Flyte alternative — Flyte vs Skippr

Flyte is an Apache-2.0 workflow runtime (Linux Foundation). Flyte 2 went GA on 4 Aug 2026. Latest on PyPI is 2.12.4 (2 Nov 2026). You write typed Python tasks; they run locally (pip install flyte), on a Devbox (flyte start devbox), or on Kubernetes. Iceberg is not a product destination. The SDK ships a PyIceberg example that scans plan_files() and fans parquet paths across tasks. CDC is a task you author. Union.ai (the Flyte creators) Team is 950 USD/month minimum, credited to usage; first month waived. Actions start at 0.0030 USD (first 100k/month), vCPU 0.0230 USD/hour, memory 0.0032 USD per GB-hour. Union-operated clusters are 500 USD per cluster per month, capped at 2,500 USD/month; self-managed clusters have no Union cluster fee. Enterprise is an annual commit. 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 Action meter for a sync. ELT into a warehouse is one path, not the product.

Feature	Skippr	Flyte
Deploy	Single Rust binary, your VPC or on-prem	Local / Devbox / Kubernetes, or Union control plane
Schema	In-stream evolution, sibling columns	Typed task I/O. Iceberg schema from PyIceberg
Storage	Open Iceberg on Parquet you own	Object store for artifacts. Iceberg if a task writes it
CDC	Log-based; fails closed if the dest cannot honour	A task you author. Flyte schedules it
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	After the run. UI observes the workflow
Pricing	Free self-hosted engine	Apache-2.0. Union Team from 950 USD/month.

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What Flyte is good at

Typed Python tasks on Kubernetes, with cache, retries, and a UI that shows the run without wrapping every job as a raw pod YAML. That is the product. Devbox and pip install flyte are why some ML teams start on a laptop and move the same code to a cluster. If the job is "this task onto this cluster," that is a real product.

Where the job is wider

Flyte is a workflow runtime. 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 inside a task. Postgres CDC still starts in Debezium or skipprd. Skippr will not replace Flyte UI, Union, or flyte run.

The Kubernetes cluster (or Union meters) is the ops. Skippr will not run Flyte. Airflow, Prefect, and Temporal are separate pages.

If the job is "Flyte we already staff," Flyte is the narrower tool. If the job is live SQL on Iceberg you run, the workflow 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 Flyte if you want a Kubernetes workflow runtime, 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 Flyte.

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