Simplicity at its core — both in development and deployment

Kubeflow entails a significant learning curve because of its proximity to the infrastructure layer. If you’re looking to enhance user experience, simplify data and ML workflow development, and streamline infrastructure navigation, you’ve come to the right place. Thanks to an extensive feature set and infrastructure abstraction, Flyte can help you take your workflows to the next level.

Flyte vs. Kubeflow

Tap Kubernetes’ power, not its complexity

Kubeflow is built as a thin layer on top of Kubernetes, and it offers limited management of Kubernetes configuration. Authoring workflows can be a challenge for data scientists and engineers who lack Kubernetes expertise. Flyte provides a clean abstraction on top of Kubernetes that can be removed for complicated use cases; however, it works for 80% of them.

No weird DSL to author workflows

Kubeflow’s DSL deviates from Python and requires users to learn its syntax specifics, such as declaring output type annotations in the input arguments declaration, while Flyte’s SDK aligns with Python.

Mitigate boilerplate code

Kubeflow enforces a type system, but it doesn’t support data types beyond fundamental Python types and artifacts/files. By contrast, Flyte supports varied data types and transformations, and it automates the interaction among different cloud providers and the local file system. The intuitive code constructs in Flyte make it simpler to develop ML workflows, and eliminates the boilerplate you would otherwise have.

Modern AI orchestration

Many organizations have found that the requirements for AI pipelines are significantly more complex than traditional ETL pipelines. Modern AI orchestration meets the demands of today’s workloads, which make dynamic use of heterogenous and resource-intensive infrastructures. With efficiency, scalability, ease of use and agility, modern AI orchestration empowers organizations to optimize and automate pipeline management.

“We’ve migrated about 50% of all training pipelines over to Flyte from Kubeflow. In several cases, we saw an 80% reduction in boilerplate between workflows and tasks vs. the Kubeflow pipeline and components. Overall, Flyte is a far simpler system to reason about with respect to how the code actually executes, and it’s more self-serve for our research team to handle.”

— Rahul Mehta, ML Infrastructure/Platform Lead at Theorem LP

Contrasting Kubeflow and Flyte for modern orchestration

Kubeflow Pipelines v2

Get real-time updates and alerts sent to Slack, Pager Duty or email to stay informed when workflows succeed or fail.


Rerun only failed tasks in a workflow to save time, resources, and more easily debug.


Enable human intervention to supervise, tune and test workflows - resulting in improved accuracy and safety.

Intra-task checkpointing

Checkpoint progress within a task execution in order to save time and resources in the event of task failure.

Dynamism in DAGs

Create flexible and adaptable workflows that can change and evolve as needed, making it easier to respond to changing requirements.

Type checking

Strongly typed inputs and outputs can simplify data validation and highlight incompatibilities between tasks making it easier to identify and troubleshoot errors before launching the workflow.

Flyte vs. Kubeflow:
What’s right for me?

Both Flyte and Kubeflow are platforms designed for orchestrating ML workflows and infrastructure within Kubernetes. Both platforms vary in the degree of scalability under intense workload and they leverage Kubernetes in different ways. In consequence Flyte and Kubeflow offer distinct developer experiences.

Kubeflow primarily focuses on ML pipelines, while Flyte is a versatile platform suitable for various use cases, including data and ML pipelines.

Let’s take a closer look at how Flyte and Kubeflow stack up.

Please note that the mentioned features are accurate as of the time of writing, but they may be subject to change in the future.

Simplify infrastructure jargon and manage Kubernetes complexity

Flyte lets ML practitioners create without having to navigate infrastructure jargon and Kubernetes details. It segregates the user and the platform teams and lets the user team — data scientists, ML practitioners and data engineers — focus on building models instead of setting up infrastructure. Kubeflow requires Kubernetes and DevOps expertise, which may slow down the development of ML pipelines because not all ML practitioners are comfortable with Kubernetes and Ops.

Define diverse data types and transformations

You can pass Pandas DataFrames among Flyte tasks, load a DataFrame to a BigQuery table using structured datasets, offload data to and download data from cloud URIs using FlyteFiles, and more. Meanwhile, Kubeflow enforces a type system, but it doesn’t support data types beyond fundamental Python types and artifacts/files. Kubeflow needs to be told what to do when it encounters another type, such as an s3 URI. Flyte, however, automates the interaction with S3 (and GCS); supports intra- and intercommunication among different cloud services and the local file system; and reduces the need to write boilerplate code.

Say goodbye to peculiar DSL

Flyte’s Python SDK (Flytekit) lets ML practitioners write Python code. In contrast, the Kubeflow Python SDK feels like a new domain-specific language (DSL) that heavily relies on Kubernetes concepts. The v2 Python DSL in Kubeflow is not purely Pythonic, which poses challenges for Python developers.

