AI · head to head
Arize AI vs MLflow
Arize AI
AI
AI engineering platform for observability and evaluation of agents and LLM apps
- From
- Free
- Rated
- -
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Arize AI the free plan caps trace spans at 25,000 per month with only 15 days of retention.; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Arize AI covers End-to-end tracing, MLflow covers Experiment tracking.
Where they differ
Only the attributes on which Arize AI and MLflow 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 Arize AI
- End-to-end tracing
- Evaluation framework
- Prompt testing and improvement
- Alyx AI engineering agent
- Custom dashboards
- Data warehouse integrations
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
What people use each for
The jobs each tool is most often brought in to do.
Arize AI
- Tracing and debugging production AI agentsnot MLflow
- Running large-scale evaluations across traces and sessionsnot MLflow
- Improving prompts before production rolloutnot MLflow
- Storing and querying GenAI traces alongside a data warehousenot MLflow
MLflow
- Machine learningnot Arize AI
- Data analysisnot Arize AI
- Model trainingnot Arize AI
- Predictive analyticsnot Arize AI
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Arize AI
- The free plan caps trace spans at 25,000 per month with only 15 days of retention.
- Self-hosted deployment and Data Fabric integration are restricted to the custom-priced Enterprise tier.
- Pricing beyond the $50/month Pro plan requires a custom quote, making cost planning less transparent at scale.
- Advanced compliance features like HIPAA are only available on Enterprise.
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
Pricing, plan by plan
Arize AI
Free- AX FreeFree
- 25k trace spans/month
- 1 GB storage/month
- 15-day retention
- AX Pro$50/month
- 50k trace spans/month
- 10 GB storage/month
- 30-day retention
- AX Enterprise$undefined/mo
- Custom trace spans, storage, and retention
- SaaS or self-hosted deployment
- Managed agents and Data Fabric
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Arize AI if
- You need end-to-end tracing.
- You want to start without paying.
- You work on web, api.
- You also want evaluation framework.
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.
Questions people ask
- Is Arize AI or MLflow better?
- Neither clearly leads. Arize AI starts at Free and MLflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Arize AI or MLflow?
- Arize AI starts at Free and MLflow at Free.
- Does Arize AI or MLflow run on more platforms?
- Arize AI runs on web, api. MLflow runs on Web, Python API, REST API.
- Can I use Arize AI for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Arize AI best used for?
- Arize AI is most often used for tracing and debugging production ai agents, running large-scale evaluations across traces and sessions, improving prompts before production rollout, storing and querying genai traces alongside a data warehouse. Of those, tracing and debugging production ai agents and running large-scale evaluations across traces and sessions are not what MLflow is typically brought in for.
- What can Arize AI do that MLflow cannot?
- Arize AI covers End-to-end tracing, Evaluation framework, Prompt testing and improvement, Alyx AI engineering agent. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
Answered from the vendors’ own pages
Arize AI: What does Arize AX cost?
Arize AX offers a free plan, a Pro plan at $50/month, and a custom-priced Enterprise plan, with pricing based on trace spans, storage, and retention needs.
SourceMLflow: 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.
SourceArize AI: Is there a free plan, and what are its limits?
The AX Free plan includes 25,000 trace spans and 1 GB of storage per month with 15-day retention, plus unlimited users, evaluations, and experiments.
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.
SourceArize AI: What deployment options are available?
Free and Pro plans are SaaS-only, while Enterprise customers can choose SaaS or self-hosted deployment with custom SLAs.
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.
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.
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.
SourceRelated pages
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- MLflow vs PyTorch
- MLflow vs scikit-learn
- MLflow vs Apache Spark MLlib
- MLflow vs Weaviate
- MLflow vs Weights & Biases
- MLflow vs Alteryx
- MLflow vs Anaconda