Datadog Scout 1.0: Cheaper AI SRE Investigations, Run More Often
Datadog Scout 1.0 is an SRE model Datadog says matches frontier models at 3 to 8 times lower cost. What it changes for on-call teams using Bits AI.
Datadog BYOC Cross-Cluster Queries: One Search, Data Stays Put
Datadog BYOC Logs can now search several self-hosted clusters in one query, including for AI agents via MCP. What that fixes for data-residency observability.
Datadog Retired Its Host Scanner: 4 Lessons From the Migration
Datadog swapped its SSH host scanner for the Datadog Agent after a six-month parallel run. Its vulnerability management lessons apply to any scanner swap.
Datadog CI/CD Optimization: The Case for Treating Your Pipeline as a Production Service
Datadog's CI/CD Optimization combines pipeline telemetry, flaky test detection, and test impact analysis. Betterment cut builds from 40 minutes to under 10.
Datadog Observability Pipelines Now Routes Security Logs to Exabeam — Without Blinding Your SIEM
Datadog Exabeam Packs filter Cisco ASA, CrowdStrike, and Palo Alto logs before SIEM ingest — cutting costs while keeping threat-relevant signals intact.
Databricks Monitoring: The 7 Dimensions Most Teams Only Partially Cover
A Datadog framework for Databricks monitoring across seven dimensions, from queue wait time as a warehouse-exhaustion signal to GPU memory on serving endpoints.
The First 72 Hours of Ransomware: Why 'Restored Isn't Recovered' Changes Your Incident Response Plan
Restoring systems isn't recovering the business. Ransomware's first 72 hours need data validation, dependency checks, and SIEM mapped to MITRE ATT&CK.
Distributed DataFusion: Datadog Open-Sources a Framework for Running One Query Across Many Machines
Datadog open-sourced a framework that runs one Apache DataFusion query across many machines, reporting 10x speedups on long queries versus one node.
Datadog Supply Chain Firewall: Block Malicious npm and pip Packages Before They Install
Datadog's open-source Supply Chain Firewall checks npm, pip, and poetry installs against threat databases and blocks malicious packages before they install.
How Datadog Built an Async-Aware Python Profiler — and Why Traditional Profilers Fail Async Code
Python profilers can't follow asyncio tasks. How Datadog rebuilt task dependency trees and cut overhead over 60% in its async-aware profiler.
Datadog Extends RUM to Shopify and Salesforce — Monitoring Revenue Flows You Don't Control
Datadog RUM now covers Shopify and Salesforce Experience Cloud, so teams can monitor checkout funnels and errors on frontends they can't instrument.
Datadog Tap to Parse: Click a Log Field, Get a Grok Parser — No Regex Required
Datadog Tap to Parse is GA: click a log field and get a generated parsing rule to review, no Grok syntax needed. Where it fits in the log lifecycle.
Datadog Trained a 9B Model for $0.003 Per Alert Investigation — Here's the Architecture
Datadog fine-tuned a 9B model to 87% of its teacher's recall at 5% of the cost, about $0.003 per production alert investigation. The architecture behind it.
Datadog's MCP Server Now Runs Code — and Cuts AI Agent Investigation Costs 73% in the Process
Datadog's MCP Server now runs sandboxed JavaScript for AI agents, cutting input tokens 73% and lifting answer correctness from 74% to 90% across 25 tasks.
Datadog IaC Security Now Accepts Custom Rego Rules — and Can Block PRs That Fail Them
Datadog IaC Security now supports organization-specific custom rules written in Rego, covering Terraform, CloudFormation, Kubernetes manifests, Ansible, Dockerfiles, and GitHub Actions. PR Gates can block merges on policy violations. Configuration lives in code-security.datadog.yaml.
NetFlow Traffic Counts Are Lying to You. Here's How to Fix Them.
Most NetFlow configurations double-count traffic by design — two independent mechanisms create duplicate records, and they often run simultaneously. Datadog's guide covers both root causes and the two complementary fixes that eliminate the noise without losing visibility.
Datadog Workload Protection Can Now Kill Processes and Isolate Workloads Mid-Attack
Datadog's Workload Protection has added automated and manual response actions — kill offending processes and isolate compromised containers — closing the gap between detection and remediation without a human handoff.
The Datadog Loop: Five Stages for Turning Agent Traces Into Verified Improvements
Datadog's five-stage optimization loop — Observe, Hypothesize, Evaluate, Experiment, Ship — gives platform teams a repeatable method for improving AI agents without blind prompt guessing.
From Traces to Experiments: The 5-Stage Loop for Improving AI Agents in Production
Datadog's framework for turning AI agent traces into actionable improvements — from pattern identification to production A/B testing — addresses the gap that offline evaluation alone can't close.
Datadog's Bitbucket Cloud Integration: Source Code Context in Every Trace
Datadog's new Bitbucket Cloud source code integration connects running services to their commit history, surfaces code snippets inline during troubleshooting, and posts automated PR feedback. Here's what changes for teams using Bitbucket.
