Back to blog
architecturekubernetescomparison

Workflow engines compared: where Tiny Systems fits

An honest look at Node-RED, n8n, Temporal, Argo Workflows, Airflow, and others — what they do well, where they fall short, and why we built something different.

There are dozens of workflow and automation tools. Before building Tiny Systems, we used many of them. Here's what we found.

The landscape

Workflow tools break down roughly like this:

  • Visual-first (Node-RED, n8n, NiFi) — drag and drop, pre-built connectors, fast integration
  • Code-first (Temporal, Airflow, Prefect) — workflows in code, full control
  • K8s-native (Argo Workflows, Tekton) — CRDs, each step is a pod
  • Hybrid (Windmill, Direktiv) — mix of visual and code

Tiny Systems sits where visual-first meets K8s-native. That intersection is surprisingly empty.

Node-RED

The original visual flow tool. IBM built it for IoT, now it's everywhere in home automation and simple integrations.

Good at: community (5,000+ contributed nodes), instant feedback when you deploy, genuinely easy to learn, great for IoT and edge.

Bad at: single-threaded Node.js, so one heavy flow blocks everything. No clustering — scaling means running independent copies. No built-in HA. State lives in a JSON file.

Node-RED is a single process. Tiny Systems modules are stateless K8s operators — add replicas and traffic distributes automatically. Node-RED makes sense for a Raspberry Pi. We're for production clusters.

n8n

Self-hostable Zapier alternative. Nice UI, 400+ integrations, growing AI workflow features.

Good at: integration catalog, queue mode with Redis for horizontal scaling, AI workflow builder, inline JS/Python nodes.

Bad at: "fair-code" license (not truly open source), memory hungry (single worker can hit 2GB+), runs on K8s but isn't K8s-native (no CRDs, no operator integration), queue mode adds latency for webhooks.

n8n is integration-focused — connect Slack to Google Sheets to Airtable. We're infrastructure-focused — build services that run as Kubernetes operators. Different problems. If you need 400 connectors, use n8n. If you need your workflows to be K8s resources with native scaling and RBAC, that's us.

Temporal

The gold standard for durable execution. Guaranteed workflow completion through crashes, restarts, network failures.

Good at: event-sourced state replay (workflows survive anything), multi-language SDKs, clean separation of orchestration and execution, battle-tested at Uber/Netflix/Snap scale.

Bad at: purely code-first (no visual editor at all), operationally complex to self-host (database cluster, multiple server roles), steep learning curve (deterministic constraints — no random, no time, no side effects in workflow functions), resource-heavy server.

Temporal is for developers building distributed microservices who need guaranteed completion. We're for teams who want to see their workflows and have them run as K8s resources. Temporal does complex orchestration patterns better than we ever will. We make Kubernetes automation accessible to people who don't want to write Go controllers.

Argo Workflows

K8s-native workflow engine. CNCF graduated. Each step is a pod.

Good at: true K8s-native (workflows are CRDs), strong DAG support, mature and well-maintained, Hera Python SDK.

Bad at: YAML-first (complex workflows get deeply nested), no visual editor for building (UI is monitoring only), every step = new pod = cold start, designed for batch jobs not long-running services, UI crashes with large workflows.

Argo runs workflows as batch jobs. Each step starts a pod, does work, exits. Tiny Systems runs components as long-lived operators. Nodes within a module share a process and talk via Go channels. Argo is "run this DAG." We're "keep this flow running and reacting to events."

Airflow

The standard for data pipeline orchestration. Now at 3.0.

Good at: massive ecosystem, KubernetesExecutor runs tasks as isolated pods, Python-native, 3.0 adds event-driven triggers and DAG versioning.

Bad at: heavy operational footprint (scheduler, webserver, executor, workers, metadata DB), DAG parsing overhead (all Python files re-parsed every 30 seconds), metadata DB becomes a bottleneck, Python-only until recently, designed for scheduled batch not event-driven.

Airflow is for scheduled data pipelines. "Run this ETL every hour." We're for event-driven flows. "When this HTTP request arrives, route it, transform it, send it to Slack." Airflow also needs a metadata database. We use K8s CRDs.

Windmill

Growing alternative to Airflow + Retool. Rust server, multi-language scripts.

