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

Forbes - Security
Forbes - Security
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
P
Palo Alto Networks Blog
Martin Fowler
Martin Fowler
T
Threatpost
D
Docker
S
Schneier on Security
M
MIT News - Artificial intelligence
G
Google Developers Blog
L
LINUX DO - 热门话题
J
Java Code Geeks
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
IT之家
IT之家
博客园 - Franky
C
Cyber Attacks, Cyber Crime and Cyber Security
K
Kaspersky official blog
Google DeepMind News
Google DeepMind News
N
News and Events Feed by Topic
V
Vulnerabilities – Threatpost
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
人人都是产品经理
人人都是产品经理
Spread Privacy
Spread Privacy
T
Tailwind CSS Blog
爱范儿
爱范儿
阮一峰的网络日志
阮一峰的网络日志
U
Unit 42
C
CERT Recently Published Vulnerability Notes
The GitHub Blog
The GitHub Blog
Simon Willison's Weblog
Simon Willison's Weblog
NISL@THU
NISL@THU
MongoDB | Blog
MongoDB | Blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
H
Heimdal Security Blog
Recorded Future
Recorded Future
云风的 BLOG
云风的 BLOG
SecWiki News
SecWiki News
P
Privacy International News Feed
P
Proofpoint News Feed
O
OpenAI News
B
Blog
腾讯CDC
F
Full Disclosure
Apple Machine Learning Research
Apple Machine Learning Research
T
Tor Project blog
H
Hacker News: Front Page
Project Zero
Project Zero
Hugging Face - Blog
Hugging Face - Blog
C
Cisco Blogs
S
Security Affairs

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
Monitor AWS Lambda Managed Instances with Datadog
2025-12-01 · via Datadog | The Monitor blog
Candace Shamieh

Candace Shamieh

Sumedha Mehta

Sumedha Mehta

Colten Woo

Colten Woo

Jason Mimick

Jason Mimick

Serverless computing has simplified how teams build and scale modern applications. By abstracting away infrastructure management, developers can focus purely on code. AWS Lambda, Amazon’s serverless compute service, automatically runs your code in response to events and scales appropriately, effectively removing your need to provision and manage servers. With a pay-per-use pricing model, AWS Lambda is ideal for workloads with unpredictable or intermittent traffic. Developers can simply upload their functions, while AWS Lambda handles the infrastructure, capacity planning, and scaling.

Despite AWS Lambda’s simplicity, many teams running large AI or data processing jobs have opted to use Amazon EC2 instead. For steady-state or compute-intensive workloads, they prefer Amazon EC2’s predictable performance and hardware flexibility. To help these customers minimize their operational overhead, AWS now offers Lambda Managed Instances, which enable you to run Lambda functions on any EC2 instance type, including GPUs and Graviton4. Lambda Managed Instances lets you choose the hardware that best fits your workloads while AWS manages the instance lifecycle, scaling, and patching.

As a Day-0 launch partner for AWS, Datadog provides you with full visibility into the metrics, logs, and traces emitted by your Lambda Managed Instances. With Datadog, you can monitor Lambda Managed Instances alongside your other serverless compute services in a single, unified view. This enables you to spot bottlenecks, fix errors, and determine which workloads to refactor in order to optimize concurrency, whether that means increasing throughput or reducing unnecessary parallel executions.

In this post, we’ll explore how Datadog helps you visualize performance and health with unified metrics, logs, and traces and optimize workloads to balance performance and cost.

Get full observability from day one

Once you install Datadog Serverless Monitoring, telemetry from your AWS Lambda Managed Instances will start to populate into the AWS Serverless Monitoring view in real time. If you’re already using Serverless Monitoring for AWS Lambda, any function that transitions from Lambda on-demand to Managed Instances will automatically continue to emit the same telemetry to Datadog.

View your AWS Lambda Managed Instances in the AWS Serverless Monitoring view.

Adding the Datadog Lambda extension to your functions enables you to collect invocation, duration, and error metrics from your Lambda Managed Instances. Datadog will automatically correlate them with the logs and traces that instances generate. Because Datadog supports auto-instrumentation, you’ll also get full trace propagation between upstream and downstream services when you install the Datadog Agent. With full trace propagation, you can visualize how requests flow between Managed Instances, other Lambda functions, and AWS services like S3, DynamoDB, or API Gateway.

You can review invocation metrics, open related logs, and then analyze full request traces in order to pinpoint performance bottlenecks or infrastructure errors. For example, if a GPU-backed Lambda Managed Instance shows a spike in duration or error rate, you can open a related trace to see which part of the function’s code or downstream service caused the slowdown.

Correlate telemetry data such as traces by inspecting a Lambda function.

As teams adopt Lambda Managed Instances for steady or high-performance workloads, including training AI models on GPUs or running data processing pipelines, they require the same visibility they expect from Lambda’s traditional model. Datadog provides that visibility out of the box.

Assess how EC2-backed AWS Lambda Managed Instances perform at scale

Because Lambda Managed Instances extend Lambda’s execution model onto EC2 hardware, organizations can now unify observability across both ephemeral and persistent compute. Using Datadog dashboards, you’re able to monitor the performance of functions running on Lambda Managed Instances alongside their underlying hosts’ performance and resource usage. All EC2 Instances that are managed by Lambda are automatically tagged with aws_ec2_managed-launch:lambda-managed-instances. This enables you to identify issues in your business logic, detect unexpected compute usage and rising costs, and correlate workload traffic with host resource utilization all from a single view.

Monitor your business logic alongside the underlying infrastructure using Datadog dashboards.

The dashboard can also be a starting point to launch your investigation when errors arise. For example, if you notice high network egress from a Managed Lambda Instance, you can directly inspect it from your dashboard to investigate the network traffic and mitigate costs. From here, you can investigate the instance’s function history and dive deeper into the traces of invocations with long runtime durations.

View an instance's invocation history directly from a dashboard.

Start monitoring your Lambda Managed Instances with Datadog today

By running Lambda functions on any EC2 instance type, Lambda Managed Instances bridge the gap between serverless agility and infrastructure control. Datadog helps you take full advantage of this flexibility with complete visibility into how your workloads perform, scale, and consume resources.

Visit the documentation to learn more about monitoring your AWS Lambda Managed Instances. New to Datadog and don’t already have an account? Sign up for a 14-day free trial.