How Datadog Bits Proves a Query Rewrite Is Actually Faster — Not Just Theoretically
Datadog's Bits Database Optimization generates synthetic production environments to validate query rewrites against three metrics before recommending them. Here's the methodology that makes it trustworthy.
What Datadog Learned About AI Agent Security by Monitoring Its Own Agents
Datadog's internal agent monitoring revealed that 69% of input tokens live in system prompts teams can't inspect, coding agents execute repo-controlled code before the first user message, and prompt injection bypasses model evaluation entirely.
Datadog Live Debugger: Fix Production Bugs Without Writing a Single Deploy
Datadog's Live Debugger attaches logpoints to running services and collects variable snapshots without code changes or restarts — with Bits AI converting a plain-language bug description into a production-backed code fix.
How Datadog Saves $1 Million a Month on AI: The Optimization Playbook
Datadog cut over $1M/month in AI costs by migrating models, adjusting effort levels, and deploying context optimization. The specific numbers and methods behind each lever.
Golden Paths for AI Agents: What Platform Engineering Gets Wrong When the User Isn't Human
Datadog's framework for redesigning golden paths when AI agents replace human developers as platform users — deterministic enforcement, machine-readable contracts, and audit co-authorship.
Golden Paths for AI Agents: Redesigning Platform Workflows for Non-Human Users
Agents are now platform users. Datadog maps three required changes: execution patterns, deterministic controls, and dispatch authorization before agent runs.
Datadog Risk AI: How Systemic Risk Detection Moves Beyond Individual Findings
Datadog's Risk Engineering team built an AI agent layer that correlates security signals across vulnerability, identity, and exposure dimensions — recognizing that severity and risk aren't the same thing.
How Datadog Scaled Git Serving 20x Without Slowing CI Down
Datadog's GitRetriever sustains 20× CI traffic growth at 40ms median latency — a two-tier mirror/relay architecture for monorepo CI that any platform team can adapt.
How to Trace AWS Lambda Durable Functions with Datadog
Datadog's Node.js and Python tracers now instrument AWS Lambda Durable Functions automatically — connecting multi-invocation workflows into a single unified flame graph without code changes.
Datadog Work Management: One Queue for Humans and AI Agents
Datadog's renamed Work Management platform centralizes human and AI agent task tracking with full observability context attached. Here's what it solves that Jira and Slack threads don't.
Datadog's Azure Integration No Longer Needs a Secret to Rotate
Secretless authentication replaces static client secrets with OIDC-based federated identity. No expiration dates, no rotation schedules, no telemetry gaps when someone forgets to update a credential.
Unfixed Kubernetes CVEs: What You Can and Can't Actually Detect
Four Kubernetes CVEs have been unfixed since 2020–2021 — not because they were overlooked, but because patching would break the platform. Here's how to know if your cluster is actually exposed.
Understanding Unfixed Kubernetes CVEs: What You Can — and Can't — Detect
Four Kubernetes CVEs remain unfixed because patching would break the platform. Datadog's guide to audit-log-based detection shows what each one exposes — and the honest limits of what audit logs can confirm.
Datadog's Mambark: A 97M-Parameter Filter That Cuts AI Security Costs 10,000x
Datadog's Mambark, a 96.9M-parameter state-space model, screens 10 billion daily security events down to ~10,000 candidates before an AI agent ever runs.
Datadog JFR: Why CPU-Time Profiling Beats Wall-Clock in JDK 25
JDK 25 adds unbiased CPU-time profiling to Java Flight Recorder, fixing wall-clock sampling bias that misleads Java performance work.
Datadog's MCP Security Agents: What Production Looks Like Beyond the Demo
Datadog built MCP tools for Cloud SIEM operations and published the engineering decisions. Progressive disclosure cut token usage 41%. Here's the architecture.
Datadog: What a Sixth Gartner Lead Means for AI-Driven Ops Teams
For the sixth straight year, Datadog tops Gartner's Observability MQ. Here's what the recognition reveals about observability in an AI-native world.
Datadog Agent Observability Brings AI App Debugging Into Your Coding Agent
Teams spend 60–80% of AI development time on evaluation and error analysis. Datadog's new Agent Observability workflow surfaces production trace data directly into coding agents to cut that cycle.
Datadog Acquires Adaptive ML: Production-Trained AI Agents for Observability
Datadog acquired Adaptive ML to embed specialized model training into its observability platform — shifting AI agents from generic APIs to systems trained on real production signals.
Datadog Achieves GovRAMP High: What It Means for State and Local Government Observability
GovRAMP High authorization opens Datadog's full platform to state, local, tribal, and educational agencies — addressing the fragmented monitoring problem endemic to government IT.
OMB M-26-14: What Federal Agencies Must Do About Logging — and How Datadog Helps
The new OMB memo replaces prescriptive logging mandates with a 320-day maturity framework across four levels. Datadog's FedRAMP High platform maps to each level directly.
Datadog's CDN Log Fix: Archive Search Lets You Query 751 GB of Logs Without Paying to Index Them
High-volume CDN access logs are too expensive to keep in Datadog's indexing tier but too valuable to throw away. Archive Search solves the gap — query petabytes in cold storage with the same syntax as Log Explorer.
