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Honeycomb vs New Relic: What are the differences?
Key Differences between Honeycomb and New Relic
Data Sampling: One major difference between Honeycomb and New Relic is their approach to data sampling. Honeycomb offers full-fidelity event data, allowing users to capture and analyze every event in their system, resulting in accurate insights. On the other hand, New Relic employs sampling techniques which reduce the volume of data collected and may lead to the loss of important details.
Query Flexibility: Honeycomb provides a more flexible querying experience compared to New Relic. With Honeycomb, users can refine their queries using boolean operators, nested conditions, and regular expressions, enabling them to investigate specific events and behaviors effectively. In contrast, New Relic's querying capabilities are relatively limited, restricting users from performing complex filtering and analysis.
Distributed Tracing: Honeycomb has a strong focus on distributed tracing, allowing users to gain visibility into the entire lifecycle of a request as it flows through various microservices and components. This tracing capability enables pinpointing performance bottlenecks and diagnosing issues in a distributed system. New Relic offers some distributed tracing features but may not be as comprehensive as Honeycomb.
Real-time Analysis: Honeycomb provides real-time analysis and visualization of event data, allowing users to monitor and react to changes in their system in real-time. New Relic, on the other hand, may have some latency in data processing, which can result in some delay in getting up-to-date insights.
User Interface: The user interface (UI) of Honeycomb has a more intuitive and user-friendly design compared to New Relic. Honeycomb's UI provides clear navigation, intuitive visualizations, and easy-to-use query builders, making it easier for users to explore and understand their data. New Relic's UI, while functional, may be slightly less user-friendly and may require more effort to navigate and utilize effectively.
Pricing: Honeycomb and New Relic have different pricing models. Honeycomb follows a pay-as-you-go model, where users pay based on the volume of data processed. In contrast, New Relic has various pricing tiers based on the features and capabilities required, with different pricing structures for different levels of access and usage.
In summary, Honeycomb offers full-fidelity event data, flexible querying, comprehensive distributed tracing, real-time analysis, user-friendly UI, and a pay-as-you-go pricing model, while New Relic may employ data sampling, have limited querying capabilities, offer less comprehensive distributed tracing, may have some latency in analysis, have a slightly less user-friendly UI, and follows a tiered pricing structure.
We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.
We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.
We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.
You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?
Can't say anything to Sysdig. I clearly prefer Datadog as
- they provide plenty of easy to "switch-on" plugins for various technologies (incl. most of AWS)
- easy to code (python) agent plugins / api for own metrics
- brillant dashboarding / alarms with many customization options
- pricing is OK, there are cheaper options for specific use cases but if you want superior dashboarding / alarms I haven't seen a good competitor (despite your own Prometheus / Grafana / Kibana dog food)
IMHO NewRelic is "promising since years" ;) good ideas but bad integration between their products. Their Dashboard query language is really nice but lacks critical functions like multiple data sets or advanced calculations. Needless to say you get all of that with Datadog.
Need help setting up a monitoring / logging / alarm infrastructure? Send me a message!
Hi Medeti,
you are right. Building based on your stack something with open source is heavy lifting. A lot of people I know start with such a set-up, but quickly run into frustration as they need to dedicated their best people to build a monitoring which is doing the job in a professional way.
As you are microservice focussed and are looking for 'low implementation and maintenance effort', you might want to have a look at INSTANA, which was built with modern tool stacks in mind. https://www.instana.com/apm-for-microservices/
We have a public sand-box available if you just want to have a look at the product once and of course also a free-trial: https://www.instana.com/getting-started-with-apm/
Let me know if you need anything on top.
I have hands on production experience both with New Relic and Datadog. I personally prefer Datadog over NewRelic because of the UI, the Documentation and the overall user/developer experience.
NewRelic however, can do basically the same things as Datadog can, and some of the features like alerting have been present in NewRelic for longer than in Datadog. The cool thing about NewRelic is their last-summer-updated pricing: you no longer pay per host but after data you send towards New Relic. This can be a huge cost saver depending on your particular setup
I'd go for Datadog, but given you have lots of containers I would also make a cost calculation. If the price difference is significant and there's a budget constraint NewRelic might be the better choice.
