惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

酷 壳 – CoolShell
酷 壳 – CoolShell
H
Hacker News: Front Page
P
Palo Alto Networks Blog
T
ThreatConnect
Apple Machine Learning Research
Apple Machine Learning Research
博客园_首页
T
True Tiger Recordings
P
Privacy & Cybersecurity Law Blog
B
Blog
IT之家
IT之家
Last Week in AI
Last Week in AI
F
Full Disclosure
Hacker News: Ask HN
Hacker News: Ask HN
C
Comments on: Blog
Microsoft Azure Blog
Microsoft Azure Blog
C
Cybersecurity and Infrastructure Security Agency CISA
Microsoft Security Blog
Microsoft Security Blog
博客园 - 【当耐特】
N
News and Events Feed by Topic
NISL@THU
NISL@THU
腾讯CDC
雷峰网
雷峰网
Security Latest
Security Latest
李成银的技术随笔
M
Microsoft Research Blog - Microsoft Research
L
LangChain Blog
L
Lohrmann on Cybersecurity
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
C
Check Point Blog
Y
Y Combinator Blog
Recent Announcements
Recent Announcements
博客园 - Franky
N
News | PayPal Newsroom
V
V2EX
A
About on SuperTechFans
The Register - Security
The Register - Security
月光博客
月光博客
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Google Online Security Blog
Google Online Security Blog
MyScale Blog
MyScale Blog
Cisco Talos Blog
Cisco Talos Blog
Vercel News
Vercel News
WordPress大学
WordPress大学
C
Cyber Attacks, Cyber Crime and Cyber Security
The Hacker News
The Hacker News
IntelliJ IDEA : IntelliJ IDEA – the Leading IDE for Professional Development in Java and Kotlin | The JetBrains Blog
IntelliJ IDEA : IntelliJ IDEA – the Leading IDE for Professional Development in Java and Kotlin | The JetBrains Blog
爱范儿
爱范儿
A
Arctic Wolf
L
LINUX DO - 最新话题
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

Datadog | The Monitor blog

Reduce CVE noise with OpenVEX assessments in Datadog How we made a SQL query optimization agent 59% more accurate using autoresearch and LLM Observability How to audit and clean up monitors effectively Diagnose slow PostgreSQL queries faster with explain plan correlation Explore Datadog metrics with Natural Language Queries Toto 2.0: Time series forecasting enters the scaling era Simplify micro-frontend observability with Datadog RUM Attribute AI costs across providers with Datadog Cloud Cost Management Diagnose and resolve database performance issues faster with Database Investigator Datadog for Government achieves FedRAMP® High certification Analyze cloud costs with flexible spreadsheets in Datadog Sheets Inside Datadog’s AI Research Lab: Meet two PhD candidates behind Toto Connect triage and investigation in a single workflow with Datadog Cloud SIEM This Month in Datadog - April 2026 Monitor and optimize Supabase query performance with Datadog Database Monitoring Add dynamically updating context to logs with Reference Tables and Observability Pipelines Introducing ARFBench: A time series question-answering benchmark based on real incidents The product signal latency gap slowing your growth Test network paths with TCP, UDP, and ICMP in Datadog Turn developer feedback into operational insight with Datadog Forms and Sheets How to investigate cloud credential compromise with Bits AI Security Analyst Evaluate, optimize, and secure your Google Cloud AI stack with Datadog Bringing observability data hosting to the UK on AWS Identify and fix code issues faster with Datadog’s Azure DevOps Source Code integration Steganography at scale: Embedding share URLs in Datadog widget screenshots Every team should be A/B testing Centralize observability management with Datadog Governance Console Spotting CI/CD misconfigurations before the bots do: Securing GitHub Actions with Datadog IaC Security Route OTel data from AI apps to ClickHouse and Datadog using Observability Pipelines Manage service tracing across hosts with Single Step Instrumentation rules Offline evaluation for AI agents: Best practices Detect runtime threats in Python Lambda functions with Datadog AAP Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API How we built a real-world evaluation platform for autonomous SRE agents at scale Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure When upserts don't update but still write: Debugging Postgres performance at scale Annotate traces to improve LLM quality with Datadog LLM Observability What's new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog's platform in the AI age: The role of observability data Closing the verification loop: Observability-driven harnesses for building with agents When an AI agent came knocking: Catching malicious contributions in Datadog’s open source repos Closing the verification loop, Part 2: Fully autonomous optimization Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Designing MCP tools for agents: Lessons from building Datadog's MCP server Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - February 2026 Fine-tune Toto for turbocharged forecasts Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog How we reduced the size of our Agent Go binaries by up to 77% Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage
Reduce costs and enhance security with cross-region Datadog connectivity using AWS PrivateLink
2025-04-02 · via Datadog | The Monitor blog

Modern cloud architectures are increasingly distributed, with applications and services spanning multiple regions to improve availability, reduce latency, and support disaster recovery. Many of our customers rely on solutions like Virtual Private Cloud (VPC) peering, Network Address Translation (NAT) gateways, and AWS Transit Gateways to securely send telemetry to Datadog across regions. These methods work but can add complexity, increase costs, and create potential security risks.

