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

The Register - Security
The Register - Security
T
Troy Hunt's Blog
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
C
Cybersecurity and Infrastructure Security Agency CISA
S
Securelist
G
GRAHAM CLULEY
S
Schneier on Security
S
Secure Thoughts
Know Your Adversary
Know Your Adversary
Forbes - Security
Forbes - Security
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
The Hacker News
The Hacker News
Y
Y Combinator Blog
L
LINUX DO - 最新话题
D
Docker
S
Security @ Cisco Blogs
P
Proofpoint News Feed
V
Vulnerabilities – Threatpost
博客园_首页
T
The Blog of Author Tim Ferriss
Blog — PlanetScale
Blog — PlanetScale
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
V2EX - 技术
V2EX - 技术
NISL@THU
NISL@THU
MongoDB | Blog
MongoDB | Blog
阮一峰的网络日志
阮一峰的网络日志
P
Privacy & Cybersecurity Law Blog
C
Cisco Blogs
AWS News Blog
AWS News Blog
博客园 - 司徒正美
Martin Fowler
Martin Fowler
W
WeLiveSecurity
月光博客
月光博客
博客园 - 聂微东
N
News and Events Feed by Topic
Google DeepMind News
Google DeepMind News
D
DataBreaches.Net
T
Tenable Blog
IT之家
IT之家
MyScale Blog
MyScale Blog
N
Netflix TechBlog - Medium
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 三生石上(FineUI控件)
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
B
Blog
量子位
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
N
News and Events Feed by Topic
Vercel News
Vercel News

Datadog | The Monitor blog

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 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 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 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 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 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 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 Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Store and analyze high-volume logs efficiently with Flex Logs
2025-06-10 · via Datadog | The Monitor blog
Sid Dhingra

Sid Dhingra

Aaron Kaplan

Aaron Kaplan

The volume of logs that organizations collect from their systems is growing exponentially. These logs have increasingly diverse sources—from distributed infrastructure to data pipelines and APIs—and similarly varied forms and use cases. As a result, log data has become more and more difficult to manage. Organizations must reconcile conflicting needs for long-term retention, rapid access, and cost-effective storage. Meanwhile, existing solutions tend to create their own hurdles, from painstaking schema maintenance and complex querying to time-consuming re-indexing operations. All of this can lead to gaps in visibility and put key data out of reach when it is most urgently needed.

To address these problems, we’re pleased to introduce Flex Logs. With Flex Logs, Datadog offers a solution for centralizing all of your logs within one platform, no matter the use case. Building on the flexibility offered by Logging Without Limits™, which decouples log ingest from storage—enabling Datadog customers to enrich, parse, and archive 100% of their logs while storing only what they choose to—Flex Logs decouples the costs of log storage from the costs of querying. It provides both short- and long-term log retention for a nominal monthly fee without sacrificing visibility, enabling streamlined correlation between all of your logs, metrics, and traces.

In this post, we’ll show you how Flex Logs enables you to:

Consolidate all of your logs—no matter the use case—in one platform

Because logs are collected from so many different kinds of sources and for such wide-ranging purposes, there can be no one-size-fits-all solution to managing them. Amid this complexity, organizations often face a dilemma: to index or to archive. Indexing makes your data rapidly searchable, so it’s essential for logs you query frequently and use for real-time monitoring and alerting, such as application logs. But indexing can be resource-intensive and costly, so indexing all of your logs is impractical at scale. Archiving tends to be a better solution for logs you need to keep long-term, for compliance and other purposes, but which you query infrequently and without much urgency. If and when you do need to search them, these types of logs can be retrieved using Datadog Log Rehydration™, for example.

But in many cases, neither indexing nor archiving is a perfect solution. Logs of network activity, security events, and business transactions, for example, are often generated in enormous volumes. Compared to application logs, these types of logs are seldom queried—often only months after they are generated. At that point, however, querying them is often a matter of urgency (during investigations of potential security breaches or fraudulent activity, for example).

