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Comparison

evalml vs mlflow

Verdict

Pick evalml if evalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license; pick mlflow if mLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use,.

Markdown twin · evalml alternatives · mlflow alternatives

GraphCanon updated 3d

evalml logo

evalml

alteryx/evalml

★ 852pushed Jan 14, 2026
vs
mlflow logo

mlflow

mlflow/mlflow

★ 28kpushed Aug 20, 2026

Trust & integrity

Signalevalmlmlflow
Maintenance
Slowing (201d since push)
As of 2w · github_public_v1
Very active (0d since push)
As of 3d · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 3d · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
evalml trust reportmlflow trust reportTrust methodology

Tagline

evalml
An AutoML library written in Python
mlflow
AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications

Stars

evalml
852
mlflow
28k

Forks

evalml
93
mlflow
6.2k

Open issues

evalml
324
mlflow
2.1k

Language

evalml
Python
mlflow
Python

Adopt for

evalml
EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license.
mlflow
MLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use,

Persona

evalml
-
mlflow
-

Runtime

evalml
-
mlflow
-

License

evalml
EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices.
mlflow
Apache-2.0

Last pushed

evalml
Jan 14, 2026
mlflow
Aug 20, 2026

Categories

evalml
Evaluation & Observability, Model Training
mlflow
Evaluation & Observability, Inference & Serving, Model Training

Trust and health

Maintenance

evalml
Slowing (36%)
mlflow
Very active (96%)

Days since push

evalml
201d
mlflow
0d

Open issues (now)

evalml
324
mlflow
2.1k

Stars delta

evalml
Unknown
mlflow
+476 (30d)

Open issues delta

evalml
Unknown
mlflow
-22 (30d)

Full report

evalml
Trust report
mlflow
Trust report

Choose evalml if…

  • License: evalml is BSD-3-Clause, mlflow is Apache-2.0.
  • Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased..
  • Requirements: Min 2 GB RAM.
  • Tags unique to evalml: automl, data-science, feature-engineering, feature-selection.
  • You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.

When NOT to use evalml

  • You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box.
  • Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.
GitHubDetails

Choose mlflow if…

  • License: mlflow is Apache-2.0, evalml is BSD-3-Clause.
  • Tags unique to mlflow: agentops, agents, ai-governance, evaluation.
  • Also covers Inference & Serving.
  • - Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**.

When NOT to use mlflow

  • - Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain.
  • - Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments.
GitHubDetails

Explore

evalml trust report →mlflow trust report →Evaluation & Observability category →Model Training category →Inference & Serving category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: evalml 852 · mlflow 28k (synced Aug 4, 2026).

Common questions

What is the difference between evalml and mlflow?
evalml: An AutoML library written in Python. mlflow: AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications. See the comparison table for live GitHub stats and shared categories.
When should I choose evalml over mlflow?
Choose evalml over mlflow when License: evalml is BSD-3-Clause, mlflow is Apache-2.0; Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.; Requirements: Min 2 GB RAM; Tags unique to evalml: automl, data-science, feature-engineering, feature-selection; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.
When should I choose mlflow over evalml?
Choose mlflow over evalml when License: mlflow is Apache-2.0, evalml is BSD-3-Clause; Tags unique to mlflow: agentops, agents, ai-governance, evaluation; Also covers Inference & Serving; - Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**.
When should I avoid evalml?
You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.
When should I avoid mlflow?
- Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain. - Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments.
Is evalml or mlflow more popular on GitHub?
mlflow has more GitHub stars (27,591 vs 852). Stars measure visibility, not whether either tool fits your constraints.
Are evalml and mlflow open source?
Yes - both are open-source projects on GitHub (evalml: BSD-3-Clause, mlflow: Apache-2.0).
Where can I find alternatives to evalml or mlflow?
GraphCanon lists graph-backed alternatives at evalml alternatives and mlflow alternatives (evalml markdown twin, mlflow 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, evalml or mlflow?
evalml: Slowing. mlflow: Very 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 evalml and mlflow?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: evalml trust report; mlflow trust report.

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