Alternatives to Amazon CloudWatch logo

Alternatives to Amazon CloudWatch

Datadog, Splunk, New Relic, Prometheus, and AWS CloudTrail are the most popular alternatives and competitors to Amazon CloudWatch.
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What is Amazon CloudWatch and what are its top alternatives?

It helps you gain system-wide visibility into resource utilization, application performance, and operational health. It retrieve your monitoring data, view graphs to help take automated action based on the state of your cloud environment.
Amazon CloudWatch is a tool in the Cloud Monitoring category of a tech stack.

Top Alternatives to Amazon CloudWatch

  • 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! ...

  • Splunk

    Splunk

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

  • 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. ...

  • Prometheus

    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • AWS CloudTrail

    AWS CloudTrail

    With CloudTrail, you can get a history of AWS API calls for your account, including API calls made via the AWS Management Console, AWS SDKs, command line tools, and higher-level AWS services (such as AWS CloudFormation). The AWS API call history produced by CloudTrail enables security analysis, resource change tracking, and compliance auditing. The recorded information includes the identity of the API caller, the time of the API call, the source IP address of the API caller, the request parameters, and the response elements returned by the AWS service. ...

  • Amazon Kinesis

    Amazon Kinesis

    Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data. ...

  • Stackdriver

    Stackdriver

    Google Stackdriver provides powerful monitoring, logging, and diagnostics. It equips you with insight into the health, performance, and availability of cloud-powered applications, enabling you to find and fix issues faster. ...

  • DigitalOcean Monitoring

    DigitalOcean Monitoring

    Collect metrics for visibility, monitor Droplet performance, and receive alerts when problems arise in your infrastructure – at no additional cost. ...

Amazon CloudWatch alternatives & related posts

Datadog logo

Datadog

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Unify logs, metrics, and traces from across your distributed infrastructure.
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PROS OF DATADOG
  • 134
    Monitoring for many apps (databases, web servers, etc)
  • 105
    Easy setup
  • 85
    Powerful ui
  • 82
    Powerful integrations
  • 69
    Great value
  • 53
    Great visualization
  • 45
    Events + metrics = clarity
  • 40
    Custom metrics
  • 40
    Notifications
  • 38
    Flexibility
  • 18
    Free & paid plans
  • 15
    Great customer support
  • 14
    Makes my life easier
  • 9
    Adapts automatically as i scale up
  • 8
    Easy setup and plugins
  • 7
    Super easy and powerful
  • 6
    In-context collaboration
  • 6
    AWS support
  • 5
    Rich in features
  • 4
    Cost
  • 4
    Docker support
  • 3
    Monitor almost everything
  • 3
    Full visibility of applications
  • 3
    Easy to Analyze
  • 3
    Expensive
  • 3
    Cute logo
  • 3
    Simple, powerful, great for infra
  • 3
    Source control and bug tracking
  • 3
    Best than others
  • 3
    Automation tools
  • 2
    Best in the field
  • 2
    Good for Startups
  • 2
    Free setup
CONS OF DATADOG
  • 16
    Expensive
  • 4
    No errors exception tracking
  • 2
    External Network Goes Down You Wont Be Logging
  • 1
    Complicated

related Datadog posts

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.

See more

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?

See more
Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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PROS OF SPLUNK
  • 2
    API for searching logs, running reports
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 1
    Ability to style search results into reports
  • 1
    Query any log as key-value pairs
  • 1
    Splunk language supports string, date manip, math, etc
  • 1
    Granular scheduling and time window support
  • 1
    Alert system based on custom query results
  • 1
    Custom log parsing as well as automatic parsing
  • 1
    Dashboarding on any log contents
  • 1
    Rich GUI for searching live logs
CONS OF SPLUNK
  • 1
    Splunk query language rich so lots to learn

related Splunk posts

Shared insights
on
KibanaKibanaSplunkSplunkGrafanaGrafana

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.

