Alternatives to AppDynamics logo

Alternatives to AppDynamics

Datadog, New Relic, Nagios, Splunk, and ELK are the most popular alternatives and competitors to AppDynamics.
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What is AppDynamics and what are its top alternatives?

AppDynamics is a performance monitoring and management tool that provides real-time insights into application performance and user experience. Its key features include application performance monitoring, code-level visibility, end-user monitoring, business transaction monitoring, and analytics. However, some limitations of AppDynamics include the complexity of setting up and configuring the tool, high pricing for small businesses, and potential performance overhead.

  1. Dynatrace: Dynatrace is a full-stack monitoring tool that offers AI-powered observability, automatic discovery of services, and root cause analysis. Pros include automatic topology discovery and dependency mapping, while cons include high pricing for small businesses.
  2. New Relic: New Relic is a cloud-based observability platform that provides full-stack monitoring, synthetic monitoring, and real user monitoring. Pros include ease of use and a wide range of integrations, while cons include complex pricing structure.
  3. Datadog: Datadog is a monitoring and analytics platform that offers infrastructure monitoring, application performance monitoring, and log management. Pros include customizable dashboards and powerful analytics, while cons include limited support for on-premises environments.
  4. SolarWinds AppOptics: SolarWinds AppOptics is a SaaS-based application performance monitoring tool that provides detailed insights into application performance and infrastructure monitoring. Pros include unified infrastructure and application monitoring, while cons include potential learning curve for beginners.
  5. Splunk: Splunk is a data analytics tool that offers log monitoring, infrastructure monitoring, and application performance monitoring capabilities. Pros include powerful search and analysis capabilities, while cons include high pricing and complexity.
  6. Riverbed SteelCentral: Riverbed SteelCentral is a network performance monitoring and diagnostics solution that provides end-to-end visibility into network and application performance. Pros include deep packet inspection capabilities, while cons include limited support for cloud environments.
  7. Instana: Instana is an AI-powered application performance monitoring tool that provides automatic monitoring and analysis of microservices and containerized applications. Pros include automatic distributed tracing and continuous monitoring, while cons include limited support for legacy systems.
  8. Stackify Retrace: Stackify Retrace is an APM tool that offers code-level performance insights, error tracking, and log management. Pros include easy setup and integration, while cons include limited support for complex enterprise environments.
  9. Raygun: Raygun is an error and crash reporting tool that provides real-time insights into application errors and performance bottlenecks. Pros include easy integration and detailed error diagnostics, while cons include limited monitoring capabilities compared to full-stack APM tools.
  10. Opsview: Opsview is an IT infrastructure monitoring tool that offers network monitoring, server monitoring, and cloud monitoring capabilities. Pros include comprehensive monitoring and alerting features, while cons include complexity in configuring advanced monitoring settings.

Top Alternatives to AppDynamics

  • Datadog
    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

    The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too. ...

  • Nagios
    Nagios

    Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License. ...

  • Splunk
    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • ELK
    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

    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
    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

    Jaeger, a Distributed Tracing System

AppDynamics alternatives & related posts

Datadog logo

Datadog

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PROS OF DATADOG
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    Monitoring for many apps (databases, web servers, etc)
  • 107
    Easy setup
  • 87
    Powerful ui
  • 84
    Powerful integrations
  • 70
    Great value
  • 54
    Great visualization
  • 46
    Events + metrics = clarity
  • 41
    Notifications
  • 41
    Custom metrics
  • 39
    Flexibility
  • 19
    Free & paid plans
  • 16
    Great customer support
  • 15
    Makes my life easier
  • 10
    Adapts automatically as i scale up
  • 9
    Easy setup and plugins
  • 8
    Super easy and powerful
  • 7
    In-context collaboration
  • 7
    AWS support
  • 6
    Rich in features
  • 5
    Docker support
  • 4
    Cute logo
  • 4
    Source control and bug tracking
  • 4
    Monitor almost everything
  • 4
    Cost
  • 4
    Full visibility of applications
  • 4
    Simple, powerful, great for infra
  • 4
    Easy to Analyze
  • 4
    Best than others
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    Automation tools
  • 3
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  • 3
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  • 3
    Good for Startups
  • 3
    Expensive
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    APM
CONS OF DATADOG
  • 19
    Expensive
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    No errors exception tracking
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    External Network Goes Down You Wont Be Logging
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    Complicated

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Noah Zoschke
Engineering Manager at Segment · | 30 upvotes · 272.1K views

We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. Behind the scenes the Config API is built with Go , GRPC and Envoy.

At Segment, we build new services in Go by default. The language is simple so new team members quickly ramp up on a codebase. The tool chain is fast so developers get immediate feedback when they break code, tests or integrations with other systems. The runtime is fast so it performs great at scale.

For the newest round of APIs we adopted the GRPC service #framework.

