ML Pipeline Frameworks Landscape 2026
The 5 published ml pipeline frameworks filed under mlops tools, positioned on the same axes as the whole category: public evidence and recent publication activity, measured against every mlops tools product rather than only against each other.
On smaller screens, browse the complete list below. Each tool links to its review, pricing, alternatives, and comparisons.
Where Each Tool Lands
Strong & Steady (2)
Building Activity (3)
- KedroFree (open source)
ML Pipeline Framework
Public signal strength: 42nd percentile · Recent release activity: 88th percentile
GitHub stars:11.0k - FlyteFree (open source)
ML Pipeline Framework
Public signal strength: 8th percentile · Recent release activity: 58th percentile
GitHub stars:7.5k - ZenMLFree tier
ML Pipeline Framework
Public signal strength: 0th percentile · Recent release activity: 75th percentile
How to read this view
A tool must show measured activity on at least 2 different platforms, at least one of which must be a primary source (Google Trends, GitHub, Docker Hub, npm, PyPI, Hugging Face and Stack Overflow); Hacker News and Product Hunt can supply the second. The horizontal position shows that evidence: Measured activity on each qualifying platform (Google Trends, GitHub, Docker Hub, npm, PyPI, Hugging Face, Stack Overflow, Hacker News and Product Hunt), log-normalized and percentile-ranked within the category. Each platform counts once and is capped, so breadth of evidence counts for more than a single large number. The vertical position shows Recent verified first-party publication activity — a GitHub release, or an npm, PyPI, Docker Hub or Hugging Face publish. A product with no public repository or package cannot have this signal, so tools without one are listed rather than positioned. Both percentiles are computed across the whole mlops tools category, so a product keeps the same position here as on the category chart. This measures how much verifiable public evidence exists for a tool. It is not a measure of product quality, market share, customer count, or enterprise adoption. Missing evidence is shown as missing, never converted to zero. A tool we could not measure is listed without a position rather than placed at the bottom of the scale.