Amazon CloudWatch is a native AWS monitoring tool for AWS programs. It provides data collection and resource monitoring capabilities.
$0
per canary run
Datadog
Score 8.6 out of 10
N/A
Datadog is a monitoring service for IT, Dev and Ops teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.
$18
per month per host
IBM Turbonomic
Score 8.8 out of 10
Enterprise companies (1,001+ employees)
IBM Turbonomic is a performance and cost optimization platform for public, private, and hybrid clouds used by cloud, infrastructure operations, and architecture to assure application performance while eliminating inefficiencies by dynamically resourcing applications through automated actions. One of the key features of IBM Turbonomic is its ability to continuously adjust application resources in real time. By monitoring resource utilization and application performance,…
N/A
Pricing
Amazon CloudWatch
Datadog
IBM Turbonomic
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
Log Management
$1.27
per month (billed annually) per host
Infrastructure
$15.00
per month (billed annually) per host
Standard
$18
per month per host
Enterprise
$27
per month per host
DevSecOps Pro
$27
per month per host
APM
$31.00
per month (billed annually) per host
DevSecOps Enterprise
$41
per month per host
IBM® Turbonomic On-Prem
Varies - Request a Quote
per month IBM Turbonomic On-prem optimizes data center resources in real time, ensuring app performance at the lowest cost by aligning infrastructure supply with dynamic application demand.
IBM® Turbonomic Cloud Standard
Varies - Request a Quote
per month For customers with more than USD 1.6 million in annual cloud spend or 50 Managed Virtual Servers (MVS) or greater
IBM® Turbonomic Hybrid Standard
Varies - Request a Quote
per month Advanced hybrid cloud optimization capabilities for customers with 200 managed virtual servers (MVS) or more
Offerings
Pricing Offerings
Amazon CloudWatch
Datadog
IBM Turbonomic
Free Trial
Yes
Yes
Yes
Free/Freemium Version
Yes
Yes
No
Premium Consulting/Integration Services
Yes
No
Yes
Entry-level Setup Fee
No setup fee
Optional
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.
Discount available for annual pricing. Multi-Year/Volume discounts available (500+ hosts/mo).
CloudWatch's log search features are impoverished compared to PaperTrail's or Loggly's. However, CloudWatch aggregates logs from Lambda, ECS, API Gateway and more out-of-the-box. You do not need to manage anything. You do not need to worry about an errant logging configuration …
As CloudWatch is integrated into AWS already, its ready to go. External products such as Nagios require a fair bit of work to actually get the metrics into the dashboards. Products like SolarWinds and Datadog provide quite a high level of very easy integration which allows for …
Amazon CloudWatch is fully integrated into your existing AWS account, and provides easy hooks into several different services to make a cohesive infrastructure. Unfortunately, using other services will not allow you to get into the weeds to do everything Amazon CloudWatch can …
I feel that CloudWatch will always remain the backbone of log analytics, events, and alarms. However, we can use other products in conjunction with it for better log analytics and monitoring. In my organization, we also ingest logs from CloudWatch to Splunk and ELK. This way we …
First think first - it's easy to use, and very easy to implement in any infrastructure. It provides a custom dashboard and monitors. I’ve used or evaluated Grafana, Prometheus, Amazon CloudWatch, and Dynatrace, and each tool has strong capabilities. Prometheus + Grafana provide …
Datadog is significantly more user-friendly than CloudWatch.In terms of capabilities, they're similar. I would not call either of the best-in-class for any single feature, but Datadog feels more polished and ready to use overall.Multi-cloud monitoring is a clear differentiator …
Datadog seems to be the most feature-rich of all the alternatives we've considered, however due to problems outlined earlier, some of the others have benefits. OpenTel can give us a way to make our platforms compatible with a variety of vendors, and can be done without …
Datadog is a more complex but complete solution than any of the other Log Aggregation, monitoring, or general observabilty tools that we have trialed. I found it easier to setup following useful and up-to-date documentation provided directly by Datadog instead of scattered …
UI of the Datadog is easy to understand and integration steps are easy to understand. It also provides the troubleshooting steps which are easy to understand. Supports multi cloud integrations which is very important for all the customers to know about the cloud service's …
Verified User
Engineer
Chose Datadog
I selected Datadog because of its features and the wide range of integration support. As I already told it supports more that 600+ integrations which helps and organization to keep everything in a single place and also its AI feature which is reducing the time for root cause …
I think IBM Turbonomic is much much better than Datadog. Both in terms of functionality and capabilities. AI driven resource allocation is the standout feature.
IBM Turbonomic supports multi cloud deployments and work well with other IBM cloud/web tools we use within our organization. While there is an initial learning curve, over long period of time it pays off once the cost benefits are realized from the efficiency gains. Given the …
Turbonomic is class apart compared to the others. It helps you achieve what is not possible with the other solutions. Turbonomic can work in an auto-mode and keep delivering savings and business performance. No other product comes even close.
As the organization had experience of years in using IBM products, we had the confidence that they will provide us with great support. And we needed a reliable solution as a financial institute to ensure continuous operations. Even though the price was very high, we made the …
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.
Datadog may be better suited for teams that have a more out-of-the-box infrastructure, on the primary platforms Datadog supports. You may also have better results if you have a bigger team dedicated to devops and/or a bigger budget. We found that trying to adapt it to our use case (small team, .NET on AWS Fargate) wasn't feasible. We continually ran into roadblocks that required us to dig through documentation (and at times, having to figure out some documentation was wrong), go back and forth with support, and in my opinion, waste money on excessive and unintended usages due to opaque pricing models and inaccurate usage reports, as well as broken/non-functional rate sampling controls.