Good at: fast Rust server, multi-language (TypeScript, Python, Go, PHP, Bash, SQL, Rust), hybrid model with visual flow builder over code scripts, built-in app builder for UIs.

Bad at: AGPL license, each script spins up an isolated runtime (~256MB+), not K8s-native, smaller community.

Windmill is script-centric — each node runs a script. We're component-centric — each node is a compiled Go component with typed ports and K8s lifecycle. Windmill is for internal tools. We're for production infrastructure.

NiFi

Enterprise data routing at scale. Recently overhauled in 2.0.

Good at: high-volume data ingestion and routing, 2.0 removes ZooKeeper dependency, real-time visual canvas, built-in backpressure.

Bad at: heavy JVM footprint, stateful by nature (conflicts with K8s ephemeral pods), steep learning curve, primarily data routing not general workflow.

NiFi moves data between systems at high volume. We orchestrate logic — routing decisions, API composition, event handling. NiFi is a firehose. We're a circuit board. Our blocking I/O gives you backpressure without NiFi's complexity.

Tekton

K8s-native CI/CD pipelines. CRDs for everything.

Good at: true K8s-native, strong CI/CD semantics, backed by the CD Foundation.

Bad at: exclusively CI/CD, extremely verbose YAML, can't run outside K8s, smaller ecosystem than GitHub Actions.

Tekton is for build pipelines. We're for runtime automation. They're complementary.

Direktiv

Event-driven serverless workflows on K8s + Knative.

Good at: serverless (containers scale to zero), any language, CloudEvents integration.

Bad at: requires both K8s and Knative, still pre-1.0, small community, Knative's constraint that containers only run while handling requests limits long-running tasks.

Direktiv's serverless model means cold starts on every invocation. Our modules are long-running operators — always warm. Direktiv makes sense for sporadic event-triggered jobs. We're for always-on flows.

Prefect

Modern Python workflow orchestration. Clean API, hybrid execution.

Good at: minimal boilerplate (just decorators), hybrid model (orchestration in cloud, execution in your infra), work pools abstract infrastructure, 6M+ monthly downloads.

Bad at: Python-only, advanced features need Prefect Cloud (paid), smaller ecosystem than Airflow.

Like Airflow, Prefect is for Python data pipelines. We're language-agnostic at the flow level. Different audiences.

Where we fit

Here's what's actually different about Tiny Systems compared to everything above:

Components are K8s resources. Not containers that run on K8s — actual CRDs. A TinyNode has the same lifecycle as a Deployment or Service. RBAC, etcd storage, watch semantics, operator reconciliation.

Same-module communication costs nothing. Nodes within a module share a Go process and use channels. No serialization, no network. Argo and Tekton spin up a pod for every step. We don't.

Blocking I/O gives you backpressure. When an HTTP Server node sends a request downstream, it blocks until the response comes back. No queue management, no dead letter config. The graph topology is the flow control.

No external database. State is in CRDs. Your K8s cluster is the database. Nothing extra to run, disaster recovery through etcd.

Horizontal scaling works like any K8s workload. Modules are stateless operators. Scale them with replicas. An HTTP Server across multiple replicas gets load-balanced by a standard K8s Service. CRDs store slow-changing config, not high-throughput state, so etcd is never the bottleneck.

Your cluster is the compute budget. Most workflow tools run inside their own process boundary. Node-RED is capped by one Node.js process. Airflow workers share a pool. Our modules are regular K8s workloads. Set CPU, memory, and replicas per module independently.

Where we don't fit

We're not the right tool for everything.

  • n8n has 400+ connectors. We have 8 modules with 40+ components. Quick Slack-to-Sheets integration? n8n is faster today.
  • Airflow and Prefect own data engineering. If your job is "run this Python ETL on a schedule," use them.
  • Temporal's event-sourced replay is unmatched for durable execution guarantees. We handle fault tolerance through K8s reconciliation, which works but is different.
  • Tekton and Argo are purpose-built for CI/CD. We're not.
  • We require Kubernetes. If you don't have a cluster, most of these alternatives are easier to get running.

The short version

Most workflow tools are either visual but not production-grade, or production-grade but not visual. Most run on Kubernetes but aren't of Kubernetes.

We're for teams that already run on K8s and want to build automations that are native to their infrastructure. If that's you, give it a try.