

Datadog and Logpoint are both prominent products in the IT monitoring category. Datadog often seems to have the upper hand due to its extensive feature set and broader integrations.
Features: Datadog offers shareable dashboards, intuitive tagging, and seamless integrations with Amazon and Docker. Its ability to create monitors quickly encourages widespread monitoring adoption. Logpoint provides effective log collection, centralization, and user-friendly dashboards.
Room for Improvement: Datadog users suggest enhancements in API consistency, user interface design, and in-depth application insights features. Logpoint users see improvement areas in third-party integrations, handling log complexity, and documentation.
Ease of Deployment and Customer Service: Datadog offers flexible deployment models suitable for various cloud environments and is known for its proactive support. Logpoint primarily supports on-premise deployment, which may limit use in cloud environments, though customer service is generally good.
Pricing and ROI: Datadog's usage-based pricing can be unpredictable, yet it provides comprehensive monitoring and reduces downtime. Logpoint's fixed-cost model offers straightforward budgeting, making it cost-effective despite less robust feature sets compared to Datadog.
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.
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.
Logpoint's customer support is not sufficient with only one engineer in the US.
The technical support for Logpoint is very good, and I would rate it as nine out of ten.
I recommend a submission to Logpoint because I worked with it before.
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.
It is web-based and accommodates the expansion of our organization.
Logpoint is scalable and capable of expanding.
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.
I have received reports indicating glitches and downtimes with Logpoint.
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.
Dealing with foreign entities for support was a challenge, leading us to switch providers due to lack of adequate support.
Logpoint needs to be cloud-native, as currently, it is not.
Logpoint's UEBA is a weak point, while Exabeam's UEBA has extra AI through automation.
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.
I rate the pricing at eight, suggesting it's relatively good or affordable.
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.
The UEBA enables us to monitor at the device level, and SOAR provides playbooks and templates that we can modify and incorporate into the platform.
It effectively facilitates logging and log storage and assists in security event management by ingesting security events.
The most valuable feature, which is endpoint security, is included in Logpoint, and an extra feature is the integration.
| Product | Mindshare (%) |
|---|---|
| Datadog | 4.1% |
| Logpoint | 0.9% |
| Other | 95.0% |


| Company Size | Count |
|---|---|
| Small Business | 81 |
| Midsize Enterprise | 46 |
| Large Enterprise | 99 |
| Company Size | Count |
|---|---|
| Small Business | 18 |
| Midsize Enterprise | 3 |
| Large Enterprise | 4 |
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.
Logpoint is a cutting-edge security information and event management (SIEM) solution that is designed to be intuitive and flexible enough to be used by an array of different businesses. It is capable of expanding according to its users' needs.
Benefits of Logpoint
Some of the benefits of using Logpoint include:
Reviews from Real Users
Logpoint is a security and management solution that stands out among its competitors for a number of reasons. Two major ones are its data gathering and artificial intelligence (AI) capabilities. Logpoint enables users to not only gather the data, but also to maximize both the amount of data that can be gathered and its usefulness. It removes many of the challenges that users may face in data collection. The solution allows users to set rules for collection and then it pulls information from sources that meet the rules that have been set. This data is then broken into manageable segments and ordered. Users can then analyze these ordered segments with ease. Additionally, LogPoint utilizes both machine learning and AI technology. Users gain the ability to protect themselves from and if necessary resolve emerging threats as soon as they arise. The AI sets security parameters for a user’s system. These act as a baseline that are triggered and notify the user if anything deviates from the rules that it set up.
The chief infrastructure & security officer at a financial services firm writes, “It is a very comprehensive solution for gathering data. It has got a lot of capabilities for collecting logs from different systems. Logs are notoriously difficult to collect because they come in all formats. Logpoint has a very sophisticated mechanism for you to be able to connect to or listen to a system, get the data, and parse it. Logs come in text formats that are not easily parsed because all logs are not the same, but with Logpoint, you can define a policy for collecting the data. You can create a parser very quickly to get the logs into a structured mechanism so that you can analyze them.”
A. Secca., a Cyber Security Analyst at a transportation company, writes, “It is an AI technology because it is using machine learning technology. So far, there is nothing better out there for UEBA in terms of monitoring endpoints and user activity. It is using machine learning language, so it is right at the top. It provides that capability and monitors all of the user’s activities. It devises a baseline and monitors if there is any deviation from the baseline.”
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