upstash-vector
Serverless Vector SDK from Upstash
Decision gist · record as of 2026-08-14
Yes, if you are already using Upstash Vector or committed to a serverless vector database architecture. The package is straightforward, has low install friction, and is backed by professional support. However, the aging maintenance status (533 days since last release) and Alpha classification warrant caution for mission-critical production systems; verify that your use case aligns with Upstash's roadmap and support commitments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN environment variables or explicit URL and token parameters; an active Upstash Vector database must exist.
- Low install friction with a single runtime dependency (httpx).
- Maintenance status is aging—last commit was 2025-10-21 and the package has not been updated in 533 days, though the project is marked GA and receives Upstash Professional Support.
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions.
last release 2025-02-27 (533 days) · last repo commit 2025-10-21 · 18 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 354,304 downloads/mo, #7,296 on PyPI
Alternatives
Verify before relying
pip install upstash-vector
from upstash_vector import Index
index = Index.from_env() # Reads UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN
index.upsert(vectors=[("id1", [0.1, 0.2])])
res = index.query(vector=[0.6, 0.9], top_k=5)- Whether the aging maintenance status (533 days since last release) affects stability or feature completeness for production use.
- Performance characteristics and throughput limits for large-scale vector operations.
- Whether sparse and hybrid index types are fully tested and production-ready.
What it is and what it does
Upstash Vector is a Python client for Upstash's serverless vector database service. It provides a REST-based interface to store and query vectors without managing your own infrastructure. The package handles three index types—dense (traditional embeddings), sparse (keyword-based), and hybrid (combining both)—and supports optional metadata filtering and associated data fields.
You initialize an Index with credentials from the Upstash console, then call upsert() to insert or update vectors and query() to find similar vectors by similarity score. The client abstracts away HTTP details via httpx and supports multiple input formats (tuples, dicts, or Vector objects). It is classified as Alpha in development status but marked GA by Upstash and backed by their professional support.
Use it for
- Store and retrieve embeddings from language models for semantic search applications.
- Build recommendation systems by querying similar vectors based on user or item embeddings.
- Implement metadata-filtered vector search to narrow results by categorical or structured fields.
- Use with embedding models to automatically vectorize and store raw text data without manual embedding.
- Prototype vector-based features without provisioning and managing a dedicated vector database.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Upstash Vector or committed to a serverless vector database architecture.
The package is straightforward, has low install friction, and is backed by professional support. However, the aging maintenance status (533 days since last release) and Alpha classification warrant caution for mission-critical production systems; verify that your use case aligns with Upstash's roadmap and support commitments.
Install
upstash-vector on PyPI
Before you install
Low install friction with a single runtime dependency (httpx). Maintenance status is aging—last commit was 2025-10-21 and the package has not been updated in 533 days, though the project is marked GA and receives Upstash Professional Support.
Requires UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN environment variables or explicit URL and token parameters; an active Upstash Vector database must exist.
License in practice
MIT license is permissive, allowing commercial and private use with minimal restrictions.
Quickstart
pip install upstash-vector
from upstash_vector import Index
index = Index.from_env() # Reads UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN
index.upsert(vectors=[("id1", [0.1, 0.2])])
res = index.query(vector=[0.6, 0.9], top_k=5)Verify before relying
- Whether the aging maintenance status (533 days since last release) affects stability or feature completeness for production use.
- Performance characteristics and throughput limits for large-scale vector operations.
- Whether sparse and hybrid index types are fully tested and production-ready.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagehttpx |
| Maintenance | Aging 533 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 354,304 / month, #7,296 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonTopic :: DatabaseTopic :: Database :: Front-EndsTopic :: Software Development :: Libraries |
Evidence: upstash_vector-0.8.0-py3-none-any.whl
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