By decoupling storage from compute costs, Flex Logs provides a solution for long-term retention of logs without impeding query performance. With Datadog Log Management, you can now:

  • Use Standard Indexing for logs you tend to query frequently within a short time span (i.e., a few days), such as application logs
  • Use Flex for logs you collect in high volumes, retain in the relatively long term (i.e., months or years), and sometimes urgently need to query, such as security, transaction, and network logs
  • Use Flex Frozen for logs you need to retain for extended periods (up to seven years) in order to meet regulatory requirements or for auditing purposes, and want to be able to query on the fly without rehydration or context switching
Flex Logs provides solutions for use cases where neither standard indexing nor archiving is the best fit for your logs.

Take, for example, an e-commerce company that makes millions of sales a day. Visibility into the services that handle the checkout process is indispensable to many different teams within this organization. Datadog Log Management enables these teams to cost-effectively centralize all their log data in one platform by tailoring their log indexing and retention policies to their needs—eliminating the overhead of managing external storage solutions as well as the need for context switching.

For example, in order to rapidly query and alert on any failed checkouts, engineering teams keep these logs in the Standard Tier. In case of an incident causing a spike in failed checkouts, teams can respond immediately and query as much as they need to quickly identify the root cause and prevent revenue loss.

Meanwhile, operations and security teams need to retain those same logs longer-term for forensic investigation purposes. Let’s say there’s an uptick in payment errors indicating potentially fraudulent activity. The errors are coming from a specific user account with a transaction history spanning several months. By keeping their transaction logs in the Flex Tier for up to 15 months, these teams are able to store these logs at cost-effective rates without compromising their ability to quickly investigate incidents like this one.

What’s more, in order to comply with regulatory requirements, the organization must retain these same logs for up to seven years. By enabling Flex Frozen, teams can ensure compliance and keep these logs fully searchable in Datadog, without any need for rehydration or external storage, via Archive Search.

Configuring Standard, Flex, and Flex Frozen storage tiers for logs needed in the short, medium, and long term.

Or, consider a video streaming platform that generates terabytes of CDN logs every day. Despite the volume of these logs, network analysts may need to query them only a few times a day. By keeping them in Flex Tier these teams can instantly query their network logs without incurring the typical costs of indexing such massive volumes of data.

Configuring Flex Tier storage for network logs in order to control costs while ensuring rapid searchability.

Lastly, consider a financial services organization that needs to keep track of employee actions in its cloud environment for auditing purposes. By storing its user activity logs from CloudTrail in both the Flex and Frozen Tiers, this organization ensures a fast response to regulatory requests, without requiring context switching between different tools. Since these logs won’t be needed for DevOps incidents, Standard Indexing can be skipped for improved cost control.

Configuring Flex and Flex Frozen storage tiers for user activity logs.
Configuring Flex and Flex Frozen storage tiers for user activity logs.

Rein in costs without sacrificing visibility

Flex Logs provides log retention at a commodity storage price point—$0.05 per million events per month—without impeding querying. It allows you to allocate a fixed level of compute capacity to individual teams within your organization according to their needs at a fixed monthly rate. With Flex Tier storage, you can retain logs for three, six, or fifteen months. With Frozen Tier, you can extend that retention up to seven years while maintaining full searchability—ideal for organizations with compliance, auditing, or forensic investigation needs. And because Flex Frozen is fully managed by Datadog, it eliminates the overhead of maintaining external object storage and switching between tools.

Flex Logs enables you to rapidly search any and all of your logs in one place without ballooning storage costs. The Log Explorer allows you to toggle between searching only standard-tier, fully indexed logs (e.g., frequently queried data such as application logs) and expanding your search to include Flex Logs, while Frozen Tier can be directly searched in Datadog via Archive Search. As a result, Flex Logs allows you to avoid the logistical overhead of maintaining additional tools, writing complex query statements, or performing targeted re-indexing operations. It also allows for smooth correlation with your other data in Datadog, including metrics, traces, and more.

Maximize the value of your logs

Flex Logs provides organizations with a log management solution whose costs won’t multiply when their storage volumes do. It enables comprehensive observability at scale, giving organizations granular control over fast-growing volumes of logs and ensuring teams quick, consistent access to the data they need.

Datadog users can learn more in our docs to get started with Flex Logs today. Sign up for the preview of Flex Frozen and Archive Search to get early access.

If you’re brand new to Datadog, you can sign up for a 14-day free trial.