A comparison of OpenObserve and Datadog focusing on how their architectures impact cost, performance, and usability. It highlights Datadog’s indexing-heavy, multi-product approach versus OpenObserve’s unified, schema-on-read design.
How pricing models differ between OpenObserve and Datadog
Why Datadog users often adopt a “data diet”
Key architectural differences: multi-product vs unified system
Impact of indexing vs schema-on-read on performance and cost
How unified query engines improve debugging and correlation
Querying approaches: SQL vs PromQL across observability data
Trade-offs between flexibility, scalability, and operational overhead
This content analyzes the differences between OpenObserve and Datadog in handling logs, metrics, and traces. It explains how Datadog’s pricing model—based on hosts, containers, and indexed logs—can drive higher costs, often forcing teams to limit data ingestion (“data diet”). In contrast, OpenObserve adopts a data-ingestion and storage-based pricing model using object storage, reducing reliance on expensive indexing.
Architecturally, Datadog functions as a multi-product platform with separate systems for logs, metrics, and traces, each with its own query layer. OpenObserve, however, uses a unified architecture with shared storage and a single query engine, simplifying cross-signal correlation and debugging.
The comparison also explores technical trade-offs: Datadog’s indexing-heavy log system versus OpenObserve’s schema-on-read columnar storage, which improves ingestion speed and compression. Additionally, OpenObserve supports SQL across all signals and integrates PromQL for metrics, offering flexibility in querying.
Simran specializes in DevOps, cloud-native technologies, and observability, with hands-on experience in Kubernetes, Docker, and AWS. Creates practical, accessible technical content and solutions that help teams simplify complex workflows and improve system reliability.
