The AI era demands AI development infrastructure.

To build and ship AI, you need to orchestrate reliable, dynamic workflows.

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Why is it difficult to productionize AI?

AI engineering teams struggle to move from experiment to production reliably because of outdated devtools and disconnected point solutions. These create a huge amount of operational complexity that make productionizing models, agents, and AI apps a slog.

Pipelines are brittle and break under scale
Workflow logic is fragmented across scripts, schedulers, and glue code
Experiments can’t be reliably reproduced
Handoffs between data and AI teams are challenging
Legacy orchestration tools like Airflow aren’t built to easily handle AI workflows

These create too much infrastructure maintenance and slow, painful iteration.

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Introducing AI Development Infrastructure

AI development infrastructure is AI-native tooling to orchestrate and ship AI workflows and agents. It differs from traditional data orchestrators by handling complex AI/ML workloads at large scale.

To qualify as true AI development infrastructure, a platform must deliver a few key capabilities:

Reliable, durable, reproducible workflows
Ability to achieve massive scale, including parallelism and fanout
Dynamic decision-making at runtime, not just DAG-based workflows
Observability for resource usage, data lineage, failure logs, etc.

In these ways, AI development infrastructure is the central nervous system for building and orchestrating AI.

What is Flyte?

Flyte is the orchestration protocol for modern AI systems, implemented using either the Union.ai enterprise platform or Flyte OSS.

Flyte is:

Dynamic, so your AI systems and agents can make decisions on the fly at runtime
Durable, so pipelines that fail can recover and continue where they left off
Authored in pure Python, so you can easily go from local dev to production in your cloud
Resource-aware, operating in your cloud and auto-scaling to optimize usage
Scalable and efficient, handling large task fanout and parallelism with ease

Flyte is expressly designed for the AI/ML era, so your pipelines adapt at runtime, scale reliably, and survive failure.

Union.ai: The enterprise Flyte platform

Union.ai is a secure platform with a powerful backend architecture to implement the Flyte protocol. Beyond the core capabilities of Flyte, Union.ai offers:

Enhanced scale and performance, with significantly improved actions/run, concurrency, and task startup time
End-to-end AI lifecycle support, including orchestration, training and fine-tuning, and inference
Developer-loved UI, for faster, easier development cycles
Observability, including for data lineage, resource usage, failure logs, etc.
Portability to open-source, for teams looking to avoid lock-in

Teams report that Union.ai accelerates them from prototype to production, cutting iteration cycle time in half.

The Union.ai team offers high-touch support to ensure users are successful.

Flyte OSS: Open-source AI orchestration

Flyte OSS is the most powerful open-source AI orchestrator, bringing Flyte’s core data model, scalability, and reliability to DIY teams. While it lacks some enterprise capabilities of Union.ai, it remains the most capable open-source AI orchestration tool available. It’s trusted by teams worldwide with 80M+ downloads and growing.

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import flyte

env = flyte.TaskEnvironment(
    name="my_flyte_env",
    image=flyte.Image.from_debian_base().with_requirements("requirements.txt"),
    secrets=[
        flyte.Secret(key="HF_TOKEN", as_env_var="HF_TOKEN"),
    ],
    resources=flyte.Resources(cpu=2, memory="4Gi", gpu=1),
    reusable=flyte.ReusePolicy(
        replicas=2,
        concurrency=6,
        scaledown_ttl=60,
    ),
)

@env.task
async def your_function(model_name: str) -> str:
    # task logic here