promptflow vs ray
promptflow and ray share 24 technologies: Flask, FastAPI, azure-storage-blob, pyarrow, flask-cors, starlette, AIOHTTP, uvicorn, SQLAlchemy, azure-identity, pyjwt, cryptography, keyring, nltk, rouge-score, tiktoken, LangChain, jinja2, pillow, strictyaml, jsonschema, azure-core, OpenTelemetry, and psutil. Both are written in Python.
What they share and what sets them apart
promptflow
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
Python11.3kRead on November 12, 2026, at commit 33a68c9What is promptflow built with?Only in promptflow
In common
ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Python44.3kRead on November 10, 2026, at commit c99c603What is ray built with?Only in ray
- aimrecords
- aimrocks
- amqp
- array-record
- arro3-core
- async-lru
- azure-common
- azure-datalake-store
confluent-kafka
- delta-kernel-rust-sharing-wrapper
- diskcache
expiringdict
- feather-format
- gitdb
google-cloud-core
h5py
- huey
kombu
- partd
pickleshare
- py-partiql-parser
- pylance
- pymongo
- s3torchconnectorclient
- s3transfer
taskiq
tensorboard-data-server
- zict
mcap
- aioboto3
- MongoDB
- Redis
- adlfs
pyiceberg
- smart-open
- pymysql
clickhouse-connect
daft
deltalake
- gcsfs
lance-namespace
- obstore
- Celery
delta-sharing
- fastavro
hudi
- pyzmq
- s3fs
- s3torchconnector
- taskiq-redis
webdataset
- Hugging Face
- Google Cloud Storage
- Kafka
- Databricks
- adagio
- asgiref
- graphene
- graphql-core
- graphql-relay
- jupyter-core
jupyterlab-server
pygame
pymars
werkzeug
- Ray
pyspark
- Lightning
raydp
- anyio
tornado
- React
react-router-dom
- accessible-pygments
- alabaster
- cycler
fonttools
jupyterlab-pygments
pydata-sphinx-theme
- soupsieve
sphinx-design
- tinycss2
- aim-ui
- alive-progress
bokeh
contourpy
- glfw
halo
- jupyter-server-terminals
- jupyterlab-widgets
- matplotlib-inline
- Plotly
prompt-toolkit
pyglet
pyopengl
- spinners
- Textual
- widgetsnbextension
- xyzservices
- Gradio
- ipywidgets
matplotlib
- Rich
- pydot
- alembic
peewee
sqlglot
- adal
- argon2-cffi
- argon2-cffi-bindings
- flask-basicauth
- gcs-oauth2-boto-plugin
google-auth-oauthlib
- google-oauth
- google-reauth
- id
itsdangerous
joserfc
- jwcrypto
- msal
- msal-extensions
- oauth2client
oauthlib
- pyu2f
requests-oauthlib
google-auth-httplib2
- albucore
- autograd
ax-platform
- bayesian-optimization
- bitsandbytes
botorch
- cma
cmdstanpy
- crcmod
cupy-cuda100
cupy-cuda12x
cupy-cuda13x
- einops
- fairscale
- gpytorch
gradio-client
- gym
jaxlib
- labmaze
- lightning-utilities
- linear-operator
- llvmlite
lm-eval
- minigrid
- ml-dtypes
- mlflow-skinny
- moocore
mosaicml
- ONNX
onnx-ir
- onnxruntime
onnxscript
openai-whisper
- opt-einsum
optax
- orbax-checkpoint
peft
prophet
pymoo
pyro-ppl
- pytorch-ranger
- sacrebleu
safetensors
scikit-image
- sentencepiece
skops
stanio
statsforecast
- tensorflow-estimator
tf-keras
timm
tokenizers
- torch-geometric
torchtext
- triton
- gymnasium
- torch
- transformers
- TensorFlow
jax
torchvision
- xgboost
lightgbm
- scikit-learn
- MLflow
- tensorflow-metadata
accelerate
- datasets
- pytorch-lightning
- comet-ml
- dm-env
evaluate
flax
- Keras
- torchmetrics
- aim
albumentations
ale-py
configspace
deepspeed
diffusers
- dm-control
- hpbandster
- hyperopt
- nevergrad
- open-spiel
- Optuna
pettingzoo
- supersuit
tensordict
tensorflow-datasets
tensorflow-probability
torchaudio
- bleach
- defusedxml
et-xmlfile
imageio
imageio-ffmpeg
- imagesize
- jsonlines
- linkify-it-py
lxml
- mako
- markdown
markdown-it-py
markupsafe
- mdit-py-plugins
- mdurl
mistune
- moviepy
myst-parser
- nbconvert
- nh3
- openpyxl
- pandocfilters
- readme-renderer
ruamel-yaml
ruamel-yaml-clib
- tinyscaler
webencodings
- xlrd
- xmltodict
- docutils
- beautifulsoup4
av
- decord
jupytext
- nbformat
- torchcodec
- annotated-types
- fastjsonschema
- fqdn
- jsonschema-path
- jsonschema-specifications
- openapi-spec-validator
- pathvalidate
- pydantic-core
- referencing
- rfc3339-validator
- rfc3986
- rfc3986-validator
- validators
- Pydantic
- openapi-schema-validator
- aiobotocore
- applicationinsights
- aws-sam-translator
aws-xray-sdk
- azure-cli-core
- azure-cli-telemetry
- azure-mgmt-compute
- azure-mgmt-core
- azure-mgmt-network
- azure-mgmt-resource
- azure-mgmt-resource-deployments
- boto
- cloud-tpu-diagnostics
- colorlog
- execnet
- Gunicorn
- log-symbols
- ml-goodput-measurement
- msrestazure
- opencensus-context
- opentelemetry-api
opentelemetry-instrumentation
opentelemetry-instrumentation-asgi
opentelemetry-semantic-conventions
opentelemetry-util-http
- python-json-logger
- supervisor
- boto3
- botocore
prometheus-client
kubernetes
opentelemetry-exporter-prometheus
- docker
opentelemetry-exporter-otlp-proto-grpc
opentelemetry-instrumentation-fastapi
- ray-haproxy
- typeface-roboto
- MUI
usehooks-ts
- lowlight
react-window
react-icons
- @mui/icons-material
- js-yaml
Side by side
Frequently asked questions
Which is bigger, promptflow or ray?
The proof: ray-project/ray's code at commit c99c603
Which has more GitHub stars, promptflow or ray?
The proof: ray-project/ray on GitHub
Which has more tests, promptflow or ray?
The proof: ray-project/ray's code at commit c99c603
Which was updated more recently, promptflow or ray?
The proof: microsoft/promptflow's commits on GitHub
Which has more commits signed by AI, promptflow or ray?
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