135 updates from Datadog and Better Stack 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.
Better Stack is a unified observability tool that helps you ship higher‑quality software faster. Monitor everything from websites to servers. Schedule on-call rotations, get actionable alerts, and resolve incidents faster than ever. Visualize your entire stack, aggregate all your logs into structured data, and query everything like a single database with SQL. Made to fit into your workflow with over 100+ integrations.
Datadog positions itself as a monitoring service tailored for IT, development, and operations teams managing large-scale applications, emphasizing the transformation of application-generated data into actionable insights. Better Stack frames itself as a unified observability tool designed to streamline software delivery by integrating monitoring, alerting, and workflow automation with a focus on structured data querying and workflow compatibility. Datadog emphasizes scalability and real-time data analysis across distributed systems, while Better Stack highlights structured log aggregation, SQL-based querying, and deeper integration with operational workflows through features like on-call scheduling and incident resolution.
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Weekly updates per vendor, last 12 weeks.
Page-type activity over the last 30 days. Brighter cells = more updates.
DatadogBetter StackDatadog demonstrated significant activity focused on scaling AI-driven observability and security, highlighted by its upcoming San Francisco Summit and several case studies showcasing cost reductions and migration successes for enterprise clients like Experian and Nomad Health. The company is heavily emphasizing runtime security for AI agents and full-stack integration across hybrid environments. In contrast, Better Stack’s recent activity centered on technical explorations of local LLM performance on mobile hardware and expanding its integration ecosystem with tools like Linear and Jira. While Datadog is positioning itself as a comprehensive enterprise platform for AI-integrated infrastructure, Better Stack is focusing on developer-centric workflows and the technical limits of on-device AI execution.
Last updates we detected for each vendor.
Datadog released the General Availability of Code Execution for the Datadog MCP Server, enhancing AI agent efficiency. This update significantly reduces input tokens and tool calls required for investigations.
Datadog shared a blog post about its Tap to Parse feature, which enables users to instantly convert log messages into structured, searchable fields without writing regex. This capability is available across Log Explorer, Log Pipelines, and
Datadog offers a unified observability platform that aggregates metrics, logs, and traces across the full DevOps stack. The service provides deep visibility into frontend and backend performance, network monitoring across hybrid environment
Datadog is hosting a live episode of its Datadog On Air podcast from the Datadog Summit in San Francisco. The episode will feature discussions with leaders and engineers regarding building and running AI in production.
Datadog shares an employee spotlight on Natalie, a Senior Enterprise Customer Success Manager in Sydney, to highlight career growth opportunities within the company. The post emphasizes how Datadog employees can evolve their roles and drive
Better Stack evaluates the differences between incident.io and FireHydrant, highlighting how their distinct design philosophies impact incident response. The comparison focuses on incident.io's chat-driven AI approach versus FireHydrant's s
Better Stack provides documentation on implementing middleware for Hono applications to log every request and response. The guide details how to capture request metadata, handle errors, and ensure log delivery across different runtimes.
Better Stack shared a technical demonstration showing llama.cpp running a 90M model on a 6-year-old Apple Watch at 24 tokens per second.
Shopify has moved from a React Native cross-platform architecture to native Swift and Kotlin development. This decision was enabled by AI agents that reduce the cost of maintaining separate codebases, allowing for better performance and sta
Better Stack shares a technical tutorial on deploying a local LLM on an Apple Watch Series 6 using llama.cpp. The article explains how to overcome Core ML limitations and hardware constraints to achieve efficient on-device inference.
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