Amazon CloudWatch is a native AWS monitoring tool for AWS programs. It provides data collection and resource monitoring capabilities.
$0
per canary run
PagerDuty
Score 8.7 out of 10
N/A
PagerDuty is an IT alert and incident management application from the company of the same name in San Francisco.
$25
per month per user
Pricing
Amazon CloudWatch
PagerDuty
Editions & Modules
Canaries
$0.0012
per canary run
Logs - Analyze (Logs Insights queries)
$0.005
per GB of data scanned
Over 1,000,000 Metrics
$0.02
per month
Contributor Insights - Matched Log Events
$0.02
per month per one million log events that match the rule
Logs - Store (Archival)
$0.03
per GB
Next 750,000 Metrics
$0.05
per month
Next 240,000 Metrics
$0.10
per month
Alarm - Standard Resolution (60 Sec)
$0.10
per month per alarm metric
First 10,000 Metrics
$0.30
per month
Alarm - High Resolution (10 Sec)
$0.30
per month per alarm metric
Alarm - Composite
$0.50
per month per alarm
Logs - Collect (Data Ingestion)
$0.50
per GB
Contributor Insights
$0.50
per month per rule
Events - Custom
$1.00
per million events
Events - Cross-account
$1.00
per million events
CloudWatch RUM
$1
per 100k events
Dashboard
$3.00
per month per dashboard
CloudWatch Evidently - Events
$5
per 1 million events
CloudWatch Evidently - Analysis Units
$7.50
per 1 million analysis units
Professional
$25
per month per user
Business
$49
per month per user
Digital Operations
Contact Sales
Offerings
Pricing Offerings
Amazon CloudWatch
PagerDuty
Free Trial
Yes
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
Yes
Yes
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
With Amazon CloudWatch, there is no up-front commitment or minimum fee; you simply pay for what you use. You will be charged at the end of the month for your usage.
16% discount for annual pricing. AIOps Add-On available for $499 for 10k events per month. Add-On Runbook Automation for Incident Response available at $71 per user / per month.
Amazon CloudWatch is great in terms of the CloudWatch Logs feature, it integrates easily with other AWS services (CloudFormation, S3, Lambda, etc.) and is reasonably low cost, so it was a no-brainer for that area. For alerting, CloudWatch didn't offer much in the way of …
Compared to the companies listed above, PagerDuty was always the stronger contender combined with Datadog. Though now the playing field may have changed since we last did any serious evaluation. PagerDuty has no downtime and "just works". That last phrase is what makes it …
We also use CloudWatch for certain apps, but since it's locked in to AWS and its UI is much inferior to PagerDuty's, we prefer to have everything go to PagerDuty. It's really the only solution worth considering that I've come across for monitoring and alerting for production …
CloudWatch and New Relic are not exactly comparable, but it is possible to use them to create some alert. PagerDuty has more features, including on-call schedules and escalation policies.
Cloud watch is great and essential if you decide to invest in AWS and have any need to monitor the health of all aspects of your VPC resources, or at the organizational level (multiple accounts). Another benefit of the service is constant upgrades at no additional costs; the software evolves to develop modules and interface improvements. For first-time users in AWS, this is going to take a bit to understand, so the learning curve to this metrics environment can seem overwhelming at first glance/use.
I've used ICM in the past which has been a very Microsoft product with everything thrown into a blender. So, PD implementation is a breath of fresh air with focused pages to achieve the end goal. If the goal is to collaborate on issues across the organization, PD might not be the best solution but within specific teams, PD excels at it.
It provides lot many out of the box dashboard to observe the health and usage of your cloud deployments. Few examples are CPU usage, Disk read/write, Network in/out etc.
It is possible to stream CloudWatch log data to Amazon Elasticsearch to process them almost real time.
If you have setup your code pipeline and wants to see the status, CloudWatch really helps. It can trigger lambda function when certain cloudWatch event happens and lambda can store the data to S3 or Athena which Quicksight can represent.
Memory metrics on EC2 are not available on CloudWatch. Depending on workloads if we need visibility on memory metrics we use Solarwinds Orion with the agent installed. For scalable workloads, this involves customization of images being used.
Visualization out of the box. But this can easily be addressed with other solutions such as Grafana.
By design, this is only used for AWS workloads so depending on your environment cannot be used as an all in one solution for your monitoring.
When getting a phone call, PagerDuty doesn't seem to allow acknowledgments of alerts through the phone, which it says it does. I constantly receive a message that it was updated by another person - when in reality, it wasn't.
Smarter notifications. If an alert was snoozed for a time, when it comes back, it sends out another alert. It should, I think, send a message asking if the alert is still an issue and give the option to close.
The UI is more complex than I would like. Part of the challenge is that most users use PagerDuty infrequently; I don't remember how I changed a policy last time. Another part of the challenge is that some users expect alerting to be a trivial feature, and are reluctant to invest any time in reading the documentation.
Support is effective, and we were able to get any problems that we couldn't get solved through community discussion forums solved for us by the AWS support team. For example, we were assisted in one instance where we were not sure about the best metrics to use in order to optimize an auto-scaling group on EC2. The support team was able to look at our metrics and give a useful recommendation on which metrics to use.
PagerDuty is reliable and easy to set up. It gives an effective way to notify the team about critical incidents which results in a faster turnaround time on issues. users can customize their alerts rules based on their preferences. Overall it's effective and easy to use which adds great business value.
I believe that CloudWatch is a better solution to use with AWS services and resources in terms of cost and ease of integration with AWS infrastructure services. But keep in mind that Elasticsearch is better at aggregating application-level metrics. We chose CloudWatch because of its capabilities to integrate and monitor AWS services in almost real-time.
I have not use the 2 technologies for as long as I have used PagerDuty but in my opinion PagerDuty makes things a lot easier. The other tools got the job done and got alerts out but PagerDuty just seemed to make the setup for on-call alert schedules and integrations easier than the others. This isn't to say the others are difficult, just that PagerDuty was slightly better. I also have noticed that more tools have options to integrate to PagerDuty over the other tools.
We were able to set up log streaming, retention, and simple downtime alerts within a few hours, having no prior experience with CloudWatch, freeing up our engineers to focus on more important business goals.
CloudWatch log groups have made it relatively easy to detect and diagnose issues in production by allowing us to aggregate logs across servers, correlate failures, isolate misbehaving servers, etc. Thanks to CloudWatch, we are generally able to identify, understand and mitigate most production fires within 10-15 minutes.
Choosing CloudWatch to manage log aggregation has saved us quite a bit of time and money over the past year. Generally, 3rd-party log aggregation solutions tend to get quite expensive unless you self-host, in which case you typically need to spend a fair amount of time setting up, maintaining, and monitoring these services.
![Screenshot of Similar Incidents [Apple iPad], Open Incidents [iPhone 8], On-Call Schedule Menu [Apple Watch])](https://media.trustradius.com/product-screenshots/kf/M5/L6SIK3HGD31M.png)
