feast
Python SDK for Feast
Decision gist · record as of 2026-08-14
Yes, with conditions. Feast is actively maintained, has no known vulnerabilities, and is well-suited for teams building production ML platforms that need consistent feature management across training and serving. However, it brings substantial dependencies (30 runtime packages) and requires Python 3.10+. Install if you need point-in-time correct features, multi-backend support, or a unified feature abstraction layer; avoid if you have simple, single-source feature needs or strict dependency constraints.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10.0 or later.
- Offline and online store backends (e.g., Snowflake, BigQuery, DuckDB) must be separately configured and accessible.
- Low friction installation via wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for enterprise ML platform adoption.
last release 2026-07-20 (25 days) · last repo commit 2026-08-14 · 7,208 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 670,939 downloads/mo, #5,407 on PyPI
Alternatives
Verify before relying
pip install feast
from feast import FeatureStore
import pandas as pd
from datetime import datetime
store = FeatureStore(repo_path=".")
training_df = store.get_historical_features(
entity_df=pd.DataFrame({"driver_id": [1001], "event_timestamp": [datetime(2021, 4, 12, 10, 59, 42)]}),
features=['driver_hourly_stats:conv_rate']
).to_df()- Whether the 30 runtime dependencies are all required for basic usage or if many are optional for specific backends.
- Performance characteristics and latency guarantees for online feature retrieval at scale.
- Supported Python versions beyond 3.10 (classifier lists only 3.10; requires_python specifies >=3.10.0).
What it is and what it does
Feast is a feature store designed to bridge the gap between data infrastructure and machine learning workflows. It manages feature data across two tiers: an offline store for historical batch processing and model training, and a low-latency online store for real-time inference serving. The core problem it solves is ensuring consistent, point-in-time correct features across training and serving environments—preventing data leakage and reducing the manual work of joining datasets.
Typically used by ML platform teams, Feast abstracts feature storage and retrieval behind a unified API, allowing models to remain portable as infrastructure changes. It supports multiple data sources (Snowflake, BigQuery, Redshift, Parquet, Postgres, and others via plugins) and provides materialization workflows to move computed features into the online store. The package includes a feature server for serving pre-computed features and a web UI for exploration.
Use it for
- Build training datasets with historical features for model training while ensuring no future data leaks into the training set.
- Serve pre-computed features to real-time prediction endpoints with low-latency lookups from an online store.
- Decouple ML models from underlying data infrastructure by providing a single feature access layer across batch and real-time systems.
- Manage feature materialization pipelines to synchronize offline computed features into an online store on a schedule.
- Support multiple data sources and backends without rewriting feature retrieval logic when switching data platforms.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Feast is actively maintained, has no known vulnerabilities, and is well-suited for teams building production ML platforms that need consistent feature management across training and serving. However, it brings substantial dependencies (30 runtime packages) and requires Python 3.10+. Install if you need point-in-time correct features, multi-backend support, or a unified feature abstraction layer; avoid if you have simple, single-source feature needs or strict dependency constraints.
Install
feast on PyPI
Before you install
Low friction installation via wheel distribution. Active maintenance with recent releases (25 days since last update). Requires Python 3.10+. Brings 30 runtime dependencies including data processing (pandas, numpy, pyarrow, dask), web serving (fastapi, uvicorn, gunicorn), and infrastructure tools (prometheus_client, psutil).
Requires Python 3.10.0 or later. Offline and online store backends (e.g., Snowflake, BigQuery, DuckDB) must be separately configured and accessible.
License in practice
Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for enterprise ML platform adoption.
Quickstart
pip install feast
from feast import FeatureStore
import pandas as pd
from datetime import datetime
store = FeatureStore(repo_path=".")
training_df = store.get_historical_features(
entity_df=pd.DataFrame({"driver_id": [1001], "event_timestamp": [datetime(2021, 4, 12, 10, 59, 42)]}),
features=['driver_hourly_stats:conv_rate']
).to_df()Verify before relying
- Whether the 30 runtime dependencies are all required for basic usage or if many are optional for specific backends.
- Performance characteristics and latency guarantees for online feature retrieval at scale.
- Supported Python versions beyond 3.10 (classifier lists only 3.10; requires_python specifies >=3.10.0).
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 30 packagesattrsclickcoloramadillprotobufJinja2mmh3numpypandaspyarrowpydanticpygmentsPyYAMLrequestsSQLAlchemytabulatetenacitytomltqdmtypeguardfastapistarletteuvicornuvicorn-workergunicorndaskprometheus_clientpsutilbigtreepyjwt |
| Maintenance | Actively maintained 25 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 670,939 / month, #5,407 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10 |
Evidence: feast-0.65.0-py3-none-any.whl
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