Home/Compare/vault-ai vs awesome-mlops

Comparison

vault-ai vs awesome-mlops

Verdict

Pick vault-ai when tags unique to vault-ai: go, generative, artificial-intelligence, chatgpt; pick awesome-mlops when tags unique to awesome-mlops: engineering, data-science, ml, machine-learning.

Markdown twin · vault-ai alternatives · awesome-mlops alternatives

GraphCanon updated today

vault-ai logo

vault-ai

pashpashpash/vault-ai

3.4kpushed Jul 8, 2025
vs
awesome-mlops logo

awesome-mlops

visenger/awesome-mlops

14kpushed Nov 21, 2024

Trust & integrity

Signalvault-aiawesome-mlops
Maintenance
Dormant (367d since push)
As of today · github_public_v1
Dormant (597d 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

vault-ai
OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
awesome-mlops
A curated list of references for MLOps

Stars

vault-ai
3.4k
awesome-mlops
14k

Forks

vault-ai
297
awesome-mlops
2.1k

Open issues

vault-ai
50
awesome-mlops
42

Language

vault-ai
JavaScript
awesome-mlops
-

Adopt for

vault-ai
-
awesome-mlops
-

Persona

vault-ai
-
awesome-mlops
-

Runtime

vault-ai
-
awesome-mlops
-

License

vault-ai
MIT
awesome-mlops
-

Last pushed

vault-ai
Jul 8, 2025
awesome-mlops
Nov 21, 2024

Categories

vault-ai
Vector Databases
awesome-mlops
Model Training, Vector Databases, Inference & Serving

Trust and health

Days since push

vault-ai
367d
awesome-mlops
597d

Open issues (now)

vault-ai
50
awesome-mlops
42

Full report

vault-ai
Trust report
awesome-mlops
Trust report

Choose vault-ai if…

  • Tags unique to vault-ai: go, generative, artificial-intelligence, chatgpt.
  • More recently updated (last pushed Jul 8, 2025).

When NOT to use vault-ai

  • Last GitHub push was 368 days ago (dormant maintenance, Jul 8, 2025). Validate activity before betting a new project on vault-ai.
  • 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, machine-learning.
  • Also covers Model Training, Inference & Serving.
  • More GitHub stars (14k vs 3.4k) - 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.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
  • 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 on cards: vault-ai 3.4k · awesome-mlops 14k (synced Jul 11, 2026).

Common questions

What is the difference between vault-ai and awesome-mlops?
vault-ai: OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
When should I choose vault-ai over awesome-mlops?
Choose vault-ai over awesome-mlops when Tags unique to vault-ai: go, generative, artificial-intelligence, chatgpt; More recently updated (last pushed Jul 8, 2025).
When should I choose awesome-mlops over vault-ai?
Choose awesome-mlops over vault-ai when Tags unique to awesome-mlops: engineering, data-science, ml, machine-learning; Also covers Model Training, Inference & Serving; More GitHub stars (14k vs 3.4k) - visibility, not fit.
When should I avoid vault-ai?
Last GitHub push was 368 days ago (dormant maintenance, Jul 8, 2025). Validate activity before betting a new project on vault-ai. 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. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Is vault-ai or awesome-mlops more popular on GitHub?
awesome-mlops has more GitHub stars (13,952 vs 3,388). Stars measure visibility, not whether either tool fits your constraints.
Are vault-ai and awesome-mlops open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to vault-ai or awesome-mlops?
GraphCanon lists graph-backed alternatives at vault-ai alternatives and awesome-mlops alternatives (vault-ai 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, vault-ai or awesome-mlops?
vault-ai: 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 vault-ai and awesome-mlops?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: vault-ai trust report; awesome-mlops trust report.