it for metrics. So if there are more data points, there might be a crash in Honeycomb Enterprise. However, honestly, I have not used it at that scale, so I cannot comment exactly on that point. I have used it for metrics and that performed well, but not on a very high volume of data where I can challenge the engine itself on how it is processing.

I would say it worked pretty well for mid-tier or similar tier companies. I do not have any complaints. But 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.

Honeycomb Enterprise's tracing solution stands out. Whatever they have built, they have built an excellent product there. Honeycomb Enterprise actually works very well with traces. When you send traces, you will get the complete view of the life of the code and how it has been executed.

The only complaint I have is that even though we are on a paid tier where we are paying one hundred thirty dollars per month, we are still lacking the amount of ingestion we have to do. It counts each and every event. We cannot blindly send the performance metrics and other things directly to Honeycomb Enterprise as it will raise our bills. They have some buffer protection, but that only happens twice or three times a month. After that, the number of events will get billed accordingly.

On the negative side, dashboarding is not that great. I have used solutions like Signoz which are open-source and Grafana. In those platforms, where we have open-source solutions where we are sending in the metrics, we are getting a complete dashboard and a complete solution around it where we can analyze everything in one shot. That is not something which is really great with Honeycomb Enterprise.

These kinds of solutions in Honeycomb Enterprise feel very difficult to build because I think it is an over-engineered product in terms of metrics. For traces, I have to give it to them. They have done an excellent job.

The only thing is the UI is not that great for Honeycomb Enterprise. That is something where it is not very appealing when I am looking into it. It is more of an engineered thing. I am an engineer and I understand this. You cannot make the UI very beautiful. However, if you use its peers, such as DataDog or Signoz, there are very interactive dashboards and other things. The dashboard is not that great, honestly, in Honeycomb Enterprise.

There is a cold start problem because we were using something called Lambda in AWS. The cold start issues basically occurred because we had not provided proper RAM to our Lambda function. Because of that, the Boto3 boot up was taking a lot of time. That was caught during these tracing sessions when we added the traces. We were able to identify that the Boto3 startup, the init part of Boto3, was taking more than twelve seconds or ten seconds to boot up. That is basically a very huge latency when it comes to a SaaS product.

Those are the major things. One thing they should do is discontinue Honeybee CLI. Right now, no one is using Honeycomb Enterprise like Honeybee CLI as per my understanding. Very few people are using it, and only POC kind of things are happening on this. People are shifting to OpenTelemetry. They should actually discontinue that product. It does not make much sense because they are trying to bring a dependency on people who are using that and they want to lock them into Honeycomb Enterprise because once the code is telemetered, it is very difficult to change it again.

However, now with cloud and everything we have, it is very easy to make those changes. There is no mode there. If there are engineers around it who are managing those things, they can actually shift to a better place where they can provide more value around it. Because people will eventually move to OpenTelemetry where they will set up their OpenTelemetry system, one OTel collector, and then OTel collector will send everything to Honeycomb Enterprise. People are moving out of these enterprise solutions mostly and they are using OpenTelemetry day in and day out. Most of the architects I talk to in terms of these things always talk about this.

Personally, I have also not used Honeycomb Enterprise CLI. I have read a lot of things and articles around it. They are actually maintaining it very well it seems, but I think they should discontinue it. That is just my opinion based on my observations. I may be wrong, I am not sure. This is what I have observed during communication channels, followed by the usage I have done, and followed by people I talk around. All have similar opinions.

I have been using this for almost three and a half years now.

I have not had any stability issues.

The setup is pretty easy. That is straightforward. I do not have any complaint there. The documentation is well written. I set up Honeycomb Enterprise traces just for a POC kind of thing within less than thirty to forty minutes just for a small code. The actual production code took one day, but that is because the code was long. That was easy. It was not very difficult. Things were very clear around it regarding how to generate the API keys to send those things and how to send it.

The load balancer API of Honeycomb Enterprise is extremely good. It accepts everything. I do not have any concerns there. I have never brought it down. I am not sure about the reliability aspect of it, but it accepts everything and I have never seen it down in my experience. I do not have any concerns there.

What we have done in terms of ROI using Honeycomb Enterprise, I still remember one use case where we reduced our overall latency because there was one Boto3 library which was not getting the expected performance.

Honeycomb Enterprise played a vital role in identifying the problems in the initial calls itself. That has actually saved us a lot of incidents. Previously, there were a lot of customer complaints coming in. I have taken one step at a time and I have added all these things in our code and followed by the customer complaints are almost ninety to ninety-two to ninety-three percent down as per the data I have.

Honeycomb Enterprise has actually helped us in various ways. The major complaints were around latency. We were able to switch our databases also because there was one problem that was identified by Honeycomb Enterprise where we were trying to fetch more than one megabyte of data from DynamoDB at one point in time. However, the problem was there is a hard limit set to DynamoDB to fetch these kinds of data.

