An Airflow alternative that puts a price on every AI call.
Airflow needs you to run a scheduler, web server, triggerer, and metadata database yourself, all to execute DAGs written only in Python. Its newer agentic tooling turns LLM calls into tasks, but doesn't track what any of them cost; that bill lands wherever your model provider sends it.
Unmeshed self-hosts with a fraction of that infrastructure, and every AI step shows its own price as the workflow runs.
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Overview
Two different approaches to orchestration.
About Unmeshed
Unmeshed is a workflow orchestration platform built by the team behind Netflix Conductor. You define workflows in JSON or YAML, with code-based workers in Java, Go, TypeScript, or Python. Retries, waits, and durable state come built in, and every AI step shows its own cost as the workflow runs.
About Airflow
Apache Airflow is an open source workflow orchestration project built by the Apache Software Foundation and a large community. Pipelines, called DAGs, are authored entirely in Python and run through a scheduler, web server, triggerer, and metadata database that you deploy and operate yourself, or through a managed provider. Airflow 3 added native LLM and approval operators for building agentic pipelines, though the platform doesn't track what any individual AI call costs.
Comparison
Unmeshed vs Airflow, feature by feature.
| Unmeshed | Airflow | |
|---|---|---|
| Platform | ||
| Built for | Code-first orchestration for developers and technical teams | Python-native scheduling and orchestration for data pipelines |
| Workflow definitions | JSON, YAML, or code | Python only, DAGs authored and dynamically generated in code |
| Native integrations | 100+ | 80+ provider packages, thousands of operators |
| Visual designer | Included | Not included |
| Resiliency and operations | ||
| Retry a single failed step without restarting the run | Included | Included |
| Hold, skip, rollback, restart as direct commands | Included | |
| Self-host without standing up a scheduler, webserver, and triggerer separately | Included | Not included |
| Self-hosted deployment | Included | Included |
| Pricing | ||
| Pricing model | Flat, predictable plans | Free open source core, managed providers bill across separate meters |
| Billing dimensions on a managed provider | One, workflow executions | Multiple, cluster time, deployment size, and worker compute |
| Free tier usable in production | Included | Included |
| Upgrade without a sales call | Included | |
| Security, compliance, and governance | ||
| Role-based access control | Included | Included |
| SSO / SAML | Included | |
| Audit logs | Included | |
| Self-hosted / on-premise deployment | Included | Included |
One platform, not five services.
Start building workflows that self-host without a five-piece stack, on a free tier you sign up for yourself.
Pricing
They price the stack. You price the workflow.
A managed Airflow provider typically bills across separate meters: cluster or environment time, deployment size, and worker compute that scales with usage, plus whatever your cloud charges for network traffic. Self-hosting Airflow avoids that bill entirely, but hands you the scheduler, webserver, triggerer, and metadata database to run instead. Unmeshed counts one flat number, a workflow execution, whichever way you choose to run it.
Free forever
For solo builders exploring workflow orchestration.
$0/month
No credit card required
Get Started Free- 2,000 workflow executions / month
- Unlimited steps per workflow
- Community support
Premium
For individuals and small teams ready to orchestrate at scale.
$20/month
Billed monthly, annual saves 2 months
Get Started- 10,000 workflow executions / month
- Batch job scheduling
- Email support
Enterprise
For organizations with scale, compliance, and infrastructure requirements.
Custom pricing
Tailored to your scale and compliance requirements
Contact Sales- Unlimited executions, workflows & seats
- SSO / SAML and RBAC
- Dedicated support with SLAs
Managed Airflow pricing, for reference
Cluster / environment
Hourly base cost for the shared or dedicated environment your Airflow deployment runs in
Deployment
Per-deployment hourly cost covering the scheduler, webserver, and triggerer, sized to your DAG volume
Worker compute
Billed while tasks actually run, scaling to zero when idle, plus any cloud network pass-through costs
Pricing structures vary by provider, but managed Airflow platforms commonly price across three or four separate meters: cluster or environment time, deployment size, and worker compute that scales with usage, often starting well under a dollar per hour per meter before volume discounts, with custom Enterprise tiers for SSO, dedicated support, and compliance features. Self-hosting the open source project avoids these fees entirely but shifts the cost to your own infrastructure and operations time. Confirm current numbers with whichever provider you're evaluating before publishing.
Agentic AI
Every AI step, priced as it runs
Unmeshed
Every AI step in Unmeshed shows its own price while the workflow runs: code steps at no charge, model calls priced live, so you know the cost of one execution before it scales to a million.
Airflow
Airflow 3's newer agentic tooling turns LLM calls into named, retryable tasks, a genuinely solid design, but it stops at execution. What each call actually costs isn't tracked anywhere in the platform; that bill just shows up wherever your model provider sends it.
Cost and infrastructure
The stack shows up before the workflow does
Airflow
Running Airflow yourself means standing up a scheduler, a web server, a triggerer, and a metadata database before a single DAG executes, then choosing and operating an executor, Celery, Kubernetes, or local, to actually run tasks. Each piece needs its own health checks, scaling plan, and upgrade path.
Unmeshed
Unmeshed runs on a disk log architecture, so growing usage is a question of compute and storage, not a fifth service to keep alive.
Feature comparison
Airflow makes you plan for this. Unmeshed already did.
The stuff you won't have to build yourself.
Self-hosting
Run it yourself, without a five-piece stack to keep alive.
Unmeshed
Self-host on your own VM or Kubernetes cluster, or use Unmeshed Cloud. On-premises deployment is included on the Enterprise plan.
Airflow
Self-hosted Airflow needs a scheduler, webserver, triggerer, and metadata database, plus an executor like Celery or Kubernetes to run tasks, all deployed and kept healthy yourself.
Pricing predictability
Know your bill before the workflow runs.
Unmeshed
Workflow executions are counted flat, no matter how long a workflow runs or how much compute it uses.
Airflow
Managed Airflow providers typically bill across cluster time, deployment size, and worker compute separately, each scaling on its own.
AI cost visibility
See what a model call costs, while it's still running.
Unmeshed
AI steps run alongside regular code steps, each with its own visible price as the workflow executes.
Airflow
Agentic operators turn LLM calls into named, retryable tasks, but don't track what any call costs; that bill goes straight to your model provider.
Workflow definitions
Write it in code, or don't.
Unmeshed
Workflows are defined in JSON, YAML, or code, whichever fits the team building it.
Airflow
DAGs are authored entirely in Python, with no JSON or YAML option for defining a pipeline.
Developer experience
Just functions. No scheduler to stand up first.
Workers are plain functions. Register them, start a process, and the platform takes care of the durable execution machinery, no scheduler, web server, or triggerer to deploy before your first workflow runs.
Retries, state, and scheduling come built in, so there's no extra worker choreography to hand roll.
from unmeshed.sdk.configs.client_config import ClientConfig
from unmeshed.sdk.unmeshed_client import UnmeshedClient
from unmeshed.sdk.decorators.worker_function import worker_function
client_config = ClientConfig()
client_config.set_client_id("your-client-id")
client_config.set_auth_token("your-auth-token")
client = UnmeshedClient(client_config)
@worker_function(name="charge-order", namespace="default", max_in_progress=100)
def charge_order(input: OrderInput) -> ChargeResult:
# your business logic — the engine handles
# state, retries, and observability
return charge_card(input)
client.start()Frequently asked questions
Still have questions? Talk to us.