
Amazon Redshift ML
Create machine learning models using SQL
- Web
4 alternatives · 1 question answered
The directory
35 products matching your filters. Each one described on the same fields, so two tools can be read against each other rather than against their own marketing.
Showing 1–24 of 35

Create machine learning models using SQL
4 alternatives · 1 question answered

The world's most popular data science platform
4 alternatives · 5 questions answered

Scalable machine learning on Apache Spark
4 alternatives · 3 questions answered

Build, train, and deploy machine learning models at scale
3 alternatives · 3 questions answered

Enterprise-grade machine learning service
3 alternatives · 5 questions answered


Open-source MLOps platform for experiment tracking and orchestration
1 alternative · 3 questions answered



Unified analytics platform for data engineering and data science
4 alternatives · 4 questions answered


Data version control for machine learning projects
4 alternatives · 2 questions answered

Open-source AI orchestration framework for LLM applications
3 alternatives · 3 questions answered


Statistical analysis software for data science
4 alternatives · 5 limitations documented


Interactive computing across all programming languages
2 alternatives · 3 questions answered

Deep learning API for humans
3 alternatives · 5 questions answered


LLM engineering platform for testing and evaluating AI agents in production
3 alternatives · 3 questions answered

Data framework for LLM applications
4 alternatives · 3 questions answered

Open source platform for managing the ML lifecycle
4 alternatives · 5 questions answered

Metadata store for MLOps
3 alternatives · 4 questions answered

Unified API gateway routing requests across 500+ models from 80+ providers
4 alternatives · 3 questions answered
Head to head
Pairings from this slice of the directory, each one checked by two reviewers who agreed a buyer would weigh the two against each other.
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How this works
Pricing, platforms and feature lists are taken from each vendor's own pages. Limitations, answered questions and alternatives are researched separately and carry a source URL on the page that states them. Every product is described on the same fields, so two tools can be read against each other rather than against their own marketing.
Because we did not collect any. Softwr hosts no reviews, and a rating aggregated from somewhere else is not ours to publish as though it were an assessment we made. What is shown instead is what a tool costs, where it runs, and what it is concretely bad at.
A researcher proposes them, then two independent reviewers judge one question each: would a buyer evaluating this product seriously consider that one instead. 225 of 1,841 candidates failed and were deleted, including a font inspector matched to a dark mode extension. Only pairings that survived both rounds are published.
Not to be listed. A vendor can buy a place in the results on the home page, on a category listing or on the catalogue, and where they do the result carries a Sponsored label. Everything not carrying that label is ordered on the same criteria as everything else, and a paid place never changes where an unpaid one lands: the editorial order flows around it. Where a link earns a commission that is disclosed on the page carrying it.
Prices and plan limits move without notice, so treat anything with a number on it as a starting point and check the vendor before you buy. Where a claim could go stale it carries the source it came from, which is the fastest way to tell.
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Anyone can submit a tool. A person reads every submission, checks the pricing against the vendor’s own page, and nothing is published until it passes.