See more
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
  • 415
    Easy setup
  • 344
    Really powerful
  • 244
    Awesome visualization
  • 194
    Ease of use
  • 151
    Great ui
  • 107
    Free tier
  • 81
    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
  • 12
    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
  • 6
    Detailed reports
  • 6
    Performance of External Services
  • 6
    Error Analysis
  • 6
    Application Availability Monitoring and Alerting
  • 6
    Application Response Times
  • 5
    JVM Performance Analyzer (Java)
  • 5
    Most Time Consuming Transactions
  • 4
    Top Database Operations
  • 4
    Easy to use
  • 4
    Browser Transaction Tracing
  • 3
    Application Map
  • 3
    Pagoda Box integration
  • 3
    Custom Dashboards
  • 3
    Weekly Performance Email
  • 2
    Easy visibility
  • 2
    App Speed Index
  • 2
    Easy to setup
  • 1
    Real User Monitoring Analysis and Breakdown
  • 1
    Incident Detection and Alerting
  • 1
    Real User Monitoring Overview
  • 1
    Worst Transactions by User Dissatisfaction
  • 1
    Metric Data Resolution
  • 1
    Metric Data Retention
  • 1
    Team Collaboration Tools
  • 1
    Super Expensive
  • 1
    Time Comparisons
  • 1
    Access to Performance Data API
  • 1
    Background Jobs Transaction Analysis
  • 1
    Free
  • 1
    Best of the best, what more can you ask for
  • 1
    Best monitoring on the market
  • 1
    Rails integration
  • 0
    Exceptions
  • 0
    Ddd
CONS OF NEW RELIC
  • 19
    Pricing model doesn't suit microservices
  • 10
    UI isn't great
  • 7
    Visualizations aren't very helpful
  • 7
    Expensive
  • 5
    Hard to understand why things in your app are breaking

related New Relic posts

Farzeem Diamond Jiwani
Software Engineer at IVP · | 5 upvotes · 700.8K views

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!

See more
Sebastian Gębski

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).

See more
Prometheus logo

Prometheus

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An open-source service monitoring system and time series database, developed by SoundCloud
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PROS OF PROMETHEUS
  • 46
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 21
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring
CONS OF PROMETHEUS
  • 12
    Just for metrics
  • 6
    Bad UI
  • 6
    Needs monitoring to access metrics endpoints
  • 4
    Not easy to configure and use
  • 3
    Supports only active agents
  • 2
    Written in Go
  • 2
    Requires multiple applications and tools
  • 2
    TLS is quite difficult to understand

related Prometheus posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3M 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)

See more
Matt Menzenski
Senior Software Engineering Manager at PayIt · | 13 upvotes · 116.5K views

Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

See more
AWS CloudTrail logo

AWS CloudTrail

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Record AWS API calls for your account and have log files delivered to you
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PROS OF AWS CLOUDTRAIL
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    Very easy setup
  • 3
    Good integrations with 3rd party tools
  • 2
    Very powerful
  • 2
    Backup to S3
CONS OF AWS CLOUDTRAIL
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    related AWS CloudTrail posts

    Amazon Kinesis logo

    Amazon Kinesis

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    Store and process terabytes of data each hour from hundreds of thousands of sources
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    PROS OF AMAZON KINESIS
    • 7
      Scalable
    • 5
      Cons
    CONS OF AMAZON KINESIS
    • 2
      Cost

    related Amazon Kinesis posts

    John Kodumal

    As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

    We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

    See more
    Praveen Mooli
    Engineering Manager at Taylor and Francis · | 14 upvotes · 2.1M views

    We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

    To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

    To build #Webapps we decided to use Angular 2 with RxJS

    #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

    See more
    Stackdriver logo

    Stackdriver

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    Monitoring, logging, and diagnostics for applications on Google Cloud Platform and AWS
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    PROS OF STACKDRIVER
    • 19
      Monitoring
    • 11
      Logging
    • 8
      Alerting
    • 7
      Tracing
    • 6
      Uptime Monitoring
    • 5
      Error Reporting
    • 4
      Multi-cloud
    • 3
      Production debugger
    • 2
      Many integrations
    • 1
      Configured basically with GAE
    • 1
      Backed by Google
    CONS OF STACKDRIVER
    • 2
      Not free

    related Stackdriver posts

    DigitalOcean Monitoring logo

    DigitalOcean Monitoring

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    Seamless Infrastructure Monitoring
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    PROS OF DIGITALOCEAN MONITORING
    • 3
      Easy, no fuss and great documentation
    CONS OF DIGITALOCEAN MONITORING
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      related DigitalOcean Monitoring posts