Unmeshed vs Netflix Conductor
We built Netflix Conductor. Here's what we built next.
Conductor OSS has stalled. Unmeshed is its successor, built by the same people, with the same core concepts and terminology you already know. Most teams migrate in weeks, not quarters.
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Netflix Conductor Unmeshed
─────────────────────────────────────────
Workflow → Process
Task → Step
Decision → Switch Case
taskReferenceName → ref
# Same concepts. Same creators. New engine.Why Teams Move
Why teams are moving.
OSS momentum stalled
Conductor's open source project has lost its momentum. It's just not moving the way it used to. That leaves your orchestration layer resting on a core that's stalled out, a bet most platform teams aren't willing to make.
Predictable pricing, no per-action fees
Your bill shouldn't climb just because your workflow volume did. Unmeshed pricing stays flat and predictable, with no per-action fees, and you can start on the free tier without a credit card.
Modern platform
One engine runs both your workflows and your AI agents. It's MCP-native, schedules on a standard cron down to one-minute intervals, and keeps execution durable, with a human brought in whenever a step needs approval, all backed by full observability.
Concept Mapping
You already know how Unmeshed works.
The concepts you use every day in Conductor have direct equivalents in Unmeshed, usually with a shorter name.
| Netflix Conductor | Unmeshed | What changes |
|---|---|---|
| Workflow | Process | Same idea: a definition that orchestrates your steps end to end. |
| Task | Step | Workers poll and execute, exactly as they do in Conductor. |
| Decision | Switch Case | Branching with simpler, script-driven case mapping. |
| taskReferenceName | ref | Step references get a shorter name in the definition schema. |
| ${...} | {{ ... }} | Variable references use a familiar template syntax. |
Platform Comparison
An honest look at your options.
Every team weighing Conductor OSS, Orkes, and Unmeshed is asking the same questions. Here's how each platform approaches them.
| Capability | Conductor OSS | Orkes | Unmeshed |
|---|---|---|---|
| Maintained by Conductor's original creators | Community-maintained; no longer driven by the original Netflix team | Founded by members of the original Conductor team | Yes. Built by the people who built Netflix Conductor. |
| Hosting options | Self-hosted only; you operate, scale, and patch it | Managed cloud, with enterprise deployment options | Managed cloud or self-hosted. Same engine, either way. |
| Pricing approach | Free software; total cost is your infrastructure and engineering time | Commercial plans; see Orkes pricing for current models | Predictable pricing, no per-action fees; free tier to start |
| Scheduling | Poll-based scheduling; latency depends on tuning and deployment | Managed Conductor runtime; see Orkes docs for scheduling characteristics | Standard 5-field cron scheduling, down to one-minute intervals, built into the engine. |
| MCP / AI-agent orchestration | Not a core feature of the original project | Offers AI orchestration capabilities | MCP-native, with workflows and AI agents running on the same engine. |
| Human-in-the-loop | Supported via the HUMAN task type | Supported through human task features | Built in. Pauses for approvals and resumes on its own. |
| Migration tooling | N/A, this is the platform most teams are migrating from | Runs existing Conductor definitions | Documented concept mapping, familiar SDKs, guided migration support |
This table describes each platform's approach in neutral terms based on public information. Spot something out of date? Tell us and we'll correct it.
Worker Code
Your workers barely change.
Your worker code barely changes. It's the same poll-and-execute model and the same shape. Register your function against a step name, and let the engine handle the rest.
Netflix Conductor
from conductor.client.automator.task_handler import TaskHandler
from conductor.client.configuration.configuration import Configuration
from conductor.client.worker.worker_task import worker_task
@worker_task(task_definition_name="charge-order")
def charge_order(order_id: str) -> dict:
# your business logic
return {"status": "CHARGED", "order_id": order_id}
task_handler = TaskHandler(configuration=Configuration())
task_handler.start_processes()Unmeshed
from unmeshed.sdk.configs.client_config import ClientConfig
from unmeshed.sdk.unmeshed_client import UnmeshedClient
from unmeshed.sdk.apis.workers.worker import Worker
def charge_order(input: dict) -> dict:
# same business logic, same poll-and-execute model
return {"status": "CHARGED", "order_id": input["order_id"]}
client = UnmeshedClient(ClientConfig())
client.register_worker(
Worker(execution_method=charge_order, name="charge-order")
)
client.start()Migration Path
A migration path, not a leap of faith.
Map definitions
Translate your workflow JSON into Unmeshed processes with the documented concept mapping. Most fields already line up.
Port workers with familiar SDKs
Keep your poll-and-execute model as is. Just re-register your task workers as Unmeshed steps in Java, Go, Python, or TypeScript.
Cut over with parallel runs
Run both engines side by side, compare outputs on real traffic, and move over once the numbers agree.
First workflow running in minutes; typical migrations land in weeks.
FAQ
Frequently asked questions
Explore More
Explore more of the platform.
Go deeper on how Unmeshed works and how the migration fits together.
Workflow orchestration engine
Durable execution and retries, with cron scheduling down to one-minute intervals, written in the language your team already uses.
AI agent orchestration
MCP-native orchestration for agents and LLM steps, on the same engine as your workflows.
API orchestration
Compose APIs and services into reliable, observable, long-running processes.
Conductor migration guide
Step-by-step concept and schema mapping, with worker and step-type equivalents.
From Netflix Conductor to Unmeshed
The story and rationale behind the successor, from the team that built Conductor.
Unmeshed vs. other orchestrators
See how Unmeshed compares across the broader orchestration landscape.
Bring your Conductor workflows home.
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