Machine Learning · head to head
MLflow vs New Relic
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves; New Relic data ingest costs can be high for large-scale deployments with high logging volume, making budgeting difficult
- They diverge on capability: MLflow covers Experiment tracking, New Relic covers APM.
Where they differ
Only the attributes on which MLflow and New Relic actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
Only in New Relic
- APM
- Infrastructure Monitoring
- Log Management
- Browser Monitoring
- Synthetic Monitoring
- Mobile Monitoring
- Kubernetes Monitoring
- AI Ops
Both cover
- Kubernetes
What people use each for
The jobs each tool is most often brought in to do.
MLflow
- Machine learningnot New Relic
- Data analysisnot New Relic
- Model trainingnot New Relic
- Predictive analyticsnot New Relic
New Relic
- Application monitoringnot MLflow
- Infrastructure monitoringnot MLflow
- Error trackingnot MLflow
- Performance optimizationnot MLflow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
MLflow
- Requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- Basic UI and visualization: lacks rich interactive dashboards and real-time monitoring compared to commercial platforms
- Limited collaboration: no built-in role-based access control or multi-user management features
- Production monitoring gaps: drift detection, explainability, and alerting require separate dedicated tools
New Relic
- Data ingest costs can be high for large-scale deployments with high logging volume, making budgeting difficult
- Core user licensing model adds complexity to pricing with distinction between full platform users and basic users
- Default logs obfuscation may miss some sensitive patterns requiring custom configuration
- Retention limits even on paid tiers require additional storage for long-term compliance requirements
Pricing, plan by plan
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
New Relic
FreeNo published plan breakdown. See the New Relic review.
Which should you pick?
Choose MLflow if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python API, REST API.
- You also want model registry.
Choose New Relic if
- You need apm.
- You want to start without paying.
- You work on Web, Api, Mobile.
- You also want infrastructure monitoring.
Questions people ask
- Is MLflow or New Relic better?
- Neither clearly leads. MLflow starts at Free and New Relic at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, MLflow or New Relic?
- MLflow starts at Free and New Relic at Free.
- Does MLflow or New Relic run on more platforms?
- MLflow runs on Web, Python API, REST API. New Relic runs on Web, Api, Mobile.
- Can I use MLflow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is MLflow best used for?
- MLflow is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what New Relic is typically brought in for.
- What can MLflow do that New Relic cannot?
- MLflow covers Experiment tracking, Model registry, Model packaging, Deployment. New Relic covers APM, Infrastructure Monitoring, Log Management, Browser Monitoring. Both handle Kubernetes.
Answered from the vendors’ own pages
MLflow: Is MLflow free to use?
Yes, MLflow is completely open-source and free. However, teams typically incur infrastructure costs for hosting and maintaining the MLflow tracking server. Databricks offers Managed MLflow as a commercial option for cloud deployment.
SourceNew Relic: Does New Relic offer a free tier?
Yes, New Relic's free tier is perpetual with no credit card required. It includes 100 GB of free data ingest monthly, one Full Platform User with access to all 50+ capabilities, and unlimited Basic Users for querying and dashboard creation.
SourceMLflow: Can MLflow track experiments for different ML frameworks?
Yes, MLflow is framework-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, and any other ML framework. This flexibility is a core design principle allowing teams to use diverse tools.
SourceNew Relic: How much does New Relic cost for paid plans?
Paid plans start at $49 per month per core user. New Relic uses consumption-based pricing where you pay only for what you use. Annual commitment options are available with volume discounts for larger teams.
SourceMLflow: Does MLflow include a model registry?
Yes, MLflow Model Registry (added in 2018) provides a central model store with versioning, stage transitions, and deployment tracking. This enables production model governance and lineage tracking.
SourceNew Relic: What data retention is included in New Relic's free tier?
The free tier includes a minimum of 8 days data retention for troubleshooting. Paid plans offer extended retention periods and customizable data retention policies.
SourceMLflow: What are MLflow's main limitations?
MLflow requires significant infrastructure setup and maintenance. The UI is basic compared to commercial tools, collaboration is limited without third-party RBAC solutions, and production monitoring requires separate tools for drift detection and alerting.
SourceNew Relic: How many integrations does New Relic support?
New Relic provides access to 780+ integrations and unlimited hosts at no additional cost. These include monitoring integrations for various cloud services, databases, and applications.
SourceMLflow: Can MLflow handle LLM and agent tracing?
MLflow added LLM and agent tracing capabilities in recent versions, though the native support is limited compared to specialized LLM observability platforms that replaced weak LLM tracing.
SourceNew Relic: Can I use New Relic to monitor multiple cloud providers?
Yes, New Relic is cloud-agnostic and supports monitoring across AWS, Google Cloud, Azure, and on-premises infrastructure in a single platform.
SourceNew Relic: What is New Relic's ownership structure today?
New Relic was acquired by TPG and Francisco Partners on July 31, 2023, for $6.5 billion and transitioned from a publicly traded company to a private company in November 2023.
SourceRelated pages
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