Comparison
voy vs awesome-mlops
Verdict
Pick voy when tags unique to voy: webassembly, similarity-search, rust, k-d-tree; pick awesome-mlops when tags unique to awesome-mlops: engineering, data-science, ml, ai.
Markdown twin · voy alternatives · awesome-mlops alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | voy | awesome-mlops |
|---|---|---|
| Maintenance | Dormant (1024d since push) As of today · github_public_v1 | Dormant (597d 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
- voy
- 🕸️🦀 A WASM vector similarity search written in Rust
- awesome-mlops
- A curated list of references for MLOps
Stars
- voy
- 1.1k
- awesome-mlops
- 14k
Forks
- voy
- 39
- awesome-mlops
- 2.1k
Open issues
- voy
- 22
- awesome-mlops
- 42
Language
- voy
- Rust
- awesome-mlops
- -
Adopt for
- voy
- -
- awesome-mlops
- -
Persona
- voy
- -
- awesome-mlops
- -
Runtime
- voy
- -
- awesome-mlops
- -
License
- voy
- Apache-2.0
- awesome-mlops
- -
Last pushed
- voy
- Sep 20, 2023
- awesome-mlops
- Nov 21, 2024
Categories
- voy
- Vector Databases
- awesome-mlops
- Vector Databases, Model Training, Inference & Serving
Trust and health
Days since push
- voy
- 1024d
- awesome-mlops
- 597d
Open issues (now)
- voy
- 22
- awesome-mlops
- 42
Owner type
- voy
- Organization
- awesome-mlops
- User
Full report
- voy
- Trust report
- awesome-mlops
- Trust report
Choose voy if…
- Tags unique to voy: webassembly, similarity-search, rust, k-d-tree.
- Leaner open-issue backlog (22).
When NOT to use voy
- Last GitHub push was 1025 days ago (dormant maintenance, Sep 20, 2023). Validate activity before betting a new project on voy.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: engineering, data-science, ml, ai.
- Also covers Model Training, Inference & Serving.
- More GitHub stars (14k vs 1.1k) - visibility, not fit.
When NOT to use awesome-mlops
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (tantaraio/voy) · observed Jul 11, 2026
- GitHub forks (tantaraio/voy) · observed Jul 11, 2026
- Last push (tantaraio/voy) · observed Sep 20, 2023
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Jul 11, 2026
- GitHub forks (visenger/awesome-mlops) · observed Jul 11, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: voy 1.1k · awesome-mlops 14k (synced Jul 11, 2026).
Common questions
- What is the difference between voy and awesome-mlops?
- voy: 🕸️🦀 A WASM vector similarity search written in Rust. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose voy over awesome-mlops?
- Choose voy over awesome-mlops when Tags unique to voy: webassembly, similarity-search, rust, k-d-tree; Leaner open-issue backlog (22).
- When should I choose awesome-mlops over voy?
- Choose awesome-mlops over voy when Tags unique to awesome-mlops: engineering, data-science, ml, ai; Also covers Model Training, Inference & Serving; More GitHub stars (14k vs 1.1k) - visibility, not fit.
- When should I avoid voy?
- Last GitHub push was 1025 days ago (dormant maintenance, Sep 20, 2023). Validate activity before betting a new project on voy. 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-mlops?
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Is voy or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (13,952 vs 1,063). Stars measure visibility, not whether either tool fits your constraints.
- Are voy and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to voy or awesome-mlops?
- GraphCanon lists graph-backed alternatives at voy alternatives and awesome-mlops alternatives (voy markdown twin, awesome-mlops 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, voy or awesome-mlops?
- voy: Dormant. awesome-mlops: Dormant. 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 voy and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: voy trust report; awesome-mlops trust report.