Datadog vs Zscaler

Detects AI-assisted pull requests from coding assistants such as Claude Code, Cursor and GitHub Copilot and compares them on adoption, PR throughput, cycle time, change failure rate and daily cost per active user.

Conversational AI interface for querying Datadog metrics, logs, traces and monitors in natural language and generating dashboards and notebooks, accessible from Datadog, Slack or mobile.

Datadog's Infrastructure-family product for shared GPU fleets across cloud, on-prem and neocloud providers: it links device health, cost and performance to the workloads and teams using them, alerts on unmet GPU requests, thermal throttling and ECC/XID errors, forecasts GPU demand and recommends optimisations such as reclaiming GPUs held by zombie processes. The page markets alerting, forecasting and recommendations but does not name a model or AI mechanism behind them.

Datadog's built-in AI engine: it continuously analyses metrics, traces and logs across the platform to raise anomaly alerts without configuration, detect faulty deployments by comparing code versions, run automated root-cause analysis on critical failures and surface tag-based insights and impact analysis. Available inside Infrastructure Monitoring, APM, Log Management and RUM rather than sold on its own.

Traces, evaluates and monitors LLM and AI-agent applications in production, with offline experimentation on datasets built from real traces.

Model Context Protocol server that gives AI coding agents such as Claude Code, Cursor and Codex secure real-time access to Datadog logs, metrics and traces under existing RBAC controls.

Governance layer for employee use of generative AI that discovers shadow AI applications, applies allow, block or coach policies per user, and monitors prompts and responses against more than 100 data loss prevention dictionaries to stop sensitive data leaving through AI tools.

AI security posture management that auto-discovers and classifies AI models, agents and services across Azure AI Foundry, Amazon Bedrock, Google Vertex AI and unmanaged providers, correlates the sensitive data connected to them, and maps findings to frameworks including NIST AI RMF 600-1 and the EU AI Act.

Runs AI models over the security data flowing through the Zero Trust Exchange, using attack graphs, user risk scoring and threat intelligence to score the probability of a breach, forecast an attacker's next tactics, and recommend policy changes before the attack completes.

AI-driven detection and diagnosis of digital-experience issues across the network and application delivery path.

Automated adversarial testing for AI applications and agents that runs simulated attacks using 25+ predefined and custom probes across text, image, voice and document inputs, scoring results for security, safety, hallucination and business alignment against MITRE ATLAS, NIST AI RMF and the OWASP LLM Top 10.