The Protocol Buffer service definition language makes it easy to design type-safe and consistent APIs, thanks to ecosystem tools like the Google API Design Guide for API standards, uber/prototool for formatting and linting .protos and lyft/protoc-gen-validate for defining field validations, and grpc-gateway for defining REST mapping.

With a well designed .proto, its easy to generate a Go server interface and a TypeScript client, providing type-safe RPC between languages.

For the API gateway and RPC we adopted the Envoy service proxy.

The internet-facing segmentapis.com endpoint is an Envoy front proxy that rate-limits and authenticates every request. It then transcodes a #REST / #JSON request to an upstream GRPC request. The upstream GRPC servers are running an Envoy sidecar configured for Datadog stats.

The result is API #security , #reliability and consistent #observability through Envoy configuration, not code.

We experimented with Swagger service definitions, but the spec is sprawling and the generated clients and server stubs leave a lot to be desired. GRPC and .proto and the Go implementation feels better designed and implemented. Thanks to the GRPC tooling and ecosystem you can generate Swagger from .protos, but it’s effectively impossible to go the other way.

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Robert Zuber

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.

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New Relic logo

New Relic

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New Relic is the industry’s largest and most comprehensive cloud-based observability platform.
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PROS OF NEW RELIC
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  • 344
    Really powerful
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    Awesome visualization
  • 194
    Ease of use
  • 151
    Great ui
  • 106
    Free tier
  • 80
    Great tool for insights
  • 66
    Heroku Integration
  • 55
    Market leader
  • 49
    Peace of mind
  • 21
    Push notifications
  • 20
    Email notifications
  • 17
    Heroku Add-on
  • 16
    Error Detection and Alerting
  • 13
    Multiple language support
  • 11
    SQL Analysis
  • 11
    Server Resources Monitoring
  • 9
    Transaction Tracing
  • 8
    Apdex Scores
  • 8
    Azure Add-on
  • 7
    Analysis of CPU, Disk, Memory, and Network
  • 7
    Detailed reports
  • 6
    Performance of External Services
  • 6
    Error Analysis
  • 6
    Application Availability Monitoring and Alerting
  • 6
    Application Response Times
  • 5
    Most Time Consuming Transactions
  • 5
    JVM Performance Analyzer (Java)
  • 4
    Browser Transaction Tracing
  • 4
    Top Database Operations
  • 4
    Easy to use
  • 3
    Application Map
  • 3
    Weekly Performance Email
  • 3
    Pagoda Box integration
  • 3
    Custom Dashboards
  • 2
    Easy to setup
  • 2
    Background Jobs Transaction Analysis
  • 2
    App Speed Index
  • 1
    Super Expensive
  • 1
    Team Collaboration Tools
  • 1
    Metric Data Retention
  • 1
    Metric Data Resolution
  • 1
    Worst Transactions by User Dissatisfaction
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    Real User Monitoring Overview
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    Real User Monitoring Analysis and Breakdown
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    Time Comparisons
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    Access to Performance Data API
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    Incident Detection and Alerting
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    Best of the best, what more can you ask for
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    Free
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    Proce
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    Price
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CONS OF NEW RELIC
  • 20
    Pricing model doesn't suit microservices
  • 10
    UI isn't great
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    Visualizations aren't very helpful
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    Hard to understand why things in your app are breaking

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Cooper Marcus
Director of Ecosystem at Kong Inc. · | 17 upvotes · 111.4K views
Shared insights
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at

I've used more and more of New Relic Insights here in my work at Kong. New Relic Insights is a "time series event database as a service" with a super-easy API for inserting custom events, and a flexible query language for building visualization widgets and dashboards.

I'm a big fan of New Relic Insights when I have data I know I need to analyze, but perhaps I'm not exactly sure how I want to analyze it in the future. For example, at Kong we recently wanted to get some understanding of our open source community's activity on our GitHub repos. I was able to quickly configure GitHub to send webhooks to Zapier , which in turn posted the JSON to New Relic Insights.

Insights is schema-less and configuration-less - just start posting JSON key value pairs, then start querying your data.

Within minutes, data was flowing from GitHub to Insights, and I was building widgets on my Insights dashboard to help my colleagues visualize the activity of our open source community.

#GitHubAnalytics #OpenSourceCommunityAnalytics #CommunityAnalytics #RepoAnalytics

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Julien DeFrance
Principal Software Engineer at Tophatter · | 16 upvotes · 3.2M views

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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Nagios logo

Nagios

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CONS OF NAGIOS
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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 4.5M views

    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...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

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    Splunk logo

    Splunk

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    CONS OF SPLUNK
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    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.

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    ELK logo

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    The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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    Grafana logo

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    • 10
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    Matt Menzenski
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    Conor Myhrvold
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    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...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

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    Azure Application Insights logo

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      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M views

      How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

      Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

      Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

      https://eng.uber.com/distributed-tracing/

      (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

      Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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