Logging · head to head
Airbrake vs MLflow
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Airbrake data retention is 30 days on every plan, including the $799 a month Business tier; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Airbrake covers Error tracking, MLflow covers Experiment tracking.
Where they differ
Only the attributes on which Airbrake 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 Airbrake
- Error tracking
- Performance monitoring
- Deploy tracking
- Custom notifications
- API
- Webhooks
- REST
- Web support
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.
Airbrake
- Error and exception monitoring for web applicationsnot MLflow
- Performance monitoring alongside error trackingnot MLflow
- Alerting a team when a deploy introduces a spike in errorsnot MLflow
- Tracking errors across multiple projects in one accountnot MLflow
MLflow
- Machine learningnot Airbrake
- Data analysisnot Airbrake
- Model trainingnot Airbrake
- Predictive analyticsnot Airbrake
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Airbrake
- Data retention is 30 days on every plan, including the $799 a month Business tier
- The entry plan at $19 a month covers 25,000 errors and 7,500 events
- Errors beyond the plan quota are billed on demand
- Audit logs and spike forgiveness require the Pro tier
- The lowest tier is limited to 1 user and 1 team
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
Airbrake
Free- Tier 1 (Dev + errors)$19/month
- 25,000 errors per month
- 1 user
- 1 team
- Tier 2 (Basic + errors)$38/month
- 100,000 errors per month
- Unlimited users
- 3 teams
- Pro$76/month
- Unlimited users
- Unlimited teams
- Unlimited projects
- Tier 5 (Growth)$299/month
- 1 million errors per month
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Airbrake if
- You need error tracking.
- You want to start without paying.
- You work on Web, Api.
- You also want performance monitoring.
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 Airbrake or MLflow better?
- Neither clearly leads. Airbrake 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, Airbrake or MLflow?
- Airbrake starts at Free and MLflow at Free.
- Does Airbrake or MLflow run on more platforms?
- Airbrake runs on Web, Api. MLflow runs on Web, Python API, REST API.
- Can I use Airbrake for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Airbrake best used for?
- Airbrake is most often used for error and exception monitoring for web applications, performance monitoring alongside error tracking, alerting a team when a deploy introduces a spike in errors, tracking errors across multiple projects in one account. Of those, error and exception monitoring for web applications and performance monitoring alongside error tracking are not what MLflow is typically brought in for.
- What can Airbrake do that MLflow cannot?
- Airbrake covers Error tracking, Performance monitoring, Deploy tracking, Custom notifications. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
Answered from the vendors’ own pages
Airbrake: What is the lowest-cost Airbrake plan and what does it include?
Tier 1 costs $19 per month and includes 25,000 errors per month, 1 user seat, 1 team, and unlimited projects. This plan targets individual developers. A 10% discount applies when paying annually ($17.10 per month).
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.
SourceAirbrake: Which Airbrake plan is marked as the best value?
The Pro plan at $76 per month is marked as Best Value. It includes unlimited users, unlimited teams, unlimited projects, audit logs, and spike forgiveness. Annual billing provides a 10% discount ($68 per month).
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.
SourceAirbrake: How many errors per month does each Airbrake tier allow?
Tier 1 allows 25,000 errors per month at $19/month. Tier 2 allows 100,000 errors at $38/month. Tier 4 allows 300,000 errors at $129/month. Tier 5 allows 1 million errors at $299/month. Tier 6 allows 5 million errors at $799/month.
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
Other head to heads
- Airbrake vs Elastic Stack
- Airbrake vs New Relic
- Airbrake vs Datadog Logs
- Airbrake vs Coralogix
- Airbrake vs Grafana Loki
- Airbrake vs incident.io
- Airbrake vs Cronitor
- Airbrake vs FireHydrant
- Airbrake vs Healthchecks
- Airbrake vs Openstatus
- Airbrake vs Rootly
- Airbrake vs Checkly
- Airbrake vs CloudWatch
- Airbrake vs Dynatrace
- Airbrake vs InfluxDB
- Airbrake vs AppDynamics
- Airbrake vs Axiom
- Airbrake vs Azure Monitor
- Airbrake vs AWS SageMaker
- Airbrake vs Google Vertex AI
- Airbrake vs Azure Machine Learning
- Airbrake vs DataRobot
- Airbrake vs Snowflake
- Airbrake vs TensorFlow
- Airbrake vs Comet ML
- Airbrake vs Jupyter
- Airbrake vs LangChain
- Airbrake vs Pinecone
- Airbrake vs Python
- Airbrake vs PyTorch
- Airbrake vs scikit-learn
- Airbrake vs Apache Spark MLlib
- Airbrake vs Weaviate
- Airbrake vs Weights & Biases
- Airbrake vs Alteryx
- Airbrake vs Anaconda
- MLflow vs Elastic Stack
- MLflow vs New Relic
- MLflow vs Datadog Logs
- MLflow vs Coralogix
- MLflow vs Grafana Loki
- MLflow vs incident.io
- MLflow vs Cronitor
- MLflow vs FireHydrant
- MLflow vs Healthchecks
- MLflow vs Openstatus
- MLflow vs Rootly
- MLflow vs Checkly
- MLflow vs CloudWatch
- MLflow vs Dynatrace
- MLflow vs InfluxDB
- MLflow vs AppDynamics
- MLflow vs Axiom
- MLflow vs Azure Monitor
- MLflow vs AWS SageMaker
- MLflow vs Google Vertex AI
- MLflow vs Azure Machine Learning
- MLflow vs DataRobot
- MLflow vs Snowflake
- MLflow vs TensorFlow
- MLflow vs Comet ML
- MLflow vs Jupyter
- MLflow vs LangChain
- MLflow vs Pinecone
- MLflow vs Python
- 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