We are excited to announce support cross-region connectivity for AWS PrivateLink for Datadog endpoints in the US1 and AP1 regions. This enables AWS customers to securely and cost-effectively connect to Datadog services from any AWS region without the need for complex networking configurations. With cross-region PrivateLink, your observability data never leaves the AWS network. This enhances security, improves compliance, and simplifies management—all while reducing costs compared to traditional solutions.

In this post, we describe the challenges of cross-region connectivity for Datadog customers, how AWS PrivateLink addresses them, and how to set it up.

Simplify Datadog connectivity and reduce costs

The common methods for cross-region connectivity—VPC peering, NAT gateways, and AWS Transit Gateways—come with trade-offs. VPC peering requires manual updates to route tables and DNS record management to ensure proper connectivity to Datadog’s PrivateLink endpoints, which can add significant operational overhead. NAT gateways and Transit Gateways can be expensive, especially when handling large volumes of metrics and logs.

Security is another concern. Observability data traveling over public networks or through NAT gateways can expose sensitive information to potential threats. Compliance with data residency and security regulations is also more difficult when data leaves the AWS network.

With the introduction of cross-region PrivateLink support for US1 and AP1, Datadog customers can now securely connect to Datadog endpoints from any AWS region. This expands on the existing Datadog PrivateLink integration, which previously only supported same-region connectivity.

AWS cross-region PrivateLink improves security by keeping your metrics, logs, and traces within the AWS network instead of exposing them to the public internet. It also simplifies management by removing the need for complex networking configurations. For organizations in regulated industries like healthcare and finance, this approach also makes it easier to confidently monitor multi-region workloads while adhering to data privacy standards.

The following diagram illustrates the streamlined connectivity that AWS cross-region PrivateLink provides between Datadog endpoints:

In this example, customers with Amazon EC2 instances in their private VPC in the US-WEST-2 region send telemetry data over cross-region PrivateLink to Datadog endpoints in the US1 region—all within the AWS network.
A diagram shows customers with Amazon EC2 instances in their private VPC in the US-WEST-2 region sending telemetry data over cross-region AWS PrivateLink to Datadog endpoints in the US1 region.
In this example, customers with Amazon EC2 instances in their private VPC in the US-WEST-2 region send telemetry data over cross-region PrivateLink to Datadog endpoints in the US1 region—all within the AWS network.

Optimize costs associated with log ingestion

Logs are one of the largest and most expensive sources of observability data—especially when generated at scale across multiple AWS regions. Using cross-region PrivateLink can help alleviate that. If you’re a streaming or media company generating hundreds of terabytes of logs daily in US-WEST-2, for example, you can now securely and cost-effectively send those logs to Datadog’s US1 or AP1 endpoints using cross-region PrivateLink.

To further enhance cost-efficiency, customers can pair cross-region PrivateLink with Flex Logs and Observability Pipelines:

  • Flex Logs enable you to retain large volumes of logs at a lower cost while maintaining the ability to search them when needed. This is ideal for workloads that require long-term storage but don’t need continuous querying, such as compliance, security auditing, or forensic investigations.
  • Observability Pipelines helps reduce log ingestion volume and the associated costs by enabling you to filter out unnecessary data, enrich logs with context, and route specific log streams before they reach Datadog. For example, customers can use Observability Pipelines to extract actionable insights from their AWS WAF, CloudFront, and VPC Flow Logs.

By using cross-region PrivateLink as the foundation for secure and cost-effective log transmission—and optionally layering in Flex Logs and Observability Pipelines—organizations can reduce costs across networking, storage, and ingestion while maintaining full control and visibility into their observability data.

To set up AWS cross-region PrivateLink for Datadog, follow these simple steps:

  1. Create a PrivateLink VPC endpoint in AWS for Datadog in the desired region.
  2. Configure the interface endpoint by providing the PrivateLink service name for the Datadog service you want to establish connectivity for.
  3. Test the connection to ensure that your resources can securely communicate with Datadog services.

For detailed instructions, refer to our documentation and the AWS reference architecture.

Eliminate the need for elaborate and costly connectivity configurations

Support for AWS cross-region PrivateLink connectivity for Datadog US1 and AP1 region endpoints eliminates the need for complex networking configurations while providing the benefits of improved security, easier management, and lower costs compared to NAT gateways and Transit Gateways. This approach also helps you meet compliance requirements for data residency and security regulations.

Cross-region support for AWS PrivateLink expands on the existing Datadog PrivateLink integration, which previously only supported same-region connectivity. To get started, check out our documentation or visit the AWS announcement blog for more information. If you’re new to Datadog, you can sign up for a 14-day free trial.