Flyte is the superior choice for teams needing Kubernetes-native workflow orchestration with type-safe tasks, caching, and GPU scheduling. MLflow is the superior choice for teams focused on experiment tracking, model versioning, and lightweight deployment. For the most complete ML platform, use both tools together — Flyte for orchestration, MLflow for experiment and model management.
| Feature | Flyte | MLflow |
|---|---|---|
| Best For | Platform teams building production ML pipelines with caching, GPU scheduling, and Kubernetes orchestration | Data science teams needing experiment tracking, model versioning, and lightweight deployment |
| Primary Function | Kubernetes-native workflow orchestration with type-safe tasks and dynamic workflows | Experiment tracking, model registry, and model deployment across ML frameworks |
| 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. | Open-source license (Apache-2.0), self-hosted for free |
| Infrastructure Requirement | Requires Kubernetes cluster for production; single-node K8s for development | No Kubernetes required; runs on any Python environment including laptops |
| Ease of Setup | Significant setup effort requiring Kubernetes expertise and cluster management | Minimal setup; add two lines of Python to start tracking experiments |
| Community/Ecosystem | 80M+ downloads, growing K8s/ML community, Union.ai backing | Largest open-source ML platform, massive community, Databricks backing |
| Feature | Flyte | MLflow |
|---|---|---|
| Core Capabilities | ||
| Primary Function | Workflow orchestration with ML-aware task scheduling and dependency management | Experiment tracking and model lifecycle management across ML frameworks |
| Pipeline Authoring | Python tasks with strong type annotations, decorators, and automatic serialization | Standard Python scripts with lightweight logging API calls |
| Task Caching | Built-in content-addressed caching keyed by input hash — skips redundant computation | No native task caching; requires manual checkpoint management |
| Type Safety | Strong typing with automatic serialization, validation at registration time | Loosely typed parameter logging; no enforced type system |
| Dynamic Workflows | Data-dependent sub-workflow spawning at runtime for adaptive pipelines | Not applicable — MLflow does not orchestrate workflows |
| ML-Specific Features | ||
| Experiment Tracking | Via MLflow or third-party integrations within Flyte tasks | Core feature with auto-logging support for 20+ ML frameworks |
| Model Registry | Requires external registry such as MLflow or Weights & Biases | Built-in model versioning with staging, production, and archived states |
| GPU Scheduling | Native Kubernetes GPU requests per task — T4, A100, H100, B200 support | No GPU scheduling; relies on external infrastructure for compute |
| Model Deployment | Kubernetes pod deployment with resource isolation and scaling | REST API serving, Docker containers, SageMaker, Azure ML, Spark UDF |
| Operations & Infrastructure | ||
| Kubernetes Requirement | Required for production; control plane and tasks run as K8s services and pods | Not required; runs on any Python environment including single VMs and laptops |
| Multi-Tenancy | Built-in project and domain isolation on shared Kubernetes clusters | Basic access controls; no native multi-tenant isolation |
| LLM Support | GPU orchestration for LLM fine-tuning and multi-GPU inference pipelines | Native LangChain, OpenAI, and Hugging Face integrations for LLM tracking |
| License | Apache 2.0 — fully free with no restrictions; 80M+ downloads | Apache 2.0 — fully free; largest open-source ML platform |
Primary Function
Pipeline Authoring
Task Caching
Type Safety
Dynamic Workflows
Experiment Tracking
Model Registry
GPU Scheduling
Model Deployment
Kubernetes Requirement
Multi-Tenancy
LLM Support
License
Flyte is the superior choice for teams needing Kubernetes-native workflow orchestration with type-safe tasks, caching, and GPU scheduling. MLflow is the superior choice for teams focused on experiment tracking, model versioning, and lightweight deployment. For the most complete ML platform, use both tools together — Flyte for orchestration, MLflow for experiment and model management.
Choose Flyte if:
Choose Flyte for production ML pipeline orchestration requiring type-safe tasks, content-addressed caching, dynamic workflows, and native Kubernetes GPU scheduling across distributed clusters
Choose MLflow if:
Choose MLflow for experiment tracking, model comparison, model registry, and lightweight deployment when you need zero-infrastructure setup and framework-agnostic logging across PyTorch, TensorFlow, scikit-learn, and 20+ other libraries
Choose both if:
Use both together for the most complete ML platform — Flyte orchestrates pipelines while MLflow tracks experiments and manages models within Flyte tasks
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Yes, and this is a recommended pattern for production ML platforms. Flyte handles workflow orchestration — scheduling tasks, managing dependencies, caching intermediate results, and allocating GPU resources — while MLflow handles experiment tracking and model management within individual Flyte tasks.
Yes, Flyte requires a Kubernetes cluster for production deployment. The Flyte control plane runs as Kubernetes services, and task execution happens in Kubernetes pods. Union.ai's managed service eliminates this requirement by handling the Kubernetes infrastructure.
No. MLflow is a fully independent open-source project that runs anywhere Python runs. While Databricks created and sponsors MLflow, the open-source version has no dependency on Databricks.
Both tools have added LLM support. MLflow has native integrations with LangChain, OpenAI, and Hugging Face Transformers for tracking LLM experiments. Flyte supports LLM fine-tuning and inference pipelines through GPU scheduling and workflow orchestration. For LLM evaluation, MLflow is stronger; for LLM training pipelines, Flyte is better.