phoenix VS flyte

Compare phoenix vs flyte and see what are their differences.

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phoenix flyte
4 31
2,730 4,853
13.2% 3.8%
9.9 9.8
7 days ago 4 days ago
Jupyter Notebook Go
GNU General Public License v3.0 or later Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

phoenix

Posts with mentions or reviews of phoenix. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-15.

flyte

Posts with mentions or reviews of flyte. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-15.

What are some alternatives?

When comparing phoenix and flyte you can also consider the following projects:

dev-gpt - Your Virtual Development Team

metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!

OpenLLM - Run any open-source LLMs, such as Llama 2, Mistral, as OpenAI compatible API endpoint in the cloud.

argo - Workflow Engine for Kubernetes

MetaGPT - 🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming

temporal - Temporal service

EVAL - EVAL(Elastic Versatile Agent with Langchain) will execute all your requests. Just like an eval method!

kubeflow - Machine Learning Toolkit for Kubernetes

dvclive - πŸ“ˆ Log and track ML metrics, parameters, models with Git and/or DVC

Celery-Kubernetes-Operator - An operator to manage celery clusters on Kubernetes (Work in Progress)

aider - aider is AI pair programming in your terminal

Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.