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
Palo Alto Networks WildFire
Score 9.3 out of 10
N/A
Palo Alto Network’s WildFire is a malware prevention service. It specializes in addressing zero-day threats through dynamic and static analysis, machine learning, and advanced sandbox testing environments.
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Pricing
Datadog
Palo Alto Networks WildFire
Editions & Modules
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
No answers on this topic
Offerings
Pricing Offerings
Datadog
Palo Alto Networks WildFire
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
Optional
No setup fee
Additional Details
Discount available for annual pricing. Multi-Year/Volume discounts available (500+ hosts/mo).
As per my experience, Datadog is best suited for complex, cloud-native environments where unified observability is critical, as it integrates seamlessly with AWS and Azure. Moreover, it provides deep visibility into latency and error rates. Datadog pricing is less appropriate for Startups with a tight budget and for organizations needing advanced incident management.
Palo Alto Networks WildFire is highly effective in enterprise environments where detecting zero-day threats and unknown malware is critical. Small businesses may find the cost of advanced subscriptions prohibitive, especially if they only need basic protection. Much of our infrastructure is OT and Palo Alto Networks WildFire is cloud dependent so cannot be used where we have air-gapped systems.
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.
This is could base and easily manageable for our collocation. While working within the could can review in live time potential treats that it has reported from other devices.
Worked very well with existing Palo Alto devices.
Another huge plus is the simplicity of managing and ease of scalability.
Its cost is competitive with similar/like products available.
It works very well and takes care of protecting us from threats new and well-known. It's been a game changer in terms of threat detection & prevention.
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.
It is a great product that has definitely improved our security posture, however it does require quite a bit of training and time spent customizing for the environment. We had several difficulties in deployment but Palo Alto support was able to help us work through the problems that we were not able to figure out on our own.
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.
PAN support is very good. You can get the reasonable and timely support on any conditions. When the product is already integrated with the PAN firewalls, you can choose the severity levels based on the effect. The customer service/TAC is very helpful, they even have additional recommendations of advises for product usability. Local partners are also assisting the cases and give their expertise.
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.
WildFire from Palo Alto Networks provides security with very little overhead. With AutoFocus, they’ve got threat intelligence built right in. That way, it can prepare us to react swiftly when a significant danger is identified and dealt with as soon as possible. They introduced firewalls that are aware of applications and can make use of Wildfire. It sped our ability to respond to emerging threats up because of this game-changing development.
We've had one or two malware files that were blocked by Wildfire. We use it occasionally to check unusual or unexpected files. Hard to monetize ROI, because we don't know what the impact would have been if the file made it through.
We pay significantly for the Wildfire licenses, but given the potential impact to our business, we feel it is worthwhile. Figure costs are somewhere around $1,500 per year per firewall for a mid-range model. Can be higher or lower for different sized firewalls. Onsite appliance was somewhere between $50-100K, which was too much for us, so we use the cloud model.
