Prefect alternative — Prefect vs Skippr

Prefect is an Apache-2.0 Python orchestrator. You decorate functions as tasks and flows; the engine tracks state, retries, and runs. Latest on PyPI is 3.9.1 (30 Oct 2026). 3.9.0 (21 Oct 2026) dropped Python 3.10; 3.9 needs Python 3.11+. Self-host is Prefect Server. Prefect Cloud Hobby is free (2 users, 5 deployments, 500 minutes of Prefect Serverless, 7-day run retention). Starter is 100 USD/month (3 users, 20 deployments, 75 hours Serverless). Team is 100 USD/user/month (4 to 8 users, 100 deployments, 225 hours Serverless, 24-hour audit log). Enterprise is a quote (SSO, object-level RBAC, PrivateLink). There is no first-party Iceberg writer. CDC is a flow you author (Debezium, a warehouse connector, PyIceberg in a task). This is not Iceberg ∪ WAL you query in one binary.

Skippr is an open Streamhouse you run as one Rust binary. Write skippr.yml (or the same thing in Python), discover, sync. You query live state over Iceberg ∪ WAL, and the history is ordinary Iceberg in your bucket. No Cloud seat per flow. ELT into a warehouse is one path, not the product.

Feature	Skippr	Prefect
Deploy	Single Rust binary, your VPC or on-prem	Prefect Server, or Prefect Cloud + work pools
Schema	In-stream evolution, sibling columns	Whatever the task returns. No Iceberg schema of its own
Storage	Open Iceberg on Parquet you own	None of its own. PyIceberg in a task if you write it
CDC	Log-based; fails closed if the dest cannot honour	A flow you author. Prefect schedules it
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	After the flow succeeds. UI observes the run
Pricing	Free self-hosted engine	Apache-2.0. Cloud Hobby free; Starter from 100 USD/month.

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What Prefect is good at

Python you already ship, with retries, caching, and a UI that shows the run without a YAML DAG. That is the product. Deployments, work pools, and automations are why some teams left cron and did not pick Airflow. If the job is "this function after that function," that is a real product.

Where the job is wider

Prefect is an orchestrator. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Iceberg still starts in PyIceberg, Spark, or Flink inside a task. Postgres CDC still starts in Debezium or skipprd, then a flow applies it. Skippr will not replace Prefect Cloud, work pools, or @flow.

The API, workers, and Cloud seats (or the self-hosted server) are the ops. Skippr will not run prefect deploy. Airflow, Dagster, and dbt are separate pages.

If the job is "Prefect we already staff," Prefect is the narrower tool. If the job is live SQL on Iceberg you run, the flow was never that.

Getting started

Install skipprd from the install guide, or pip install skippr. The three tabs write the same skippr.yml. Then discover, sync, and query.

YAML

skippr:
  workspace: analytics
  skipprd_el_storage_mode: local

pipelines:
  orders:
    data_source: data_sources.app_db
    data_sink: data_sinks.lake

data_sources:
  app_db:
    Postgres:
      host: db.internal
      user: skippr_reader
      password: ${APP_DB_PASSWORD}
      database: app
      tables: [orders, customers]
      cdc_mode: snapshot_then_cdc

data_sinks:
  lake:
    AthenaIceberg:
      warehouse: s3://my-iceberg-warehouse/
      glue_database_name: analytics
      athena_workgroup_name: primary
      athena_results_s3_bucket: my-athena-results
      region: us-east-1
    schema_sink: schema_sinks.lake_schema

schema_sinks:
  lake_schema:
    AthenaIceberg:
      warehouse: s3://my-iceberg-warehouse/
      glue_database_name: analytics
      athena_workgroup_name: primary
      athena_results_s3_bucket: my-athena-results
      region: us-east-1

CLI

skipprd --workspace analytics --storage-mode local connect data-source postgres \
  --pipeline orders \
  --name app_db \
  --host db.internal \
  --user skippr_reader \
  --password '${APP_DB_PASSWORD}' \
  --database app \
  --tables orders \
  --tables customers \
  --cdc-mode snapshot_then_cdc

skipprd connect data-sink athenaiceberg \
  --pipeline orders \
  --name lake \
  --warehouse s3://my-iceberg-warehouse/ \
  --glue-database-name analytics \
  --athena-workgroup-name primary \
  --athena-results-s3-bucket my-athena-results \
  --region us-east-1

skipprd connect schema-sink athenaiceberg \
  --pipeline orders \
  --name lake_schema

Python

from skippr import (
    Config,
    DataSinkAthenaIceberg,
    DataSourcePostgres,
    DataSourcePostgresSourceCdcMode,
    EnvRef,
    LocalStorage,
    Pipeline,
)

cfg = Config().workspace("analytics").storage(LocalStorage())

app_db = cfg.data_source(
    "app_db",
    DataSourcePostgres(
        host="db.internal",
        user="skippr_reader",
        password=EnvRef("APP_DB_PASSWORD"),
        database="app",
        tables=["orders", "customers"],
        cdc_mode=DataSourcePostgresSourceCdcMode.SNAPSHOTTHENCDC,
    ),
)

lake = cfg.data_sink(
    "lake",
    DataSinkAthenaIceberg(
        warehouse="s3://my-iceberg-warehouse/",
        glue_database_name="analytics",
        athena_workgroup_name="primary",
        athena_results_s3_bucket="my-athena-results",
        region="us-east-1",
    ),
    schema_sink="lake_schema",
)

cfg.pipeline("orders", Pipeline(data_source=app_db, data_sink=lake))
cfg.save("skippr.yml")

export APP_DB_PASSWORD='your-postgres-password'
skipprd discover --pipeline orders
skipprd sync --pipeline orders --once
skipprd query "SELECT count(*) FROM analytics.orders"

Which to choose

Choose Prefect if you want a Python orchestrator, and Iceberg or CDC as tasks you already write is enough.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not run Prefect.

Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.