What is AppDynamics and what are its top alternatives?
Top Alternatives to AppDynamics
Datadog
Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog! ...
New Relic
New Relic is the all-in-one web application performance tool that lets you see performance from the end user experience, through servers, and down to the line of application code. ...
Nagios
Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License. ...
Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...
ELK
It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch. ...
Grafana
Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...
Azure Application Insights
It is an extensible Application Performance Management service for developers and DevOps professionals. Use it to monitor your live applications. It will automatically detect performance anomalies, and includes powerful analytics tools. ...
Jaeger
Jaeger, a Distributed Tracing System
AppDynamics alternatives & related posts
Datadog
- Monitoring for many apps (databases, web servers, etc)130
- Easy setup103
- Powerful ui83
- Powerful integrations80
- Great value66
- Great visualization50
- Events + metrics = clarity41
- Custom metrics39
- Notifications38
- Flexibility36
- Free & paid plans16
- Great customer support13
- Makes my life easier12
- Easy setup and plugins7
- Adapts automatically as i scale up6
- Super easy and powerful5
- In-context collaboration4
- AWS support4
- Rich in features3
- Best than others2
- Cost2
- Docker support2
- Free setup1
- Easy to Analyze1
- Monitor almost everything1
- Automation tools1
- Source control and bug tracking1
- Full visibility of applications1
- Expensive1
- Cute logo1
- Simple, powerful, great for infra1
- Expensive12
- No errors exception tracking2
- Complicated1
related Datadog posts
Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.
We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.









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?
New Relic
- Easy setup416
- Really powerful345
- Awesome visualization244
- Ease of use194
- Great ui152
- Free tier107
- Great tool for insights81
- Heroku Integration66
- Market leader55
- Peace of mind49
- Push notifications21
- Email notifications20
- Heroku Add-on16
- Error Detection and Alerting16
- Multiple language support12
- Server Resources Monitoring11
- SQL Analysis11
- Transaction Tracing9
- Azure Add-on8
- Apdex Scores8
- Analysis of CPU, Disk, Memory, and Network7
- Application Response Times6
- Detailed reports6
- Performance of External Services6
- Error Analysis6
- Application Availability Monitoring and Alerting6
- Most Time Consuming Transactions5
- JVM Performance Analyzer (Java)5
- Easy to use4
- Browser Transaction Tracing4
- Top Database Operations4
- Pagoda Box integration3
- Custom Dashboards3
- Weekly Performance Email3
- Application Map3
- Easy to setup2
- App Speed Index2
- Easy visibility2
- Metric Data Retention1
- Team Collaboration Tools1
- Worst Transactions by User Dissatisfaction1
- Real User Monitoring Analysis and Breakdown1
- Time Comparisons1
- Access to Performance Data API1
- Metric Data Resolution1
- Background Jobs Transaction Analysis1
- Incident Detection and Alerting1
- Real User Monitoring Overview1
- Best of the best, what more can you ask for1
- Best monitoring on the market1
- Rails integration1
- Free1
- Super Expensive1
- Exceptions0
- Pricing model doesn't suit microservices18
- UI isn't great10
- Expensive7
- Visualizations aren't very helpful7
- Hard to understand why things in your app are breaking5
related New Relic posts









Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.
Current Environment: .NET Core Web app hosted on Microsoft IIS
Future Environment: Web app will be hosted on Microsoft Azure
Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server
Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.
Please advise on the above. Thanks!
Regarding Continuous Integration - we've started with something very easy to set up - CircleCI , but with time we're adding more & more complex pipelines - we use Jenkins to configure & run those. It's much more effort, but at some point we had to pay for the flexibility we expected. Our source code version control is Git (which probably doesn't require a rationale these days) and we keep repos in GitHub - since the very beginning & we never considered moving out. Our primary monitoring these days is in New Relic (Ruby & SPA apps) and AppSignal (Elixir apps) - we're considering unifying it in New Relic , but this will require some improvements in Elixir app observability. For error reporting we use Sentry (a very popular choice in this class) & we collect our distributed logs using Logentries (to avoid semi-manual handling here).
Nagios
- It just works53
- The standard28
- Customizable12
- The Most flexible monitoring system8
- Huge stack of free checks/plugins to choose from1
related Nagios posts
Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:
By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.
To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...
(GitHub : https://github.com/m3db/m3)
- Ability to style search results into reports1
- API for searching logs, running reports1
- Query any log as key-value pairs1
- Splunk language supports string, date manip, math, etc1
- Granular scheduling and time window support1
- Alert system based on custom query results1
- Query engine supports joining, aggregation, stats, etc1
- Custom log parsing as well as automatic parsing1
- Dashboarding on any log contents1
- Rich GUI for searching live logs1
- Splunk query language rich so lots to learn1
related Splunk posts
I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.
- Open source8
- Elastic Search is a resource hog3
- Logstash configuration is a pain3
related ELK posts
Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx
- Beautiful79
- Graphs are interactive64
- Easy52
- Free50
- Nicer than the Graphite web interface33
- Many integrations23
- Can build dashboards14
- Easy to specify time window9
- Can collaborate on dashboards8
- Dashboards contain number tiles8
- Open Source5
- Integration with InfluxDB4
- Authentification and users management4
- Click and drag to zoom in4
- Threshold limits in graphs3
- Alerts2
- Great community support2
- It is open to cloud watch and many database2
- You can visualize real time data to put alerts1
- You can use this for development to check memcache1
- Simple and native support to Prometheus1
- Plugin visualizationa0
- Grapsh as code0
related Grafana posts
Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:
By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.
To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...
(GitHub : https://github.com/m3db/m3)
For our Predictive Analytics platform, we have used both Grafana and Kibana
- Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
- Kibana based reporting screenshot: https://imgur.com/vuVvZKN
Kibana has predictions
and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).
For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:
- Creating and organizing visualization panels
- Templating the panels on dashboards for repetetive tasks
- Realtime monitoring, filtering of charts based on conditions and variables
- Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
Azure Application Insights
- Focus in detect performance anomalies and issues4
- Integrated with Azure3
- Live Metrics1
- User flow1
- Availability tests (Heart Beat check)1
related Azure Application Insights posts
- Open Source3
- CNCF Project2
- Feature Rich UI1
- Easy to install1