Datacenter Consolidation and Hardware Optimization: This scenario is relevant to you as a hardware manager. It applies when you have physical servers (like Power or System z) and want to maximize virtual machine density. Why it works: IBM Turbonomic analyzes the peak usage times of each VM. If VM "A" is active during the day and VM "B" at night, it places them on the same physical host. Ideal scenario: Data migration projects or when you're told, "[...], there's no budget for more servers this year, make everything fit on what we have." Consolidación de Datacenters y Optimización de Hardware,Este escenario te toca de cerca como encargado de Hardware. Cuando tienes servidores físicos (como los Power o System z) y quieres maximizar la densidad de máquinas virtuales.Por qué funciona: IBM Turbonomic analiza las horas pico de cada VM. Si la VM "A" es activa de día y la VM "B" de noche, las coloca en el mismo host físico.Escenario ideal: Proyectos de migración de datos o cuando te dicen: "[...], no hay presupuesto para más servidores este año, haz que quepa todo en lo que tenemos". This review was originally written in Spanish and has been translated into English using a third-party translation tool. While we strive for accuracy, some nuances or meanings may not be perfectly captured.
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.
The thing which Datadog does really well, one of them are its broad range of services integrations and features which makes it one step observability solution for all. We can monitor all types of our application, infrastructure, hosts, databases etc with Datadog.
Its custom dashboard feature which helps us to visualize the data in a better way . It supports different types of charts through those charts we can create our dashboard more attractive.
Its AI powered alerting capability though that we can easily identify the root cause and also it has a low noise alerting capability which means it correlated the similar type of issues.
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.
Alert windows cause lag in notifications (e.g. if the alert window is X errors in 1 hour, we won't get alerted until the end of the 1 hour range)
I would appreciate more supportive examples for how to filter and view metrics in the explorer
I would like a more clear interface for metrics that are missing in a time frame, rather than only showing tags/etc. for metrics that were collected within the currently viewed time frame
It would be nice if the UI included a break-down of features that are both licensed as well as un-licensed. That way, you could not only see what you have, but what you don't.
The right-sizing recommendations are great, but very little info is given about why the recommendation is being made. More info would not only increase understanding, but would also help drive decision-making.
We are certainly happy with Turbonomic as a whole and have invested quite a bit of time and effort into learning the ins and outs of the product. We have our reporting setup the way we want it and have gained definite value from these features. I will say though that many products nowadays are offering more native monitoring, reporting, and alerting features which may eventually steer us away from this product
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.
There are so many features that it can be hard to figure out where you need to go for your own use case. For example, RUM monitoring us buried in a "Digital Experience" sidebar setting when this is one of our key use cases that I sometimes struggle to find in the application. It appears that ECS + Fargate monitoring was recently released which is great because we had to build a lambda reporting solution for ephemeral task monitoring. But this new feature was never on my radar until I starting clicking around the application.
Excellent approach to larger VM organizational management. They have an very clean integrated dashboard that allows us to see everything in our environment and what that is doing in real-time. It works on multiple hyper-visors really well and integrates capacity planning on my local site as well as my cloud locations.
It allocates resources among applications by showing more on the cost breakdown by cloud service, with metrics on cloud provider information like Azure Management, Identity, Networking, Storage with costs per day, and total services costs. This then could facilitate and show the corresponding actions thereafter upon scaling.
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.
The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.
When I contact support I get a quick response and they are able to solve my problem quickly. I also get a sense that they want to make sure that we are getting value from the product and walk me through whatever steps are needed to accomplish my goals.
Alex (from VMTurbo) has worked with the product for years and helped develop the product. He was very knowledgeable and was able to provide our support team with details knowledge on how to get our deployment configured correctly as well as help with another VMTurbo POC within another customers environment.
After buying VMTurbo Operations Manager, I was invited to an online user training event. I felt this training was effective and dug just deep enough to be informative yet still keep my attention. Additionally, the webinar was free.
The implementation was very simple. Just upload an OVA file and power on the VM. Once it comes up enter some networking information and you can then access the web interface. From there, just begin configuring the system for your environment by adding you license and the various virtual environments and storage through the inventory tab
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
Our logs are very important, and Datadog manages them exceptionally well. We frequently use Datadog services for our investigations. Use case: Monitor your apps, infrastructure, APIs, and user experience.
Key features:
Logs, metrics, and APM (Application Performance Monitoring)
Real-time alerting and dashboards
Supports Kubernetes, AWS, GCP, and other integrations
RUM (Real User Monitoring) and Synthetics
✅ Best for backend, server, and distributed systems monitoring.
As the organization had experience of years in using IBM products, we had the confidence that they will provide us with great support. And we needed a reliable solution as a financial institute to ensure continuous operations. Even though the price was very high, we made the correct decision to go ahead with IBM Turbonomic as the feedback from existing users in the region was very positive. We needed a solution which was capable of handling our automation requirements. All these were green in IBM Turbonomic.
Professional services were always there to guide us in our transformation to the cloud. They understood our business model and then were able to provide guidance on what we needed from the tool.
Application performance has been a big one. With Turbonomic keeping everything running at top performance, it can make changes when extra resources are need, quicker than somebody being notified and then making the necessary changes.
Turbonomic has been a great cost savings for us on multiple occasions. We use it every time we are improving servers.
With the planning feature we get the best performance form new hardware purchases
