WarpStream alternative — WarpStream vs Skippr

WarpStream is diskless Kafka: Agents in your cloud, control plane in theirs (BYOC), data in your object storage. No per-agent, per-core, or per-partition fee. BYOC meters uncompressed writes, uncompressed storage, and cluster-minutes in 15-minute slices. Public write bands start at 0.01 USD per GiB (first 50 TiB/month) and fall to 0.003 USD per GiB above 7800 TiB/month. Storage starts at 0.01 USD per GiB-month and falls to 0.004. Cluster-minutes: Dev 0.0345 USD, Fundamentals 0.1725 USD, Pro 0.52083 USD per 15 minutes. Monthly base on the April 2026 grid: Dev 100 USD, Fundamentals 500 USD, Pro 1500 USD. WarpStream Tableflow is a separate BYOC product: a stateless binary in your VPC that reads any Kafka-compatible source (Apache Kafka, MSK, Confluent Cloud, Redpanda, WarpStream) and writes Iceberg in your bucket, with compaction and maintenance in the product. Catalogs listed: AWS Glue, Databricks Unity Catalog, Iceberg REST. Tableflow writes start at 0.04 USD per GiB and fall to 0.012. Storage bands match BYOC. You can buy Tableflow without WarpStream Kafka. This is not Iceberg ∪ WAL. Live consume is still Kafka protocol; Iceberg is Tableflow after the topic.

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 cluster-minute. ELT into a warehouse is one path, not the product.

Feature	Skippr	WarpStream
Deploy	Single Rust binary, your VPC or on-prem	BYOC Agents plus WarpStream control plane
Schema	In-stream evolution, sibling columns	Kafka schemas. Tableflow YAML plus catalog mapping
Storage	Open Iceberg on Parquet you own	Object-store Kafka log. Iceberg is Tableflow in your bucket
CDC	Log-based; fails closed if the dest cannot honour	A source writes Kafka. Tableflow materializes the topic
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	Kafka consumers. Iceberg after Tableflow (Athena, Trino, Spark, …)
Pricing	Free self-hosted engine	Writes + storage + cluster-minutes. Tableflow writes from 0.04 USD/GiB.

What WarpStream is good at

Kafka-protocol streaming without broker disks, data in your account, and Tableflow when any existing Kafka-compatible cluster must land Iceberg without a Spark job. That is the product. Source-agnostic Tableflow is why some teams pick it over Confluent Tableflow, which wants Confluent Cloud as the source. If the job is "this topic into this Iceberg table in our VPC," that is a real product.

Where the job is wider

WarpStream is a Kafka service plus an Iceberg materializer. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Postgres is not a Tableflow source. CDC still starts in Debezium or an app. The control plane is theirs; Agents and Parquet are yours.

GiB and cluster-minute math is the ops. Skippr will not replace a WarpStream topic that already fans out. Skippr also will not run Agents. Confluent Tableflow is a separate page.

If the job is "WarpStream Kafka we already staff," WarpStream is the narrower tool. If the job is live SQL on Iceberg you run, Tableflow 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.

YAMLCLIPython

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

bash

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")

bash

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 WarpStream if you want diskless Kafka in your account, and Tableflow on an existing Kafka-compatible source is the Iceberg job.

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

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

WarpStream alternative — WarpStream vs Skippr ​

What WarpStream is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

WarpStream alternative — WarpStream vs Skippr

What WarpStream is good at

Where the job is wider

Getting started

Which to choose