In the competitive security software category, Vectra AI and Exabeam vie for top positioning. Both have strong capabilities, though Vectra AI stands out for its alert reduction and host detection, while Exabeam is notable for its advanced timeline creation and integration.
Features: Vectra AI enhances threat prioritization through AI and machine learning, reducing alert fatigue and offering insight into the full attack lifecycle. Its risk score aggregation helps focus resources. Exabeam features machine learning and behavioral analytics, excelling in complex threat detection and workflow automation. Vectra AI's standout alert reduction and host detection, complemented by Exabeam's superior timeline creation and cloud service integration, highlight their key strengths.
Room for Improvement: Vectra AI could benefit from improved integrations and detection rule flexibility and needs to address false positive reduction. Issues with licensing models and feature costs are noted. Exabeam is encouraged to improve dashboard customization and adaptive rule features, alongside better API interactions. Both could streamline integrations and minimize false positives for enhanced user satisfaction.
Ease of Deployment and Customer Service: Vectra AI supports diverse deployment modes (on-premises, hybrid, public cloud) and is praised for its responsive customer support. Exabeam offers similar deployment options, with specific attention required in API performance. Vectra AI is noted for exceptional customer interaction and recognizing user feature enhancement requests. Both are well-regarded for technical support, but Vectra AI stands out for proactive customer service.
Pricing and ROI: Vectra AI is associated with higher pricing, yet its comprehensive features may justify the cost for larger organizations. Its complex licensing model suggests a need for simplification. Exabeam offers a more affordable and flexible pricing structure, appealing to budget-conscious organizations. Both deliver efficiency improvements, with Vectra AI noted for its security workload impact, while Exabeam provides more accessible pricing options.
Exabeam offers more machine learning models that detect anomalies.
The payback period is roughly six months.
Even with TAM support from Exabeam, many issues go unresolved.
I also had the chance to look at the documentation, and the documentation is good.
The support is quite reliable depending on the service engineer assigned.
When I create tickets, the response is fast, and issues are solved promptly.
Customer support receives a rating of nine out of ten due to being very supportive and responding quite efficiently.
Regarding Exabeam's scalability and how well it adapts to its customers' needs, I would rate it an eight.
Vectra AI is scalable because it can work through different kinds of solutions and is compatible with all kinds of cloud solutions.
These problems were not frequent, and the last six to eight months have been stable.
Overall, I think Exabeam's stability level is good.
Exabeam needs to improve its documentation and provide more customization for dashboards and case management.
I have explored the SaaS version; it offers many new features.
Exabeam's integration capabilities are not good, as Exabeam has a very limited number of integrations and no out-of-box integration.
ExtraHop's ability to decrypt encrypted data is a feature that Vectra AI lacks.
You need to have a Linux server, and from the Linux server, you must perform AI tasks, and there is a lot to be handled in the back end.
All threats, including hacking attempts, should be comprehensively addressed.
Vectra is cheaper in terms of pricing and features compared to Darktrace.
It is very acceptable when you compare it with Darktrace, for example.
Exabeam's AI capabilities, like the natural language mode, convert natural language into Exabeam queries, enhancing ease of use.
The product offers useful features like the dashboard, timeline, and session views, which enhance our security tools.
Exabeam's UEBA is the most valuable feature that I have found so far.
Our company used Vectra AI to detect the malicious threats and viruses before they could cause more damage, and we successfully stopped the threats.
Alert noise was dramatically reduced by nearly 80%, allowing SOC analysts to focus more on true threats, which made them more productive and resulted in higher operational efficiency.
There are extensive out-of-box detection capabilities.
| Product | Market Share (%) |
|---|---|
| Vectra AI | 9.8% |
| Exabeam | 3.2% |
| Other | 87.0% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 4 |
| Large Enterprise | 7 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 10 |
| Large Enterprise | 29 |
Exabeam Fusion is a cloud-delivered solution that that enables you to:
-Leverage turnkey threat detection, investigation, and response
-Collect, search and enhance data from anywhere
-Detect threats missed by other tools, using market-leading behavior analytics
-Achieve successful SecOps outcomes with prescriptive, threat-centric use case packages
-Enhance productivity and reduce response times with automation
-Meet regulatory compliance and audit requirements with ease
Vectra AI offers advanced hybrid network and identity security, detecting threats traditional tools miss. It uses AI to identify lateral attacks and credential misuse, providing a proactive defense for enterprises.
Vectra AI enhances security by using AI-driven detection across network, cloud, and identity layers, surpassing EDR and SIEMs by offering real-time threat detection. It ensures continuous observability and automates SOC workflows to minimize manual efforts, creating an efficient security environment. Its AI-powered approach significantly reduces noise, focusing on true threats, and provides insights into complex threat landscapes, with seamless integration into environments like EDR and Office 365.
What are Vectra AI's key features?Vectra AI is utilized across industries for comprehensive network and anomaly detection. Organizations deploy it for threat hunting and incident response, monitoring both on-premises and cloud activities. By placing sensors across sites, they optimize security practices and streamline their detection processes.
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