Amazon CloudWatch is a native AWS monitoring tool for AWS programs. It provides data collection and resource monitoring capabilities.
$0
per canary run
GitLab
Score 8.6 out of 10
N/A
GitLab DevSecOps platform enables software innovation by aiming to empower development, security, and operations teams to build better software, faster. With GitLab, teams can create, deliver, and manage code quickly and continuously instead of managing disparate tools and scripts. GitLab helps teams across the complete DevSecOps lifecycle, from developing, securing, and deploying software. Differentiators, as described by Gitlab:
Simplicity: With GitLab, DevSecOps can…
$0
per month per user
Pricing
Amazon CloudWatch
GitLab
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
GitLab Essential
$0
per month per user
GitLab Premium
$29
per month per user
GitLab Ultimate
$99
per month per user
Offerings
Pricing Offerings
Amazon CloudWatch
GitLab
Free Trial
Yes
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
Yes
Yes
Entry-level Setup Fee
No setup fee
Optional
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.
For out business we find that AWS Cloudwatch is good at providing real-time metrics for monitoring and analysing the performance and usage of our platform by customers. It is possible to create custom metrics from log events, such people adding items to a basket, checking out or abandoning their orders.
GitLab is good if you work a lot with code and do complex repository actions. It gives you a very good overview of what were the states of your branches and the files in them at different stages in time. It's also way easier and more efficient to write pipelines for CI\CD. It's easier to read and it's easier to write them. It takes fewer clicks to achieve the same things with GitLab than it does for competitor products.
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.
Gitlab is the best in its segment. They have a free version, they have open-source software, they provide a good service with their SaaS product, they are a fully-remote company since the beginning (which means they are fully distributed and have forward-thinking IMO). I would certainly recommend them to everyone.
It's excellent at collecting logs. It's easy to set up. The viewing & querying part could be much better, though. The query syntax takes some time to get used to, & the examples are not helpful. Also, while being great, Log Insights requires manual picking of log streams to query across every time.
I find it easy to use, I haven't had to do the integration work, so that's why it is a 9/10, cause I can't speak to how easy that part was or the initial set up, but day to day use is great!
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.
At this point, I do not have much experience with Gitlab support as I have never had to engage them. They have documentation that is helpful, not quite as extensive as other documentation, but helpful nonetheless. They also seem to be relatively responsive on social media platforms (twitter) and really thrived when GitHub was acquired by Microsoft
Grafana is definitely a lot better and flexible in comparison with Amazon CloudWatch for visualisation, as it offers much more options and is versatile. VictoriaMetrics and Prometheus are time-series databases which can do almost everything cloudwatch can do in a better and cheaper way. Integrating Grafana with them will make it more capable Elasticsearch for log retention and querying will surpass cloudwatch log monitoring in both performance and speed
Gitlab seems more cutting-edge than GitHub; however, its AI tools are not yet as mature as those of CoPilot. It feels like the next-generation product, so as we selected a tool for our startup, we decided to invest in the disruptor in the space. While there are fewer out-of-the-box templates for Gitlab, we have never discovered a lack of feature parity.