The AI developer platform
Weights & Biases (W&B) is the leading MLOps platform for experiment tracking, model versioning, and collaboration. Track experiments in real-time, visualize model performance, and share results with your team. Used by OpenAI, NVIDIA, and thousands of ML teams.
Best for: ML teams wanting best-in-class experiment tracking and collaboration
Key Features
- ✓Real-time experiment tracking
- ✓Interactive dashboards
- ✓Model registry & versioning
- ✓Hyperparameter sweeps
- ✓Collaborative reports
Integrations
PyTorchTensorFlowKerasHugging Facescikit-learnLangChain
Pricing: Free for individuals, Team from $50/user/month
Open source platform for the ML lifecycle
MLflow is the most popular open-source platform for managing the end-to-end machine learning lifecycle. Created by Databricks, it handles experiment tracking, model packaging, deployment, and registry. Self-host or use managed offerings.
Best for: Teams wanting open-source flexibility with strong community support
Key Features
- ✓Experiment tracking
- ✓Model packaging (MLflow Models)
- ✓Model registry
- ✓Project reproducibility
- ✓Multi-framework support
Integrations
DatabricksAWS SageMakerAzure MLSparkPyTorchTensorFlow
Pricing: Free (open-source), managed options available
Build, train, and deploy ML models at scale
Amazon SageMaker is a fully managed service for building, training, and deploying machine learning models. Includes SageMaker Studio IDE, built-in algorithms, AutoML, and one-click deployment. The most comprehensive cloud ML platform.
Best for: AWS-native teams needing end-to-end managed ML infrastructure
Key Features
- ✓SageMaker Studio IDE
- ✓Built-in algorithms & AutoML
- ✓Distributed training
- ✓One-click deployment
- ✓Ground Truth labeling
Integrations
S3ECRLambdaStep FunctionsCloudWatchIAM
Pricing: Pay-as-you-go, varies by instance type
Unified ML platform for building and deploying AI
Vertex AI is Google Cloud's unified platform for building, deploying, and scaling ML models. Combines AutoML and custom training with managed notebooks, feature store, model monitoring, and integration with BigQuery and Google's AI services.
Best for: Google Cloud users wanting integrated ML with BigQuery and Google AI
Key Features
- ✓AutoML & custom training
- ✓Managed notebooks (Workbench)
- ✓Feature Store
- ✓Model Registry & Endpoints
- ✓Vertex AI Pipelines
Integrations
BigQueryCloud StorageDataflowTensorFlowPyTorchKubeflow
Pricing: Pay-as-you-go, varies by service
Experiment tracking and model registry for ML teams
Neptune is a metadata store for MLOps, built for research and production teams that run many experiments. Lightweight, flexible logging with powerful querying and comparison. Handles millions of runs without slowing down.
Best for: Research teams running large-scale experiments needing flexible tracking
Key Features
- ✓Flexible metadata logging
- ✓Powerful experiment comparison
- ✓Model registry
- ✓Team collaboration
- ✓Scales to millions of runs
Integrations
PyTorchTensorFlowKerasXGBoostOptunaSacred
Pricing: Free tier available, Team from $49/month
Build production-ready AI applications
BentoML is the unified framework for building, shipping, and scaling AI applications. Package models from any framework, create prediction services, and deploy anywhere. Open-source with a managed cloud platform (BentoCloud).
Best for: Teams wanting flexible, production-ready model serving with any framework
Key Features
- ✓Framework-agnostic model serving
- ✓Adaptive batching
- ✓GPU inference optimization
- ✓Containerized deployment
- ✓REST & gRPC APIs
Integrations
PyTorchTensorFlowHugging FaceONNXKubernetesDocker
Pricing: Free (open-source), BentoCloud from $0.05/hour
Serverless infrastructure for AI/ML
Modal is a serverless platform purpose-built for AI/ML workloads. Run any Python code on cloud infrastructure with instant cold starts, automatic scaling, and GPU access. No Docker or Kubernetes knowledge required.
Best for: Teams wanting serverless GPU compute without infrastructure complexity
Key Features
- ✓Instant cold starts
- ✓GPU access (A100, H100)
- ✓Automatic scaling
- ✓No Docker required
- ✓Python-native interface
Integrations
Hugging FacePyTorchFastAPIvLLMJupyterGitHub Actions
Pricing: Pay-per-use, from $0.000016/GB-second
Enterprise feature platform for ML
Tecton is the enterprise feature platform for operational ML. Define, compute, and serve features in real-time and batch. Ensures consistency between training and serving, with built-in monitoring and governance.
Best for: Enterprise teams building real-time ML applications needing feature consistency
Key Features
- ✓Real-time & batch features
- ✓Feature versioning
- ✓Training-serving consistency
- ✓Feature monitoring
- ✓Enterprise governance
Integrations
SnowflakeDatabricksSparkKafkaAWSGCP
Pricing: Contact for pricing
Open-source feature store for ML
Feast is the leading open-source feature store for machine learning. Define features once, serve them consistently for training and inference. Self-managed or use managed offerings from Tecton or cloud providers.
Best for: Teams wanting open-source feature management with flexibility to self-host
Key Features
- ✓Feature definition & registry
- ✓Online & offline serving
- ✓Point-in-time joins
- ✓Multiple data sources
- ✓Python SDK
Integrations
SnowflakeBigQueryRedshiftSparkRedisPostgreSQL
Pricing: Free (open-source)
ML observability for production models
Arize is the leading ML observability platform for monitoring, troubleshooting, and explaining production models. Detect drift, debug performance issues, and understand model behavior with automatic insights and root cause analysis.
Best for: Teams needing production monitoring with automatic issue detection
Key Features
- ✓Model performance monitoring
- ✓Drift detection
- ✓Explainability & fairness
- ✓Root cause analysis
- ✓LLM observability
Integrations
SageMakerVertex AIDatabricksMLflowLangChainOpenAI
Pricing: Free tier available, Pro from $500/month