Flyte is the better choice for ML engineering teams on Kubernetes that need type-safe, cached, multi-tenant orchestration with complete data lineage. Metaflow is the better choice for data science teams on AWS that need the fastest path from experiment to production without managing Kubernetes infrastructure.
| Feature | Flyte | Metaflow |
|---|---|---|
| Best For | ML engineering teams needing type-safe, cached, multi-tenant K8s-native orchestration | Data science teams shipping ML models to production without managing Kubernetes |
| Architecture | Kubernetes-native with control plane, data plane, strongly-typed task containers | Python framework with AWS Step Functions backend; no K8s required |
| Pricing Model | Flyte is fully open-source and free (Apache 2.0, 80M+ downloads). Commercial managed offering via Union.ai: Team plan $950/month (includes $950 usage credit) with GPU rates from T4g $0.15/hr to H200 $1.58/hr and B200 $2.85/hr. CPU $0.0417/vCPU/hr, memory $0.0051/GB/hr. Enterprise plan: custom pricing with volume discounts, multi-cluster, 1-year data retention, dedicated support. Team plan supports up to 1,000 concurrent actions, 30-day retention. | GitHub license: Apache-2.0 (tool can be self-hosted for free) |
| Ease of Use | Steeper learning curve requiring K8s knowledge; powerful once configured | Minimal learning curve; decorator-based Python API; hours to first production workflow |
| Infrastructure Requirements | Requires Kubernetes cluster; Union.ai managed eliminates K8s ops burden | No Kubernetes needed; AWS Step Functions + Batch for serverless execution |
| Community/Ecosystem | CNCF incubating, 80M+ container downloads, Spotify/Toyota adoption | Netflix-developed, active GitHub community, strong AWS ecosystem integration |
| Feature | Flyte | Metaflow |
|---|---|---|
| Core Orchestration | ||
| Architecture | Kubernetes-native with control plane, data plane, and admin server | Python framework with pluggable compute backends (AWS, K8s) |
| SDK Language Support | Python (primary), Java, Scala | Python only |
| Type System | Strongly-typed with Flytekit annotations for all inputs/outputs | Dynamic Python types with automatic pickling |
| Workflow Authoring | @task and @workflow decorators with explicit type signatures | @step decorators on Python class methods with minimal boilerplate |
| Production Backend | Kubernetes (required for all execution) | AWS Step Functions (primary), Argo Workflows (K8s alternative) |
| Data & Caching | ||
| Caching | Content-addressable, type-aware automatic caching across executions | Decorator-based caching with @cache step-level control |
| Data Versioning | Automatic versioning of all task inputs/outputs in Flyte blob store | Automatic artifact versioning via S3-backed Metaflow datastore |
| Container Isolation | Every task runs in its own container with declared image | @conda/@pypi decorators for per-step dependency isolation |
| Operations & Scale | ||
| Multi-Tenancy | Built-in project and domain isolation for shared installations | No built-in multi-tenancy; separate deployments per team |
| GPU Support | Native K8s GPU scheduling; Union.ai rates from T4g $0.15/hr to H200 $1.58/hr | @resources decorator for GPU allocation via underlying compute layer |
| Scheduling | Built-in LaunchPlans with cron scheduling and parameterization | Event-triggered via AWS Step Functions or Argo cron workflows |
| Web UI | Flyte Console with execution graphs, task logs, and data preview | Optional Metaflow UI for run inspection; AWS Console for Step Functions |
| Pricing & Deployment | ||
| Self-Hosted Cost | Free but requires Kubernetes cluster (EKS ~$73/mo control plane + nodes) | Free with no Kubernetes required; pay only for AWS compute |
| Managed Service | Union.ai Team $950/mo with $950 usage credit; Enterprise custom pricing | No managed service available; self-hosted only |
Architecture
SDK Language Support
Type System
Workflow Authoring
Production Backend
Caching
Data Versioning
Container Isolation
Multi-Tenancy
GPU Support
Scheduling
Web UI
Self-Hosted Cost
Managed Service
Flyte is the better choice for ML engineering teams on Kubernetes that need type-safe, cached, multi-tenant orchestration with complete data lineage. Metaflow is the better choice for data science teams on AWS that need the fastest path from experiment to production without managing Kubernetes infrastructure.
Choose Flyte if:
Choose Flyte if you have ML platform engineers, operate Kubernetes, and need type safety, intelligent caching, multi-tenancy, or multi-language SDK support for production ML systems
Choose Metaflow if:
Choose Metaflow if your team is primarily data scientists on AWS who need to ship ML models to production quickly without managing Kubernetes infrastructure or learning a new type system
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Yes. Flyte is architecturally dependent on Kubernetes for task execution, scheduling, and resource management. The control plane runs as K8s deployments and every task executes as a pod. Union.ai's managed service handles the Kubernetes complexity, but the underlying infrastructure is still Kubernetes.
Yes. While Metaflow's primary production backend is AWS Step Functions, it also supports Argo Workflows as a Kubernetes-based execution backend. This gives teams the option to use K8s while maintaining the same Python API.
Both provide built-in experiment tracking. Flyte tracks all typed inputs/outputs with lineage via Flyte Console. Metaflow versions all Python variables and artifacts via its Client API. Neither replaces dedicated tools like MLflow or W&B for metric visualization.
Flyte handles GPU scheduling natively through Kubernetes GPU device plugins. Union.ai offers GPU rates from T4g at $0.15/hr to H200 at $1.58/hr. Metaflow uses @resources decorators to request GPUs from the underlying compute layer (AWS Batch or Kubernetes).