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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
This Month in Datadog - December 2025
2025-12-11 · via Datadog | The Monitor blog

For our last episode of 2025, we’re focusing on Datadog releases announced at AWS re:Invent. Join Jeremy to see how you can manage logs at petabyte scale in your infrastructure, eliminate unneeded costs in Amazon S3 buckets, build agentic workflows, and detect credential leaks.

Later in the episode, Scott spotlights how you can connect your AI agents to Datadog tools and context with our MCP Server. After that, Kai shows how Bits AI SRE can help you investigate alerts, coordinate incidents, and more. We also take a look at how parts of the cloud landscape are changing with Datadog’s 2025 State of Cloud Security report and State of Containers and Serverless report.

When you’re done watching the episode, visit our re:Invent microsite for a full list of our announcements.

New features

Ingest prompts and map them onto Datadog data with our MCP Server

Having the right context is an essential part of real-time data analysis by AI agents. Datadog MCP Server enables you to connect AI agents, such as Amazon Kiro, to Datadog tools and context. Once connected, you can ask questions about your production systems and get insights backed by Datadog data.

Let Bits AI SRE investigate alerts and coordinate incidents for you

Identify root causes and remediate issues faster with Bits AI SRE. Our new product helps you spend less time investigating alerts and coordinating incidents, so you can stay focused on building and shipping great software. Bits AI SRE is now generally available.

Manage logs at scale in your own environment with CloudPrem

Teams need a way to balance complete visibility against retention needs and residency requirements. Datadog CloudPrem is a hybrid log management solution that runs in your infrastructure and stays fully integrated with our platform. Filter your CloudPrem logs with Log Explorer, preprocess data before indexing with Observability Pipelines, and more with our new product, which is now available in Preview.

See what’s driving costs in your Amazon S3 buckets with Storage Management

Bucket-level metrics can show how much you spend on storage but not what drives those costs. Datadog Storage Management breaks down cost drivers to the prefix level so you can attribute spend to specific sources, such as teams, services, and workloads. Combined with object count and age metrics, this visibility also helps you spot opportunities to move cold data to lower-cost tiers.

Create AI agents that reason through complex decisions with Agent Builder

To help workflows adapt to real-world complexity, teams often try to hardcode logic branches for every possible outcome. Datadog Agent Builder lets you create AI agents that analyze data, reason through complex decisions, and adapt to changing inputs. With this new feature, you can define an agent’s goals using natural language, add prompts, and control which data sources and tools it can access.

Detect credential leaks before they compromise your environment with Secret Scanning

Continuously monitor your source code, repositories, and CI/CD pipelines for credential leaks with Datadog Secret Scanning. When a potential leak is found, Datadog automatically verifies it with the corresponding third-party provider, such as AWS, to determine if the credential is active. This new feature also integrates with CI/CD workflows and enforces pre-commit and pre-merge checks that stop keys from entering repositories.

Additional updates

More new features and updates released this month:

See you next month

This Month in Datadog is a monthly roundup of our latest features, product announcements, and more. Subscribe to our YouTube channel to get notified when future episodes are live.

In the meantime, check out our release notes for a full list of new features and updates. Or see them in action by logging in to the Datadog platform or signing up for a 14-day free trial. See you next month!