From Datadog to Open Source Observability (Step-by-Step Migration + Lessons Learned)
Is your Datadog bill growing faster than your business? Learn how a fast-scaling engineering team transitioned from Datadog to a modern, high-performance, open-source observability stack—without downtime, without code changes, and in under a few hours. In this webinar, we sit down with Debo Ray, CEO of DevZero, and Chatania, Solutions Architect at OpenObserve, to walk through a real-world observability migration from Datadog to OpenObserve, including timelines, costs, dashboards, traces, and lessons learned. ▬▬▬ What You’ll Learn ▬▬▬ → Why DevZero chose Datadog initially and what finally pushed them to look elsewhere → How unpredictable billing and sampling trade-offs created hidden engineering overhead → Why AI-driven development is increasing telemetry volume → The step-by-step Datadog migration strategy: dashboards, alerts, logs, metrics, and traces → How OpenObserve’s SQL-based query language compares to Datadog’s proprietary syntax → Why change management (bookmarks, PagerDuty rules, team habits) matters more than the software migration itself → The truth about Datadog pricing vs modern alternatives → How OpenTelemetry (OTel) eliminates vendor lock-in from the start → What to do today if your Datadog contract renews in 90 days ▬▬▬ 🔗 Resources ▬▬▬ 🎓 Register for upcoming webinars: https://openobserve.ai/webinars-videos/ 🚀 OpenObserve documentation: https://openobserve.ai/docs/getting-started/ 👨💻 Try OpenObserve for free: https://cloud.openobserve.ai/web/login 💬 Join the community: https://openobserve-community.slack.com/ ▬▬▬ 🛠️ Tools & Technologies Mentioned ▬▬▬ • OpenObserve • Datadog • OpenTelemetry (OTel Collector, auto-instrumentation) • Kubernetes (Helm chart deployment, DaemonSet architecture) • eBPF (zero-code auto-instrumentation) • Jaeger (distributed tracing, open-source) • PagerDuty (alerting workflows) • AWS (EKS, Bedrock), GKE • AI-assisted development (AI agents)
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