NeuBird
NeuBird's flagship product, Hawkeye (Agentic AI SRE), is a groundbreaking Site Reliability Engineering platform that utilizes artificial intelligence to transform IT operations by continuously monitoring telemetry from the entire observability stack, which encompasses logs, metrics, traces, alerts, and incident tickets. This platform facilitates the identification of issues, performs in-depth root cause analysis, and provides or automates effective resolutions in real-time, thereby removing the necessity for manual investigation. Tailored for enterprise-scale environments, Hawkeye ensures secure integration with a wide range of existing monitoring and incident management tools, including DataDog, Splunk, PagerDuty, Prometheus, ServiceNow, AWS CloudWatch, Azure Monitor, among others. By effectively correlating signals from various sources and reasoning akin to a human engineer, it reveals actionable insights that can dramatically reduce mean time to resolution (MTTR) by almost 90%. Operating around the clock, Hawkeye can be implemented as a Software as a Service (SaaS) or within a customer's Virtual Private Cloud (VPC), boasting stringent enterprise security protocols and features such as autonomous incident response and sophisticated pattern recognition, thus presenting a well-rounded solution to contemporary IT challenges. Furthermore, its capacity to adapt and learn from ongoing operations guarantees that organizations can uphold high availability and performance levels, even in an ever-changing technological landscape, making it an indispensable asset for any business.
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Grafana Cloud
Grafana Labs provides an open and composable observability stack built around Grafana, the leading open source technology for dashboards and visualization. Recognized as a 2025 Gartner® Magic Quadrant™ Leader for Observability Platforms and positioned furthest to the right for Completeness of Vision, Grafana Labs supports over 25M users and 5,000+ customers.
Grafana Cloud is Grafana Labs’ fully managed observability platform designed for scale, intelligence, and efficiency. Built on the open-source LGTM Stack—Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics—it delivers a complete, composable observability experience without operational overhead.
Grafana Cloud leverages machine learning and intelligent data management to help teams optimize performance and control costs. Features like Adaptive Metrics and cardinality management automatically aggregate high-volume telemetry data for precision insights at a fraction of the cost. With AI-driven alerting and incident correlation, teams can detect anomalies faster, reduce alert fatigue, and focus on what matters most—system reliability and user experience. Grafana Cloud supports OLAP-style analysis through integrations with analytical databases and data warehouses, allowing teams to visualize and correlate multi-dimensional datasets alongside observability data.
Seamlessly integrated with OpenTelemetry and hundreds of data sources, Grafana Cloud provides a single pane of glass for monitoring applications, infrastructure, and digital experiences across hybrid and multi-cloud environments. Backed by Grafana Labs’ global expertise and trusted by 5,000+ customers, it empowers organizations to achieve observability at scale—open, intelligent, and future-ready.
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Edge Delta
Edge Delta introduces a groundbreaking approach to observability, being the sole provider that processes data at the moment of creation, allowing DevOps, platform engineers, and SRE teams the flexibility to direct it wherever needed. This innovative method empowers clients to stabilize observability expenses, uncover the most valuable insights, and customize their data as required.
A key feature that sets us apart is our distributed architecture, which uniquely enables data processing to occur at the infrastructure level, allowing users to manage their logs and metrics instantaneously at the source. This comprehensive data processing encompasses:
* Shaping, enriching, and filtering data
* Developing log analytics
* Refining metrics libraries for optimal data utility
* Identifying anomalies and activating alerts
Our distributed strategy is complemented by a column-oriented backend, facilitating the storage and analysis of vast data quantities without compromising on performance or increasing costs.
By adopting Edge Delta, clients not only achieve lower observability expenses without losing sight of key metrics but also gain the ability to generate insights and initiate alerts before the data exits their systems. This capability allows organizations to enhance their operational efficiency and responsiveness to issues as they arise.
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Cribl Stream
Cribl Stream enables the creation of an observability pipeline that facilitates the parsing and reformatting of data in real-time before incurring costs for analysis. This tool ensures that you receive the necessary data in your desired format and at the appropriate destination. It allows for the translation and structuring of data according to any required tooling schema, efficiently routing it to the suitable tools for various tasks or all necessary tools. Different teams can opt for distinct analytics platforms without needing to install additional forwarders or agents. A staggering 50% of log and metric data can go unutilized, encompassing issues like duplicate entries, null fields, and fields that lack analytical significance. With Cribl Stream, you can eliminate superfluous data streams, focusing solely on the information you need for analysis. Furthermore, it serves as an optimal solution for integrating diverse data formats into the trusted tools utilized for IT and Security purposes. The universal receiver feature of Cribl Stream allows for data collection from any machine source and facilitates scheduled batch collections from REST APIs, including Kinesis Firehose, Raw HTTP, and Microsoft Office 365 APIs, streamlining the data management process. Ultimately, this functionality empowers organizations to enhance their data analytics capabilities significantly.
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