Datadog Expertise
Operationalize AI-driven incident response with Datadog Bits AI SRE to automatically investigate alerts, surface root cause faster, and reduce operational friction across your engineering teams.
Bits AI SRE acts as an extension of your on-call team, automatically investigating every alert the moment it fires. By analyzing metrics, logs, traces, and infrastructure data in real time, it applies experienced SRE reasoning at machine scale to identify issues faster and reduce manual triage.
Instead of assembling large war rooms and manually correlating signals, Bits AI surfaces the root cause in minutes. This reduces mean time to resolution, minimizes escalation noise, and enables teams to resolve incidents with fewer responders and less downtime.
Bits AI provides actionable insights and recommended next steps directly within alerts and collaboration tools. By integrating with your telemetry, service metadata, and organizational context, it ensures every engineer can confidently diagnose and remediate issues.
With built-in feedback loops, Bits AI improves over time by learning from past investigations and outcomes. Combined with enterprise-grade controls like RBAC and secure data handling, organizations can adopt AI in production while maintaining governance and reliability.
Bits AI integrates directly with Datadog and your broader ecosystem, including tools like Slack and Microsoft Teams. This brings investigation insights to where teams already work and enables faster communication and more efficient incident response.
Operationalize Datadog LLM Observability with RapDev to bring visibility, control, and reliability to AI applications, reducing risk and enabling teams to confidently scale LLM-powered systems in production.
Trace prompts, model behavior, and tool interactions to understand how LLM outputs are generated. By instrumenting metrics, logs, and traces, teams gain real-time visibility into performance, errors, and usage.
Detect hallucinations, inconsistencies, and unsafe outputs early with evaluation-driven guardrails. This ensures reliable, consistent AI behavior across applications and use cases.
Monitor token usage, latency, and request volume to optimize performance and prevent cost overruns. Teams gain clear insight into usage patterns across models and services.
Protect against prompt injection and sensitive data exposure with built-in detection and controls, enabling safe and compliant AI adoption.
Test prompts and models, evaluate performance, and catch regressions early. With ongoing visibility and feedback loops, teams continuously improve LLM reliability and efficiency.
Arlo, by RapDev, is a suite of AI Agents designed to automate and enhance your Datadog workflows
Automatically start on clean up before your VMs crash. Alert fires due to a Linux VM running out of disk space? Arlo quickly figures out the top files by size within the directory and prompts you to archive a recent log file that is overfilling the mount.
Let your engineers engineer—not spend all their time diagnosing issues. Users reporting strange behavior with their application deployments? Arlo investigates your kubernetes cluster, discovers that a specific deployment is saturating some of the nodes in the cluster, and provides advice on ways this can be blocked and prevented in the future.
Avoid the chaos and streamline root cause analysis. VM hosting a .NET app being blamed for poor application performance and you want to quickly rule out system performance issues? Arlo runs a series of commands on the VM to provide information about memory pressure which could be causing the application issue and lists out the applications that need to be investigated further to bring down memory consumption.
Accelerating network troubleshooting just got easier. Paged by an SRE to rule out network issues based on strange behavior in your environment? Arlo logs into your network switches and run commands to narrow in on evidence of a spanning tree issue with advice on what to update your switch configuration to resolve it.
Test prompts and models, evaluate performance, and catch regressions early. With ongoing visibility and feedback loops, teams continuously improve LLM reliability and efficiency.
600
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Implementations
10
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Deployed Agents
110
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US-Based Engineers
"RapDev just comes in and becomes a part of the team. RapDev’s implementation has helped make troubleshooting and getting to the bottom of incidents much, much faster."
We go further and faster when we collaborate. Geek out with our team of engineers on our learnings, insights, and best practices.
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