

Find out in this report how the two AI Observability solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
We have automated complete order-to-cash processes for multiple customers, saving over 90 million dollars.
The return is faster since development efforts are minimized, allowing for quicker integration delivery.
Previously we had thirteen contractors doing the monitoring for us, which is now reduced to only five.
Datadog has delivered more than its value through reduced downtime, faster recovery, and infrastructure optimization.
I believe features that would provide a lot of time savings, just enabling you to really narrow down and filter the type of frustration or user interaction that you're looking for.
Seamlessly meeting SLAs and providing excellent responses to challenges and issues related to interface and data connectivity.
The tech support is very good, offering immediate responses and chat options.
We receive service that is more than adequate, even exceptional.
When I have additional questions, the ticket is updated with actual recommendations or suggestions pointing me in the correct direction.
Overall, the entire Datadog comprehensive experience of support, onboarding, getting everything in there, and having a good line of feedback has been exceptional.
I've had a couple instances where I reached out to Datadog's support team, and they have been really super helpful and very kind, even reaching back out after resolving my issues to check if everything's going well.
The reason we wanted to use our own Kubernetes cluster was to do automatic scaling for utilized resources, allowing us to save.
We have built approximately 1,100 interfaces for one customer, which is a significant achievement.
Datadog's scalability has been great as it has been able to grow with our needs.
We did, as a trial, engage the AWS integration, and immediately it found all of our AWS resources and presented them to us.
Datadog's scalability is strong; we've continued to significantly grow our software, and there are processes in place to ensure that as new servers, realms, and environments are introduced, we're able to include them all in Datadog without noticing any performance issues.
Datadog is very stable, as there hasn't been any downtime or issues since I've been here, and it's always on time.
Datadog seems stable in my experience without any downtime or reliability issues.
Datadog seems to be more stable, and I really want to have a complete demo before making a call to decide on this.
The integration landscape has become complex, and having a data strategy with unified data models would make integration easier for any platform, including Boomi.
A significant area for improvement is version control.
The ETL aspect of Boomi iPaaS is not mature enough at the moment.
It would be great to see stronger AI-driven anomaly detection and predictive analytics to help identify potential issues before they impact performance.
We want to be able to customize the cost part, and we would appreciate more granular access control.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
While pricing is relative, compared to others, it is favorable.
The pricing for Boomi iPaaS is reasonable, costing around $6,000 per year.
The setup cost for Datadog is more than $100.
Everybody wants the agent installed, but we only have so many dollars to spread across, so it's been difficult for me to prioritize who will benefit from Datadog at this time.
My experience with pricing, setup cost, and licensing is that it is really expensive.
It automates the creation and population of a data warehouse, reducing the need to write SQL scripts and procedures.
The most valuable features of Boomi are the integration capabilities, the Data Hub product, and the UDI integration.
The maturity of the product is significant.
Our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a multitude of programming languages.
Having all that associated analytics helps me in troubleshooting by not having to bounce around to other tools, which saves me a lot of time.
Datadog was able to find the alerts and trigger to notify our team in a very prompt manner before it got worse, allowing us to promptly adjust and remediate the situation in time.
| Product | Market Share (%) |
|---|---|
| Datadog | 12.5% |
| Boomi iPaaS | 0.1% |
| Other | 87.4% |

| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 4 |
| Large Enterprise | 15 |
| Company Size | Count |
|---|---|
| Small Business | 80 |
| Midsize Enterprise | 46 |
| Large Enterprise | 99 |
The Boomi AtomSphere integration platform as a service (iPaaS) supports all your application integration processes – between cloud platforms, software-as-a-service applications, and on-premises systems. Your entire team has online access to a powerful range of integration and data management capabilities, that can be realized in a fraction of the time of legacy middleware technologies.
Datadog integrates extensive monitoring solutions with features like customizable dashboards and real-time alerting, supporting efficient system management. Its seamless integration capabilities with tools like AWS and Slack make it a critical part of cloud infrastructure monitoring.
Datadog offers centralized logging and monitoring, making troubleshooting fast and efficient. It facilitates performance tracking in cloud environments such as AWS and Azure, utilizing tools like EC2 and APM for service management. Custom metrics and alerts improve the ability to respond to issues swiftly, while real-time tools enhance system responsiveness. However, users express the need for improved query performance, a more intuitive UI, and increased integration capabilities. Concerns about the pricing model's complexity have led to calls for greater transparency and control, and additional advanced customization options are sought. Datadog's implementation requires attention to these aspects, with enhanced documentation and onboarding recommended to reduce the learning curve.
What are Datadog's Key Features?In industries like finance and technology, Datadog is implemented for its monitoring capabilities across cloud architectures. Its ability to aggregate logs and provide a unified view enhances reliability in environments demanding high performance. By leveraging real-time insights and integration with platforms like AWS and Azure, organizations in these sectors efficiently manage their cloud infrastructures, ensuring optimal performance and proactive issue resolution.
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