280 updates from Datadog and Grafana Labs in the last 30 days. We read them all so you don't have to.
Datadog is a monitoring service for IT, Dev and Ops teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.
Grafana Labs helps users get the most out of Grafana, enabling them to take control of their unified monitoring and avoid vendor lock in and the spiraling costs of closed solutions.
Datadog positions itself as a comprehensive monitoring platform designed to help IT, development, and operations teams derive actionable insights from large-scale application data, emphasizing integration and built-in analytics. Grafana Labs focuses on empowering users with open-source tools to create unified monitoring experiences, prioritizing flexibility, vendor independence, and cost control. While Datadog offers a closed, all-in-one solution with proprietary data collection and analysis, Grafana emphasizes open-source customization and interoperability with external data sources, appealing to users seeking avoid vendor lock-in and tailored monitoring setups.
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Weekly updates per vendor, last 12 weeks.
Page-type activity over the last 30 days. Brighter cells = more updates.
DatadogGrafana LabsDatadog focused heavily on expanding its AI ecosystem through strategic partnerships with OpenAI and Databricks, alongside product launches like Datadog Apps and Bits Chat for Slack. Their activity centered on integrating AI agents into native workflows and strengthening their partner network in the APJ region. In contrast, Grafana Labs released a high volume of technical updates and changelogs, focusing on the granular mechanics of observability. Their releases included Tempo 3.1 updates, new CLI tools for AI agent telemetry, expanded cost attribution for synthetic monitoring, and several security disclosures. While Datadog prioritized ecosystem integration and high-level AI partnerships, Grafana Labs prioritized platform feature depth, cost management tools, and specific telemetry refinements.
Last updates we detected for each vendor.
Datadog shared insights on the limitations of traditional application logs for monitoring AI agents and showcased their rebuilt tracking capabilities for better observability.
Datadog has signed a Strategic Collaboration Agreement with MegazoneCloud to help enterprises in the APJ region accelerate cloud modernization and scale AI workloads through unified observability and multi-cloud expertise.
Datadog shared insights on how observability data like logs and traces is essential for reconstructing cloud attack paths. The company also highlighted its organizational decision to merge SRE and security teams to unify monitoring efforts.
Datadog is soliciting community feedback regarding how artificial intelligence is changing the workflows of security teams and the tactics used by attackers.
Datadog launched Bits Chat, a natural-language interface that brings Datadog's AI capabilities directly into Slack to streamline incident response and reduce context switching.
Grafana Labs offers an interactive learning path for Logs Drilldown within the Grafana Cloud stack. This tutorial helps users explore logs to improve troubleshooting and insight capabilities.
Grafana Labs explains the five-step log investigation workflow used to analyze and filter log data. The process includes viewing log volumes, applying time-based patterns, and utilizing LogQL queries through Grafana Explore.
Grafana Labs provides documentation on using Logs Drilldown to filter and analyze log lines. The feature allows users to use labels, fields, and text filters to isolate specific log data.
Grafana Labs explains how to use the Logs Drilldown feature to visualize Loki logs without writing LogQL. The tool allows users to filter logs by text or level and view log volume patterns.
Grafana Labs amplified a post by Rishab Dawar discussing how Property Finder reduced their observability costs by 99% using the Grafana stack.
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