Amazon open sources DSSTNE, its library for building deep learning models, says it's 2.1X faster than Google's TensorFlow when run on specific AWS configuration
Context & Ripple Effects
Amazon releasing DSSTNE as open source is the opening move in a decade-long AWS pattern: give away ML software, monetize the compute it runs on. The benchmark claim matters because it was run on AWS's own configuration, positioning Amazon's infrastructure as the reference environment for deep learning rather than a neutral host for Google's framework.
The subsequent coverage shows where this leads: Elastic Inference to cut GPU inference costs, Neo-AI to make models portable across environments, then custom Trainium silicon with TensorFlow support. DSSTNE is the first link in a chain that ends with AWS owning more of the AI stack.
First-order effects
- Developers get Amazon's production-grade recommendation-modeling code for free, and Google's TensorFlow now has a public head-to-head benchmark claim — 2.1X on AWS hardware — attached to a rival's name.
Second-order effects
- Framework competition pushes AWS deeper into the stack: within a few years it ships Elastic Inference for cheaper inference and Neo-AI for cross-environment portability, each making AWS-hosted models cheaper or easier to run than alternatives.
Third-order effects
- If the pattern holds, open-source ML software functions as a funnel to proprietary infrastructure — culminating in AWS designing its own training and inference chips (Trainium, per the Trainium2 analysis) and selling model customization services like Nova Forge, so the free layer captures demand while the paid layers capture margin.
The trend: Cloud providers are using open-source ML tooling as the top of a vertically integrated funnel that ends in their own silicon and managed AI services.