Union.ai vs. Temporal

**Durable execution means orchestrating logic and infrastructure.**

Union.ai, the enterprise Flyte platform, is the durable runtime for the AI era. Don’t get stuck manually fixing the 50% of failures caused by infrastructure.

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50% of workflow failures are caused by compute infrastructure

Teams building AI, ML, and agentic systems know the pain of manually babysitting and fixing infra-caused failures:

Out-of-memory (OOM) errors
Node interruptions
Container pre-emption

These failures prevent workflows from truly durable execution. And the more you scale your workloads, the worse this problem becomes.

Temporal can’t orchestrate compute

Temporal was designed as a durable microservices orchestrator. But because it can’t orchestrate compute, it can’t solve the 50% of failures caused by compute infra:

Replay log. Like Union.ai, when an execution fails, Temporal replays completed events to reconstruct state and picks up from where it left off, without re-running work that already succeeded.
No diagnosis. While Temporal can detect infra-caused failures, it isn’t able to clarify the cause of these failures. It leaves diagnosis to the user.
Can’t provision resources. Without infrastructure-awareness, Temporal cannot fix these failures by re-provisioning compute resources.

If you’re running simple microservices, these compromises can be totally fine. But if you’re running AI or agentic projects, you’ll need an AI runtime.

Temporal

Flyte 2

Infrastructure-aware failure recovery

Can detect that a workflow failed due to infrastructure, but cannot diagnose the cause or fix it; OOM errors, node interruptions, and container preemption fall to the user to diagnose and remediate

Classifies infra failures and retries automatically with configurable backoff

Same plus typed exception handling; catch OOM and retry with more memory; catch spot preemption and resume from checkpoint without rebuilding the workflow

Durable execution and replay

Limited

Replays completed events to reconstruct state; picks up from the last successful activity without re-running completed work

Caches task outputs; re-executions skip completed tasks automatically

Same task-level caching plus infrastructure failure handling; durable execution extends to compute-caused failures, not just logic failures

Per-task compute provisioning

Workers are user-managed processes; Temporal has no concept of compute resources, GPU types, or infrastructure; workloads share whatever environment the worker runs in

Each task declares its own compute requirements; Kubernetes provisions the right resources per task

Same plus task-level routing across clusters; one workflow can preprocess on CPU spot, train on H100s, and validate on cheaper GPUs without manual coordination

Dynamic workflows at runtime

Workflows can make decisions and schedule activities dynamically at runtime; a core feature of the platform

Pure Python control flow; tasks branch and adapt based on intermediate results

Same plus runtime resource overrides; tasks can request different hardware mid-workflow based on what intermediate results require

Python-native authoring

Partial

SDKs available in Python and other languages, but the activity/workflow model requires Temporal-specific patterns; not pure Python

Pure Python with decorators; workflows are real Python functions with loops, conditionals, and normal async control flow

Same Python model; no rewriting needed to move from Flyte OSS to Union.ai

AI/ML workload support

General-purpose workflow engine; no native support for GPU workloads, ML training jobs, distributed training frameworks, or model serving

Purpose-built for AI/ML; native plugins for Spark, Ray, PyTorch, and distributed training

Same plus end-to-end AI lifecycle in one platform: orchestration, training, fine-tuning, and inference serving

Data lineage and artifact tracking

Tracks workflow state and event history, not data provenance; no native lineage across runs or artifact versioning

Partial

Typed inputs and outputs are tracked per task, lineage is limited to execution metadata and user conventions

Full cross-run provenance graph queryable through UI and SDK; artifacts are typed, versioned, and lineage-linked; when a dataset is bad, immediately identify every downstream model and artifact affected

Task fanout / parallelism

Limited

Supports parallel activities but not designed for high-cardinality ML fan-out; large parallel workloads require external coordination

~10K tasks

Bounded by Kubernetes control-plane scheduling throughput

250K+ tasks

Purpose-built execution substrate bypasses the K8s pod-scheduler bottleneck that caps Flyte OSS at high cardinality

Observability

Partial

Workflow state and event history visible in UI; no compute metrics since Temporal does not manage infrastructure

Unified execution UI with task state, logs, and inputs/outputs

Same plus per-task CPU, GPU, and memory profiling; persisted logs queryable after pod termination; cost attribution by workflow, project, and team

Union.ai orchestrates logic and compute infra

Union.ai, the enterprise Flyte platform, is expressly designed for AI engineers. Using its infrastructure-awareness, teams can build workflows that are:

Compute-aware, autonomously solving both logic and infra-caused failures like OOM errors
Self-healing, so pipelines that fail autonomously recover and continue
Dynamic, so your AI systems and agents can make decisions on the fly at runtime
Authored in pure Python, so you can easily go from local dev to production in your cloud
Scalable and efficient, handling large task fanout and parallelism with ease

Union.ai is built for production

The platform deploys to your secure cloud

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 2 OSS: Open-source AI runtime

Flyte 2 OSS is the most powerful open-source AI runtime, 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 runtime available. It’s trusted by teams worldwide with 80M+ downloads and growing.