Coming from a Ruby background, we've been users of New Relic for quite some time. When we adopted Elixir, the New Relic integration was young and missing essential features, so we gave AppSignal a try. It worked for quite some time, we even implemented a :telemetry reporter for AppSignal . But it was difficult to correlate data in two monitoring solutions, New Relic was undergoing a UI overhaul which made it difficult to use, and AppSignal was missing the flexibility we needed. We had some fans of Datadog, so we gave it a try and it worked out perfectly. Datadog works great with Ruby , Elixir , JavaScript , and has powerful features our engineers love to use (notebooks, dashboards, very flexible alerting). Cherry on top - thanks to the Datadog Terraform provider everything is written as code, allowing us to collaborate on our Datadog setup.
I haven't heard much about Datadog until about a year ago. Ironically, the NewRelic sales person who I had a series of trainings with was trash talking about Datadog a lot. That drew my attention to Datadog and I gave it a try at another client project where we needed log handling, dashboards and alerting.
In 2019, Datadog was already offering log management and from that perspective, it was ahead of NewRelic. Other than that, from my perspective, the two tools are offering a very-very similar set of tools. Therefore I wouldn't say there's a significant difference between the two, the decision is likely a matter of taste. The pricing is also very similar.
The reasons why we chose Datadog over NewRelic were:
- The presence of log handling feature (since then, logging is GA at NewRelic as well since falls 2019).
- The setup was easier even though I already had experience with NewRelic, including participation in NewRelic trainings.
- The UI of Datadog is more compact and my experience is smoother.
- The NewRelic UI is very fragmented and New Relic One is just increasing this experience for me.
- The log feature of Datadog is very well designed, I find very useful the tagging logs with services. The log filtering is also very awesome.
Bottom line is that both tools are great and it makes sense to discover both and making the decision based on your use case. In our case, Datadog was the clear winner due to its UI, ease of setup and the awesome logging and alerting features.
I chose Datadog APM because the much better APM insights it provides (flamegraph, percentiles by default).
The drawbacks of this decision are we had to move our production monitoring to TimescaleDB + Telegraf instead of NR Insight
NewRelic is definitely easier when starting out. Agent is only a lib and doesn't require a daemon
Pros of Honeycomb
- Powerful UI2
- High-Cardinality Data2
- BubbleUp + Heat maps2
- Better Value1
Pros of New Relic
- Easy setup415
- Really powerful344
- Awesome visualization244
- Ease of use194
- Great ui151
- Free tier107
- Great tool for insights80
- Heroku Integration66
- Market leader55
- Peace of mind49
- Push notifications21
- Email notifications20
- Heroku Add-on17
- Error Detection and Alerting16
- Multiple language support13
- Server Resources Monitoring11
- SQL Analysis11
- Transaction Tracing9
- Azure Add-on8
- Apdex Scores8
- Detailed reports7
- Analysis of CPU, Disk, Memory, and Network7
- Application Response Times6
- Performance of External Services6
- Application Availability Monitoring and Alerting6
- Error Analysis6
- JVM Performance Analyzer (Java)5
- Most Time Consuming Transactions5
- Top Database Operations4
- Easy to use4
- Browser Transaction Tracing4
- Application Map3
- Weekly Performance Email3
- Custom Dashboards3
- Pagoda Box integration3
- App Speed Index2
- Easy to setup2
- Background Jobs Transaction Analysis2
- Time Comparisons1
- Access to Performance Data API1
- Super Expensive1
- Team Collaboration Tools1
- Metric Data Retention1
- Metric Data Resolution1
- Worst Transactions by User Dissatisfaction1
- Real User Monitoring Overview1
- Real User Monitoring Analysis and Breakdown1
- Free1
- Best of the best, what more can you ask for1
- Best monitoring on the market1
- Rails integration1
- Incident Detection and Alerting1
- Cost0
- Exceptions0
- Price0
- Proce0
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Cons of Honeycomb
Cons of New Relic
- Pricing model doesn't suit microservices20
- UI isn't great10
- Expensive7
- Visualizations aren't very helpful7
- Hard to understand why things in your app are breaking5
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