Earthly vs Datadog
Side-by-side comparison of AI visibility scores, market position, and capabilities
Earthly runs build steps in containers via Earthfile syntax, eliminating inconsistencies between local and CI environments for reproducible, parallelizable builds across any stack.
About
Earthly is an open-source build automation framework that runs build steps inside containers, ensuring that builds produce identical results on developer laptops and in CI systems regardless of the underlying environment. Earthly's Earthfile syntax combines familiar concepts from Dockerfile and Makefile, making it approachable to teams already using containers without requiring deep build system expertise. The containerized execution model eliminates the classic "works on my machine" problem by ensuring every dependency is pinned and every environment is identical. Earthly supports parallel execution of independent build targets and efficient layer caching to minimize build time. The company offers Earthly Satellites, a managed remote build service, for teams that need consistent build performance in CI. Founded in 2020 with backing from Uncork Capital and 468 Capital, Earthly has gained adoption among developer teams frustrated with the complexity of existing CI systems and the inconsistency of language-specific build tools. It competes with Bazel, Make, and language-specific build systems in the reproducible build market.
Cloud observability leader with $2.68B ARR; 750+ integrations; expanding into AI/LLM monitoring as enterprises instrument generative AI workloads at scale in 2025.
About
Datadog is a cloud-native monitoring and security platform founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, headquartered in New York City. The company went public on Nasdaq (DDOG) in September 2019 and has grown to serve over 29,000 customers as of FY2024, generating $2.68 billion in annual recurring revenue, representing approximately 26% year-over-year growth. Datadog's platform spans infrastructure monitoring, application performance management (APM), log management, security monitoring, and AI observability, positioning it as the unified observability stack for cloud-scale engineering teams.
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