Constructing pipelines in Kubeflow is not as intuitive as in Flyte due to the following reasons:

  1. The output type annotation needs to be declared within the input arguments declaration in case of custom container components and output artifacts.
  2. Passing output from a lightweight Python component as an input to a downstream component requires using the `.output` attribute of the source task.
  3. Kubeflow’s containerized and custom container components lean towards an infrastructure DSL, unlike lightweight components which are Pythonic. This means that code imported from different modules needs to be refactored to use containerized components, creating friction in the developer experience and impeding development cycles.
  4. Kubeflow emphasizes the use of containerized and custom container components for production usage, which brings it closer to an infrastructure DSL.

Simplify the orchestration of dynamic ML pipelines

For the most part, ML is dynamic, so it’s important to be able to construct dynamic DAGs. Here are some example use cases:

  • If a dynamic modification is required in the code logic, such as determining the number of training regions, programmatically stopping the training if the error surges, introducing validation steps dynamically, or data-parallel and sharded training
  • During feature extraction, if there’s a need to decide on the parameters dynamically
  • Building an AutoML pipeline
  • Tuning hyperparameters dynamically while a pipeline is in progress

Kubeflow supports dynamism with DSL recursion, which presents its own set of problems: It’s a little awkward to construct dynamic DAGs with recursion, it doesn’t work well with deep workflows, output cannot be dynamically resolved and types are ignored. Moreover, KFP v2 doesn’t yet provide support for recursion.

In Flyte, dynamic workflows enable the construction of dynamic DAGs. When a Flyte task is decorated with `@dynamic`, Flyte evaluates the code at runtime and determines DAG structure. Flyte dynamic workflows offer much more flexibility to compose and run dynamic DAGs:

  • Dynamism isn’t restricted to recursion
  • Data passing isn’t any different from general Flyte tasks
  • Types are respected

Connect with the tools you love

Flyte offers extensibility at all levels, and with the inclusion of Flyte Agents, the integration process has never been smoother.

Kubeflow’s native integrations:

  • Kale
  • KServe
  • Fairing
  • Seldon core
  • BentoML
  • MLRun Serving
  • TensorFlow Serving

Flyte’s native integrations:

  • HuggingFace Datasets
  • Vaex
  • Polars
  • Modin
  • Great Expectations
  • Pandera
  • DuckDB
  • BigQuery
  • Snowflake
  • Dolthub
  • SQLAlchemy
  • Hive
  • Databricks
  • DBT
  • Apache Spark
  • AWS Athena
  • AWS Sagemaker
  • Dask
  • Kubeflow MPI
  • Ray
  • Kubeflow TensorFlow
  • Kubeflow PyTorch
  • ONNX TensorFlow, PyTorch, Scikit Learn
  • MLFlow
  • Whylogs
  • Kubernetes Pods
  • AWS Batch
  • Papermill

Bottom line: If you’re looking to enhance user experience, simplify data and ML workflow development, while also enjoying the benefits of modern orchestration and simplified infrastructure management, we built Flyte just for you.

Why engineers choose Flyte over Kubeflow

Speed development cycles

Lightweight components in Kubeflow are easy to compose, but containerized and custom container components are the preferred choice for production usage and they take you closer to infrastructure code constructs, which isn’t desirable. Flyte offers a Pythonic way of composing workflows locally or during deployment that simplifies development and speeds iteration cycles. 

Iterate using a single command

Iterating with Flyte requires you to run a single `pyflyte` command that serializes, registers and triggers the code on the Flyte backend.

Recover when there’s a failure

Flyte lets you recover an individual execution by copying all successful node executions. This is a critical feature for compute-intensive ML workflows to avoid resource overuse. Ideally, skipping successful task node executions means better resource management and quicker iterations.

Join an extremely helpful community

The No. 1 reason people cite for loving Flyte? Our community. We ensure that no question goes unanswered, and we tackle a diverse set of problems with the help of power users spanning several industries.

Be a part of our community

From Kubeflow to Flyte

If you’re a Kubeflow user, transitioning to Flyte is a matter of removing boilerplate code and making your workflows much simpler. After some initial investment in technical integration required, Flyte’s scalability and agility make it well worth the effort. Your team will appreciate the benefits of using Flyte as your workflows, team and needs evolve.

See for yourself how easy it is to migrate code from Kubeflow to Flyte

Copied to clipboard!
from kfp import dsl
from kfp import client

def addition_component(num1: int, num2: int) -> int:
    return num1 + num2

def my_pipeline(a: int, b: int, c: int = 10):
    add_task_1 = addition_component(num1=a, num2=b)
    add_task_2 = addition_component(num1=add_task_1.output, num2=c)
Copied to clipboard!
from flytekit import task, workflow

def addition_component(num1: int, num2: int) -> int:
    return num1 + num2

def my_pipeline(a: int, b: int, c: int = 10):
    add_task_1 = addition_component(num1=a, num2=b)
    add_task_2 = addition_component(num1=add_task_1, num2=c)

Kubeflow and Flyte code samples (we intentionally picked a simple example to showcase the contrast).

Begin your Flyte journey today