That was a serious problem because customers were expecting data what they want to see on their screen. These kinds of issues were identified during our initial checks on DynamoDB and we were able to move to Snowflake. This has given us better solutioning around it.

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.

I am part of the performance engineering practice, and I lead the performance engineering practice at my current employer. We use Honeycomb Enterprise for tracing, which is application performance management in short. Our client has several APM tools such as Datadog, and in addition to Datadog, we only have the monitoring capacity of the counters. We do not have the agent-level monitoring which Honeycomb Enterprise is providing, where we can see the traces for each call being made by the software to trace where it is spending the time. For that gap, they have Honeycomb Enterprise in addition to Datadog. We use Honeycomb Enterprise for the same purpose, as Honeycomb hooks into our applications and tells us the traces where the request is spending the time.

We have Datadog here and often we get restrictions related to cardinality on Datadog because of their billing systems. They have limitations of cardinality, and that is the impact. That is how we can compare the impact.

Another thing I want to add here is that the team here had tried to use Honeycomb Enterprise earlier for tracing, but they faced issues. They could not get proper tracing with Honeycomb Enterprise at that time. That is what I have been given as feedback.

My main focus is on the tracing part. We have a microservices architecture with multiple microservices, and we want to see how when the request flows across multiple microservices, where the time gets spent.

We mostly look at the time the requests spend. Honeycomb Enterprise would capture a trace and span, and from there, we look at the time, the milliseconds or seconds that get spent at a particular request. That is what we look at and what we are interested in.

I did not get a chance to utilize the BubbleUp feature, and I think this is the most famous feature for SREs. We have not been able to utilize BubbleUp.

Dynatrace has this PurePath technology where they have the ability to give end-to-end tracing, which is a very robust feature. AppDynamics also has certain good features such as we could deploy that on our database systems and it would give an entire picture of our database, showing which application is creating how much load. From a pros perspective, Honeycomb Enterprise could be a better candidate with high cardinality; when there are too many unique values, Honeycomb Enterprise could be more beneficial there.

I have used better tools, I would say. I would not say that I prefer Honeycomb Enterprise as much. I have used Dynatrace, and I found it more comprehensive, and AppDynamics and other tools. These tools can also provide good information, but I find other tools better.

Most of the products, I would say, such as Dynatrace or AppDynamics or New Relic, are targeting this microservices market. I think Honeycomb Enterprise can have something very dedicated for microservices because there is an explosion in the migration from monolithic to microservices. If Honeycomb Enterprise can create a stable solution which is easy to use and which gives additional value and helps for faster debugging with microservices, they can certainly gain market share from others.

Tracing is already there. I just wish that these tools are a bit less cryptic. These tools sometimes get quite cryptic for new users. The less cryptic they can be made, that can help these tools. 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. That is what performance engineers and SREs need to see for each request, where it spent the entire time; how many other services or databases it interacted with and what took more or less time, and if there is a sequence, it should highlight that also. Was it parallel or if, for instance, a call to service A and then a call was made to a database, or a call to service A and a database were in parallel, that kind of information.

I have used Honeycomb Enterprise for around a year.

Another thing I want to add here is that the team here had tried to use Honeycomb Enterprise earlier for tracing, but they faced issues. They could not get proper tracing with Honeycomb Enterprise at that time. That is what I have been given as feedback. This was also one of the reasons the team is less interested in using Honeycomb Enterprise. Most recently, as far as I know, they started using an open-source tool now, Jaeger. They also brought in Jaeger because when I was using Honeycomb Enterprise, I got this feedback that they could not use Honeycomb Enterprise meaningfully in the team, although they had this license and everything. What I know is that it was crashing with the application. These tools, as they instrument into the application, they can sometimes lead to crashes. I had seen this with AppDynamics also, but that was for a different client. There, we could see AppDynamics did provide a fix for that. The team did follow up with AppDynamics. They had a wider setup of AppDynamics in the company, so they could not just get rid of that. A similar feedback I also got from the team that when they were trying to use Honeycomb Enterprise for tracing, it was causing issues.

As I said, there have been issues with Honeycomb Enterprise. The team had reported to me that at one point in time when they were trying to use Honeycomb Enterprise, they faced stability issues. It was interacting with the application and causing some crashes in the application.

I would rate the stability of Honeycomb Enterprise around five.

Frankly speaking, we did not use it at such a large scale here, so I cannot comment on that. The Datadog we use here is quite scalable. That is being used for at least eight thousand hosts. Given the cloud version and all, the scalability depends on our provider, the service provider who gave us the Honeycomb Enterprise license. That is something which is beyond my scope.

The setup sounds a bit complex. The overall setup at our client's end is really very complex because they have high security limitations, which makes it a bit difficult to use any of the tools.

