Is F-Pulse really free?

Yes. F-Pulse is open source under the MIT license. Self-host it, fork it, modify it, ship it. There is no telemetry, no usage caps, and no feature gating. F-Pulse+ is a separate paid tier for production deployments.

How does F-Pulse compare to Apache Airflow?

F-Pulse is visual-first while Airflow is code-first. F-Pulse uses a drag-and-drop canvas with SQL transforms, while Airflow requires Python DAGs. F-Pulse is faster to start for SQL-first teams; Airflow gives more flexibility for complex programmatic workflows.

What connectors does F-Pulse support?

F-Pulse ships 124 first-party connectors: databases (Postgres, MySQL, SQL Server, Oracle, MongoDB), warehouses (Snowflake, BigQuery, Redshift, Databricks), object storage (S3, GCS, Azure Blob, MinIO), streaming (Kafka, Redpanda), 100+ SaaS APIs, vector databases, and CDC via Debezium.

When should I upgrade from F-Pulse to F-Pulse+?

Upgrade when you need production deployment with encrypted credentials, role-based access control, audit trail, IP restrictions, or session management. Solo developers on a laptop don't need F-Pulse+.

How do I install F-Pulse?

The fastest path is Docker: clone the repo, run docker compose up -d, and the full stack is up in under two minutes. A native desktop installer for Windows and macOS is coming soon.

Migration Guides

Move to visual pipeline control

Step-by-step guides for switching from code-first orchestrators to F-Pulse. Your data stays where it is — only the control layer changes.

See every step

Live data preview at every node

No Python required

SQL transforms with auto-complete

3-minute setup

docker compose up -d

Zero data egress

Self-hosted, your infrastructure

Move from Apache Airflow

to F-Pulse visual pipeline control

Common pain points

✕DAGs are Python code — analysts can't modify without engineering help

✕30+ minutes to set up, longer to understand the executor model

✕No live data preview — debugging means reading logs

✕Scheduler bottlenecks at scale without careful tuning

What you gain with F-Pulse

Visual canvas replaces Python DAGs — anyone who writes SQL can build pipelines

Live data preview at every node — see the data, not just the logs

3-minute setup vs 30-minute Airflow deploy

SQL transforms with schema awareness and AI-assisted generation

Same connectors (databases, warehouses, SaaS) — visual configuration

Migration steps

1.Install F-Pulse (`docker compose up -d`)

2.Map your Airflow DAG tasks to F-Pulse nodes on the canvas

3.Move SQL queries into F-Pulse's expression editor (with auto-complete)

4.Configure connections via the visual connection picker

5.Set up equivalent cron schedules in F-Pulse's scheduler

6.Test with live data preview, then cut over

What doesn't change: Your data stays where it is. F-Pulse connects to the same databases, warehouses, and APIs. Nothing moves except the orchestration layer.

Move from Prefect

to F-Pulse visual pipeline control

Common pain points

✕Python-only pipeline definition — no visual debugging

✕Flow state is opaque until you check the UI after the fact

✕Integration ecosystem is smaller than Airflow's

✕Cloud dependency for advanced features

What you gain with F-Pulse

Visual pipeline builder — see the flow before you run it

Per-node data preview — inspect data at every transformation step

124 connectors included out of the box (vs installing integrations)

CDC support via Debezium (not available in Prefect)

Self-hosted with zero cloud dependency

Migration steps

1.Install F-Pulse (`docker compose up -d`)

2.Map your Prefect flow tasks to F-Pulse visual nodes

3.Move Python transform logic to SQL transforms (or keep Python where needed)

4.Configure connections using F-Pulse's connection picker

5.Set up schedules and retry policies

6.Run both in parallel during transition, then cut over

What doesn't change: Your infrastructure doesn't change. F-Pulse reads from the same sources and writes to the same destinations. Migrate the orchestration, not the data.

Move from Dagster

to F-Pulse visual pipeline control

Common pain points

✕Asset-centric model has a learning curve — not intuitive for SQL teams

✕Python-only definition requires software engineering skills

✕Smaller connector ecosystem

✕Self-hosted deployment requires careful configuration

What you gain with F-Pulse

Visual pipeline design — no Python required for standard ETL

SQL-first transforms with live preview and AI assistance

124 connectors included (databases, warehouses, SaaS, CDC, vector DBs)

Medallion ETL templates to start fast

One-command Docker setup

Migration steps

1.Install F-Pulse (`docker compose up -d`)

2.Map Dagster assets to F-Pulse pipeline nodes

3.Move SQL transformations to the expression editor

4.Configure data sources with the visual connection picker

5.Set up schedules (F-Pulse uses cron, same as Dagster's schedule definitions)

6.Validate with live data preview, then switch

What doesn't change: Your data assets remain in your warehouse. F-Pulse doesn't move your data — it replaces the orchestration layer with visual control.

Move from n8n

to F-Pulse visual pipeline control

Common pain points

✕Designed for general workflow automation — not data engineering

✕No SQL transform support, no data preview

✕No CDC, no vector database connectors

✕Enterprise features locked behind paid tier

What you gain with F-Pulse

Purpose-built for data engineering (not generic automation)

Full SQL expression editor with schema awareness

CDC via Debezium (PostgreSQL, MySQL, SQL Server, MongoDB, Oracle)

Vector database connectors (Pinecone, Weaviate, Qdrant, Chroma, pgvector)

Medallion architecture templates, data quality nodes

F-Pulse is MIT licensed — no 'sustainable use' restrictions

Migration steps

1.Install F-Pulse (`docker compose up -d`)

2.Identify n8n workflows that are data pipelines (vs general automation)

3.Recreate data workflows on F-Pulse's visual canvas

4.Use SQL transforms instead of n8n's code nodes

5.Configure database and API connections

6.Keep n8n for non-data automation, use F-Pulse for data pipelines

What doesn't change: You can run both tools. n8n handles IT automation and webhook workflows. F-Pulse handles data engineering. They complement each other.

Ready to see your pipelines?

Install F-Pulse in 3 minutes. Keep your existing data infrastructure — just change the control layer.

Download F-Pulse Full Comparison