Comparison
fastembed vs awesome
Verdict
Pick fastembed when license: fastembed is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, fastembed is Apache-2.0.
Markdown twin · fastembed alternatives · awesome alternatives
GraphCanon updated today
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Trust & integrity
| Signal | fastembed | awesome |
|---|---|---|
| Maintenance | Active (18d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- fastembed
- Fast, Accurate, Lightweight Python library to make State of the Art Embedding
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- fastembed
- 3.1k
- awesome
- 484k
Forks
- fastembed
- 213
- awesome
- 36k
Open issues
- fastembed
- 137
- awesome
- 92
Language
- fastembed
- Python
- awesome
- -
Adopt for
- fastembed
- -
- awesome
- -
Persona
- fastembed
- -
- awesome
- -
Runtime
- fastembed
- -
- awesome
- -
License
- fastembed
- Apache-2.0
- awesome
- CC0-1.0
Last pushed
- fastembed
- Jun 23, 2026
- awesome
- Jun 30, 2026
Categories
- fastembed
- LLM Frameworks, Data & Retrieval, Vector Databases
- awesome
- LLM Frameworks
Trust and health
Days since push
- fastembed
- 18d
- awesome
- 11d
Open issues (now)
- fastembed
- 137
- awesome
- 92
Owner type
- fastembed
- Organization
- awesome
- User
Full report
- fastembed
- Trust report
- awesome
- Trust report
Choose fastembed if…
- License: fastembed is Apache-2.0, awesome is CC0-1.0.
- Tags unique to fastembed: embeddings, python, rag, retrieval-augmented-generation.
- Also covers Data & Retrieval, Vector Databases.
When NOT to use fastembed
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Choose awesome if…
- License: awesome is CC0-1.0, fastembed is Apache-2.0.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 3.1k) - visibility, not fit.
When NOT to use awesome
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (qdrant/fastembed) · observed Jul 11, 2026
- GitHub forks (qdrant/fastembed) · observed Jul 11, 2026
- Last push (qdrant/fastembed) · observed Jun 23, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: fastembed 3.1k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between fastembed and awesome?
- fastembed: Fast, Accurate, Lightweight Python library to make State of the Art Embedding. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose fastembed over awesome?
- Choose fastembed over awesome when License: fastembed is Apache-2.0, awesome is CC0-1.0; Tags unique to fastembed: embeddings, python, rag, retrieval-augmented-generation; Also covers Data & Retrieval, Vector Databases.
- When should I choose awesome over fastembed?
- Choose awesome over fastembed when License: awesome is CC0-1.0, fastembed is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 3.1k) - visibility, not fit.
- When should I avoid fastembed?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- When should I avoid awesome?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is fastembed or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 3,085). Stars measure visibility, not whether either tool fits your constraints.
- Are fastembed and awesome open source?
- Yes - both are open-source projects on GitHub (fastembed: Apache-2.0, awesome: CC0-1.0).
- Where can I find alternatives to fastembed or awesome?
- GraphCanon lists graph-backed alternatives at fastembed alternatives and awesome alternatives (fastembed markdown twin, awesome markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, fastembed or awesome?
- fastembed: Active. awesome: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for fastembed and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: fastembed trust report; awesome trust report.