When other organizations are evaluating their APM tools, I could name Honeycomb Enterprise as one of the APM tools, another addition. There are not that many APM tools, with the top ones being Dynatrace, AppDynamics, New Relic and a few others. I heard about Honeycomb Enterprise in my current project only; I did not hear about Honeycomb Enterprise before. Given the new technologies emerging, if Honeycomb Enterprise can provide better traceability and monitoring, they have a chance.

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.

My main use case for Honeycomb Enterprise is alerting and full stack tracing. I use Honeycomb Enterprise for tracking our traces from the front end all the way to our back end in Kotlin, and through Kafka and into Elastic. This helps us to spot any bottlenecks and see where errors occurred in our data processing pipeline.

We primarily use Honeycomb Enterprise to power our alerting, so we get alerts based on throughput, based on spikes, anomalies, and any error rates in each service. The best features Honeycomb Enterprise offers are alerting, which is mainly what we use it for.

Visualization is fine for me, although our dashboards are a bit cluttered. Primarily, the alerts are the most useful thing. The querying is good, although sometimes a bit messy. I would appreciate a better way to store my queries, perhaps with some auto-fill functionality. The alerts are the best feature.

Honeycomb Enterprise has positively impacted our organization by providing live alerts. We have been able to get alerts when something may pop up and see any issues in the system. We get alerts into Slack, and they work great. We see a lot of metrics go through into Slack, and they are really useful for keeping our team focused on only seeing one place to see alerts.

Honeycomb Enterprise can be improved by having a cleaner dashboard and a nicer way to search between insights.

I have been using Honeycomb Enterprise for one and a half years.

I do not have the metrics for a return on investment, but it has been very useful.

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 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.

Although Grit is a tool code code migration and management of technical debt for large chunks of work, we reviewed Grit from the use case of assisting in faster remediation of vulnerable libraries. We examined 3 areas and how we could use the synergy of Grit.io along with Snyk.io that helps overcome Snyk's limitations:

1. Deep scanning and reachability analysis

2. Management of auto-generated Pull Requests (PRs)

3. Reduction of false positives

I'm connected and had interactions with the founder Mr. Morgante Pell, while I designed a comprehensive synergistic solution, and I wrote a 35+ page technical paper on this topic.

Large organizations with hundreds of development teams and tens of thousands of code repositories face challenges in efficiently identifying and remediating potential vulnerabilities within third-party libraries across numerous projects. Manual scanning and updating is time-consuming, error-prone, and can lead to delays in addressing security risks.

While Grit.io is not primarily a vulnerability scanner, its pattern-matching and code transformation capabilities can be adapted for mass identification and remediation of vulnerable libraries.

For each of these 3 areas listed above, we examined how Grit.io's unique features can complement Snyk.io's capabilities, resulting in a more robust and efficient security scanning process. We realize This synergistic approach addresses the limitations of relying solely on Snyk.io, resulting in improved code security and reduced risk of overlooking critical vulnerabilities.

The limitations of security scanning tools like Snyk.io represent real challenges faced by development teams on a daily basis. These limitations can lead to:

- Missed vulnerabilities in complex code structures

- Overwhelming numbers of auto-generated PRs, causing developer fatigue

- High rates of false positives, leading to wasted time and resources

We considered implementing Grit into our pipelines to address these specific scenarios for code security, though Grit isn't a security tool:

- Custom Rules and Pattern Creation

- Remediation Pattern Creation

- Automated Code Updates

- Custom Pattern Recognition

- Pull Request Generation

- and others

1. Grit.io's flexibility allows for custom rules and patterns to identify vulnerable libraries, extending its use beyond traditional refactoring tasks.

2. Automated pull requests streamline the remediation process, facilitating efficient mass updates across multiple repositories.

3. While not a replacement for dedicated security tools, Grit.io can be a valuable addition to a large organization's security toolkit for vulnerability identification and remediation.

4. The approach offers significant benefits in terms of efficiency, consistency, and proactive security management, particularly valuable for organizations with large, distributed development teams.

I asked very specific questions to Mr. Pell about consideration of code security scenarios in pattern design and rules, specifically that tuned with OWASP Top 10. I believe addition of code security focus can be a value-add, though the way Grit architecture is designed and how it works, it is and may not become an alternative choice of code security solutions. Rather, it must be treated as a powerful supplementary tool that augments the existing code security solutions (such as Snyk or Checkmarx) in a DevSecOps or Secure DevOps environment.

Anyone interested in learning more on this front or have queries, can get in touch with me for a consulting.

Our internal comprehensive evaluation of Grit spans over 6 months to a year since our client organization considered Grit under the Accelerator program of promising AI startups back in Sep 2023. Different phases of the implementation have been conducted by various development architects spanning several scenarios. Our scenario was very specific to how Grit's AI-powered capabilities could be leveraged on code security remediations for a large tech ecosystem.

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.