Trigger.dev vs ZenML
A side-by-side comparison of Trigger.dev and ZenML across 26 workflow orchestration capabilities. Trigger.dev fully supports 17 of them and ZenML fully supports 13; of the 12 mandatory features, Trigger.dev covers 10 and ZenML covers 9.
Trigger.dev
Trigger.dev is an open-source platform for building and running durable background tasks, cron jobs and AI agent workflows in TypeScript, with retries, queues and full observability.
ZenML
ZenML is an open-source MLOps framework that lets teams write ML and AI pipelines in Python once and run them on any orchestrator and infrastructure stack.
Feature comparison
11 of 26 capabilities differ between Trigger.dev and ZenML.
| Capability | Importance* | Trigger.dev | ZenML |
|---|---|---|---|
DAG-based workflow definition Define dependencies and execution order Mandatory | Mandatory | Partial | Yes |
Code-first workflows Versionable, testable workflows Mandatory | Mandatory | Yes | Yes |
Declarative workflow model Predictability, reproducibility Mandatory | Mandatory | No | Partial |
Task dependency resolution Correct execution order Mandatory | Mandatory | Yes | Yes |
Persistent workflow state Crash recovery, observability Mandatory | Mandatory | Yes | Yes |
Task retries Handle transient failures Mandatory | Mandatory | Yes | Yes |
Retry policies (backoff, limits) Avoid cascading failures Recommended | Recommended | Yes | Yes |
Scheduling (cron / time-based) Automated recurring workflows Depends | Depends | Yes | Partial |
Event-driven execution React to external systems Depends | Depends | Partial | Partial |
Backfills / replays Reprocess historical data Depends | Depends | Partial | No |
Idempotency support Safe retries & replays Mandatory | Mandatory | Yes | Partial |
Exactly-once semantics Correctness for business workflows Optional | Optional | Partial | No |
Long-running workflows (days/months) Business processes, approvals Depends | Depends | Yes | Partial |
Human-in-the-loop steps Approvals, manual actions Optional | Optional | Yes | Partial |
Dynamic workflows at runtime Conditional execution Depends | Depends | Yes | Partial |
Workflow versioning Safe evolution over time Recommended | Recommended | Yes | Yes |
Distributed execution Scalability Mandatory | Mandatory | Yes | Yes |
High availability scheduler Production reliability Recommended | Recommended | Partial | Partial |
Fault-tolerant execution Crash safety Mandatory | Mandatory | Yes | Partial |
Observability (logs, states, UI) Debugging & ops Mandatory | Mandatory | Yes | Yes |
Workflow-level visibility Understand end-to-end runs Mandatory | Mandatory | Yes | Yes |
Task isolation Failure containment Mandatory | Mandatory | Yes | Yes |
Native Kubernetes support Modern infra compatibility Depends | Depends | Yes | Yes |
Multi-language SDKs Polyglot systems Optional | Optional | No | No |
Deterministic replay Safe recovery & correctness Optional | Optional | No | No |
Pluggable execution backends Flexibility Recommended | Recommended | Partial | Yes |
* To be considered a complete workflow orchestration tool, a tool must implement the features marked as mandatory according to our assessment.
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Read the full profiles, including pros and cons, installation and alternatives.
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