Portable alternative — Portable vs Skippr

Portable is managed ELT for the long tail. 1,500+ prebuilt connectors, plus an in-house team that will research, build, and keep a missing source. You pay a fixed monthly fee for enabled flows. A flow is one source-destination pair, for example Stripe to Snowflake. Standard is $1,800 a month for six flows, hourly sync, warehouse destinations. Pro is $2,800 for 15 flows and a 15-minute schedule. Advanced is $4,200 for 25. Enterprise starts at $7,000. Volume is unlimited. Extract and load only. Transforms live in the warehouse (dbt or SQL). No reverse ETL. No log-based CDC product.

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 $1,800 flow floor. ELT into a warehouse is one path, not the product.

Feature	Skippr	Portable
Deploy	Single Rust binary, your VPC or on-prem	SaaS only
Schema	In-stream evolution, sibling columns	Connector-owned mapping. Custom fields via their integration services
Storage	Open Iceberg on Parquet you own	Warehouse or database on Standard. Lakes and files from Pro
CDC	Log-based; fails closed if the dest cannot honour	Incremental API sync. Not a log-based CDC engine
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Destination warehouse after the load
Pricing	Free self-hosted engine	$1,800 / $2,800 / $4,200 by flow count. Enterprise from $7,000. See their pricing.

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What Portable is good at

The catalog is the product. Vertical SaaS, HR, legal, billing tools that Fivetran and Airbyte do not ship. Teams say a new connector arrives in days, and the people who built it sit in Slack. Fixed fee, no MAR. If your job is "this one awkward SaaS into Snowflake, and I will keep Fivetran for the rest," Portable is built for that. Pro includes the integration desk. Standard charges $2,000 extra a month for the same desk after the first three months.

Where the job is wider

$1,800 is six flows. Fan-out counts. Stripe to Snowflake and Stripe to BigQuery is two. Fastest published schedule is 15 minutes on Pro. There is no transform canvas. There is no reverse ETL. There is no log CDC. Reviewers who like Portable still treat it as a complement, not the whole stack.

Portable is cloud-only. There is no binary in your VPC. Query waits for the warehouse. There is no Iceberg ∪ WAL. Skippr will not give you 1,500 long-tail SaaS connectors or a connector desk that ships in days. If the source you need is not in skipprd yet, say so and use Portable for that extract. If the job is live SQL on Iceberg you run, Portable 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 Portable if you need a long-tail SaaS connector they will build and keep, and a fixed flow fee is easier than MAR.

Choose Skippr if you want one binary, no flow floor, live SQL, and Iceberg files that stay yours. Keep Portable beside it when skipprd does not have that source yet.

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