LogPoint and Amazon OpenSearch Service compete in the logging and analytics category. LogPoint stands out for its user-friendliness and cost-effective pricing, while Amazon OpenSearch Service offers extensive features and scalability, preferred by larger organizations.
Features: LogPoint shines with quick setup, ease of use, and sophisticated logging capabilities, including effective dashboards and analytics. Its pricing model is flexible and based on the number of devices. AI-based user and entity behavior analytics (UEBA) and SIEM and SOAR integration are key features. Amazon OpenSearch Service excels in robust search capabilities, scalability, handling large data volumes with ease, and powerful dashboards that integrate multiple analytics tools.
Room for Improvement: LogPoint users seek better integration, cloud-native deployment, and enhanced documentation. Handling non-standard logs and high memory consumption are challenges, with improvements desired in UI and ransomware detection support. Amazon OpenSearch Service faces criticism for complex configurations and high costs, with calls for improved data handling, customization, and pricing models.
Ease of Deployment and Customer Service: LogPoint primarily offers on-premises solutions with some hybrid cloud options. Technical support receives mixed reviews, praised for responsiveness but critiqued for low-level support quality. Amazon OpenSearch Service, available on public cloud platforms, encounters issues with customer service speed but generally provides good technical support. It offers broader cloud deployment options compared to LogPoint.
Pricing and ROI: LogPoint is valued for its fixed pricing model, offering predictability and cost-effectiveness, suited for medium-sized deployments and providing good ROI. While Amazon OpenSearch Service delivers managed service benefits, its high costs and unpredictability in pricing during idle times are concerns. It offers substantial functionality with a higher ROI for large-scale applications.
Amazon OpenSearch Service provides scalable and reliable search capabilities with efficient data processing, supporting easy domain configuration and integration with numerous systems for enhanced performance.
Amazon OpenSearch Service offers advanced features for handling JSON, diverse search grammars, quick historical data retrieval, and ultra-warm storage. It also includes customizable dashboards and seamless tool integration for large enterprises. With its managed infrastructure, OpenSearch Service supports efficient system analysis and business analytics, improving overall performance and flexibility. Despite these features, areas like configuration complexity, lack of auto-scaling, and integration with Kibana require attention. Users seek enhanced documentation, better pricing options, and more flexible data handling. Desired improvements include default filters, mapping configuration, and alerting capabilities. Enhanced data visualization and Compute Optimizer Service integration are also recommended for future updates.
What features define Amazon OpenSearch Service?Amazon OpenSearch Service is utilized in various industries for log management, data storage, and search capabilities. It supports infrastructure and embedded management, analyzing logs from AWS Lambda, Kubernetes, and other services. Companies use it for application debugging, monitoring security and performance, and customer behavior analysis, integrating it with tools like DynamoDB and Snowflake for a cost-effective solution.
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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