How Datadog Used AI to Turn a 45-Minute Query Into a 1-Second One
Datadog migrated its Stream Router — the control plane handling 100+ trillion events daily — from FoundationDB to PostgreSQL using AI-assisted refactoring. The approach cut 45-minute operations to 1 second and reduced database costs by 90%.
Datadog MCP Apps Bring Rendered Dashboards Into Claude, Cursor, and ChatGPT
Datadog's MCP Apps extend the Model Context Protocol with rendered UI components — interactive monitor cards, timeseries graphs, and APM trace views — surfaced directly inside AI coding and chat interfaces.
Datadog DASH 2026: When Your Monitoring Platform Became an Agentic Operating System
Bits AI agents now detect, remediate, validate, and write code inside Datadog. DASH 2026 marks the shift from observability tool to autonomous engineering platform.
Datadog's BewAIre: From 97.4% to 99.86% Accuracy on Malicious Package Detection
Datadog's BewAIre system detects malicious code in npm and PyPI packages with 99.86% accuracy using a two-stage AI pipeline — a lightweight filter that exits 95% of packages cheaply, then an agentic investigation loop for everything that looks suspicious.
Datadog Cut Spark Compute Costs 44% with an AI Agent. Here's Exactly How.
Datadog's internal ServiceQueryEdge job ran 17+ hours daily and cost $1.5k/day. An AI agent using Claude and Jobs Monitoring found three Scala-level optimizations that cut duration by 60% and cost by 44% — approximately $250K in annual savings.
Kubernetes Autoscaling at Scale: The Datadog Approach
From bulk UI activation to GitOps cluster profiles: how Datadog deploys Kubernetes autoscaling across an entire fleet without per-service configuration handoffs or manual YAML.
Inference-Aware Routing for LLMs: What Datadog Now Monitors
Kubernetes Inference Extension routes LLM traffic by KV cache state and LoRA adapter availability. Datadog surfaces the metrics that reveal whether that routing is actually working.
Platform Depth Unlocks AI Agents: The Unified Data Model Case
Fragmented tooling creates 20+ minute delays correlating data before rollout decisions. Unified data models eliminate that latency — and are the prerequisite for AI agents that can monitor and respond to production events autonomously.
Datadog AI Impact: Connecting AI Coding Tool Usage to Deployment Performance
Datadog AI Impact maps AI-assisted commits to lead time, change failure rate, and deployment frequency — measuring AI tool ROI against actual delivery performance.
Datadog's 4th DevEx Dimension: Why AI Changes What You Have to Measure
Datadog added a 4th dimension to the DevEx framework for the AI era: AI adoption and impact. With 80% AI-assisted PRs, what you measure has to change.
Datadog API Authentication Detection: Why Three States Beat Two
Datadog App & API Protection adds three-state auth classification to eliminate false positives. How evidence-based detection works at scale.
AI Agent Guardrails: Where You Place Them Matters as Much as What They Do
Reactive guardrails on tool outputs miss the attack. Why pre-execution placement and multi-hook coverage change AI agent security posture.
Datadog Database Investigator: Root Cause Analysis Without a DBA
Datadog's agentic Database Investigator diagnoses latency spikes, connection pool exhaustion, and replication lag without requiring dedicated DBA expertise.
Datadog Governance Console: Observability Gets a Policy Enforcement Layer
Datadog's new Governance Console shifts observability management from reactive cleanup to proactive policy enforcement — automated credential revocation, drift detection, and usage accountability built in.
Datadog's Open Source AI SAST: 90% Detection, Fewer False Positives
Datadog released an open source AI-native SAST tool that hits 90% command injection detection on OWASP benchmarks — up from 59% with traditional rules. Here's the architecture and why false positives were the real problem.
Platform Engineering Metrics: What to Measure, What to Ignore
Datadog maps the three-tier metrics hierarchy every platform team needs — outcomes, drivers, diagnostics — and explains why optimizing CI duration in isolation is a trap. Here's the framework.
Datadog CI/CD Threat Matrix: A MITRE Framework for Pipeline Security
Datadog published a MITRE ATT&CK-style threat matrix for CI/CD pipelines — 10 attack tactics mapped to the specific exposure points where compromised pipelines ship malicious code.
Datadog Code Security MCP: Security Scanning Inside AI Code Generation
Datadog launched an MCP server that runs SAST, SCA, secrets detection, and IaC scanning during AI code generation — catching vulnerabilities before they reach a pull request.
Datadog Bits Assistant: Natural Language Across the Entire Observability Stack
Datadog's Bits Assistant brings conversational AI to dashboards, logs, traces, and incident response — making complex telemetry queries accessible without query syntax expertise.
Datadog Experiments: Measuring the Business Cost of Every Product Change
Datadog Experiments connects A/B tests to APM, warehouse metrics, and Session Replay — measuring business impact without a separate analytics platform.
Datadog Bits AI Dev Agent: Automated Vulnerability Remediation at Scale
Datadog's Bits AI Dev Agent opens GitHub PRs to fix SAST findings automatically — closing the security backlog that manual triage alone can't clear.