Home/Compare/fastembed-rs vs awesome

Comparison

fastembed-rs vs awesome

Verdict

Pick fastembed-rs when license: fastembed-rs is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, fastembed-rs is Apache-2.0.

Markdown twin · fastembed-rs alternatives · awesome alternatives

GraphCanon updated today

fastembed-rs logo

fastembed-rs

Anush008/fastembed-rs

958pushed Jun 30, 2026
vs
awesome logo

awesome

sindresorhus/awesome

484kpushed Jun 30, 2026

Trust & integrity

Signalfastembed-rsawesome
Maintenance
Active (10d since push)
As of today · github_public_v1
Active (11d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal 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-rs
Rust library for generating vector embeddings, reranking locally!
awesome
😎 Curated list of awesome topics including hardware resources

Stars

fastembed-rs
958
awesome
484k

Forks

fastembed-rs
133
awesome
36k

Open issues

fastembed-rs
2
awesome
92

Language

fastembed-rs
Rust
awesome
-

Adopt for

fastembed-rs
-
awesome
-

Persona

fastembed-rs
-
awesome
-

Runtime

fastembed-rs
-
awesome
-

License

fastembed-rs
Apache-2.0
awesome
CC0-1.0

Last pushed

fastembed-rs
Jun 30, 2026
awesome
Jun 30, 2026

Categories

fastembed-rs
Vector Databases, Data & Retrieval, LLM Frameworks
awesome
LLM Frameworks

Trust and health

Days since push

fastembed-rs
10d
awesome
11d

Open issues (now)

fastembed-rs
2
awesome
92

Full report

fastembed-rs
Trust report

Choose fastembed-rs if…

  • License: fastembed-rs is Apache-2.0, awesome is CC0-1.0.
  • Tags unique to fastembed-rs: embeddings, fastembed, reranker, rag.
  • Also covers Vector Databases, Data & Retrieval.

When NOT to use fastembed-rs

  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
  • Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

Choose awesome if…

  • License: awesome is CC0-1.0, fastembed-rs is Apache-2.0.
  • Tags unique to awesome: resources, awesome-list.
  • More GitHub stars (484k vs 958) - 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 on cards: fastembed-rs 958 · awesome 484k (synced Jul 11, 2026).

Common questions

What is the difference between fastembed-rs and awesome?
fastembed-rs: Rust library for generating vector embeddings, reranking locally!. 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-rs over awesome?
Choose fastembed-rs over awesome when License: fastembed-rs is Apache-2.0, awesome is CC0-1.0; Tags unique to fastembed-rs: embeddings, fastembed, reranker, rag; Also covers Vector Databases, Data & Retrieval.
When should I choose awesome over fastembed-rs?
Choose awesome over fastembed-rs when License: awesome is CC0-1.0, fastembed-rs is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 958) - visibility, not fit.
When should I avoid fastembed-rs?
Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
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-rs or awesome more popular on GitHub?
awesome has more GitHub stars (484,026 vs 958). Stars measure visibility, not whether either tool fits your constraints.
Are fastembed-rs and awesome open source?
Yes - both are open-source projects on GitHub (fastembed-rs: Apache-2.0, awesome: CC0-1.0).
Where can I find alternatives to fastembed-rs or awesome?
GraphCanon lists graph-backed alternatives at fastembed-rs alternatives and awesome alternatives (fastembed-rs 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-rs or awesome?
fastembed-rs: 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-rs and awesome?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: fastembed-rs trust report; awesome trust report.