MLflow vs New Relic: Key Differences & When to Use Each

Comprehensive side-by-side comparison of features, pricing, and metrics

Key Differences

Compare MLflow and New Relic across features, pricing, integrations, and community metrics. MLflow / New Relic.

Feature

MLflow

Machine Learning

New Relic

Monitoring

Side-by-side comparison of developer tools
Platform for the machine learning lifecycle
New Relic APM agent for Ruby applications
GitHub Stars
⭐ 27,734
⭐ 1,210
Contributors
👥 1,161
👥 306
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Python
Ruby
Features
  • • Agentops
  • • Agents
  • • Ai
  • • Ai Governance
  • • Apache Spark
  • • Agent
  • • Apm Agent
  • • Hacktoberfest
  • • Ruby
Integrations
  • • prometheus
No integrations listed
Momentum Score
95/100 (slowing)
26/100 (stable)
Community Health
88/100 (excellent)
20/100 (needs-attention)
Maturity Index
66/100 (growing)
15/100 (experimental)
Innovation Score
75/100 (innovative)
16/100 (traditional)
Risk Score (higher is safer)
67/100 (low)
29/100 (high)
Developer Experience
53/100 (needs-improvement)
19/100 (poor)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

MLflow Strengths

  • ✓ More popular (27,734 stars)
  • ✓ Larger community (1,161 contributors)
  • ✓ More features (5 listed)

New Relic Strengths

When to Use MLflow vs New Relic

Use MLflow when its strengths align better with your stack and team needs, and choose New Relic when its ecosystem, integrations, or cost profile is a better fit.

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Data source: GitHub API

Last updated: 8/30/2026