Registry › Compare › MLflow vs Weights & Biases Weave

MLflow vs Weights & Biases Weave

MLflow leads on trust: 89.6/100 (Grade A) against 84.2/100 (Grade A), a 5.4-point gap. MLflow leads on maintenance and adoption; Weights & Biases Weave leads on transparency.

A MLflow 89.6

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

mlflow/mlflow · #28 overall · #2 Observability & Evaluation · coverage A (4/5)

Choose MLflow if maintenance and adoption matter most.

+5.3Adoption: 4.5M weekly downloads against 175.7k
+0.7Maintenance: last push today, against 1d ago

A Weights & Biases Weave 84.2

Weave is a toolkit for developing AI-powered applications, built by Weights & Biases.

wandb/weave · #104 overall · #5 Observability & Evaluation · coverage A (4/5)

Choose Weights & Biases Weave if transparency matters most.

+0.7Transparency: OSSF Scorecard 6.3 against 5.5

Where they differ

19.4

Safety / IntegrityWeights & Biases Weave +0.3

19.7

13.2

TransparencyWeights & Biases Weave +0.7

13.9

20.0

MaintenanceMLflow +0.7

19.3

19.6

AdoptionMLflow +5.3

14.3

-0.6

Runtime calibrationMLflow +0.4

-1.0

1 dimension identical: Identity 18.0 · Full evidence table

An independent, evidence-based trust comparison of MLflow and Weights & Biases Weave, two Observability & Evaluation projects in the HVTracker registry. Scores come from public, checkable signals — supply-chain provenance, OSSF Scorecard, maintenance, and adoption — not popularity.

Full evidence

Signal	MLflowmlflow/mlflow	Weights & Biases Weavewandb/weave
HVTrust score	89.6	84.2
Evidence grade	A	A
Coverage grade	A	A
Overall rank	#28	#104
Rank in Observability & Evaluation	#2	#5
GitHub stars	28.4k	1.1k
Last updated	today	1d ago
Build provenance	Yes	Yes
OSSF Scorecard	5.5 / 10	6.3 / 10
License	Apache-2.0	Apache-2.0
Downloads	4.5M/wk	176k/wk
Trust dimensions (points earned)
Safety / integrity / 25	19.4	19.7
Identity & provenance / 18	18.0	18.0
Transparency / 17	13.2	13.9
Maintenance / 20	20.0	19.3
Adoption / 20	19.6	14.3
Runtime capability surface (full matrix)
MCP server	Implemented	Implemented
External providers	3 — Amazon Bedrock, Anthropic, Postgres	8 — Amazon Bedrock, Anthropic, Cohere, …
Requires API keys	No	Yes
Plugin surface	plugins	—
Provenance drift	Unknown	Partial

Open in the live compare tool → MLflow profile Weights & Biases Weave profile More Observability & Evaluation →

How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption, scaled by an evidence-confidence factor. Grade bands: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Signals refresh daily. Full methodology v4.4 →