Twilio Segment alternative — Twilio Segment vs Skippr
Twilio Segment is a customer data pipeline (Connections) with Unify and Engage on the CDP SKU. Sources collect events. Destinations and warehouses receive them. Reverse ETL queries the warehouse, diffs in a Segment-managed schema (for example _segment_reverse_etl; they need read and write), and loads SaaS dests. Extract does not count as MTUs; sending those rows through the Segment Connections destination does. Usage is also counted as processed reverse-ETL records per dest. Iceberg is not a Segment writer. Connections pricing (Oct 2026): Free is 1,000 visitors/month, 500,000 reverse-ETL records/month, 2 sources, 1 warehouse, 1 warehouse sync per day. Team starts at 120 USD/month for 10,000 MTUs, unlimited sources, 1 warehouse, 2 syncs per day; extra MTUs 12 / 11 / 10 USD per thousand in the 10k-25k / 25k-100k / 100k+ bands. Business is a quote. Sync floor 15 minutes; a single extract caps at 150 million records then continues on the next run. 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. Skippr will not be analytics.js. ELT into a warehouse is one path, not the product.
| Feature | Skippr | Twilio Segment |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | Twilio Cloud |
| Schema | In-stream evolution, sibling columns | Tracking plan / Protocols. Warehouse SQL for reverse ETL |
| Storage | Open Iceberg on Parquet you own | Warehouse dest. Reverse ETL writes the SaaS dest |
| CDC | Log-based; fails closed if the dest cannot honour | Event stream, or warehouse reverse ETL. Not Postgres WAL |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | The warehouse. Segment is collect / activate |
| Pricing | Free self-hosted engine | Free 1,000 MTU. Team from 120 USD/month (10,000 MTU). |
What Twilio Segment is good at
One track / identify into many destinations and a warehouse, with Protocols to drop bad events, and reverse ETL when the model already lives in Snowflake. That is the product. 700-plus integrations on Connections is why some estates never wrote the Braze client. If the job is "this event onto this CDP," that is a real product.
Where the job is wider
Segment is a CDP. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Reverse ETL writes _segment_reverse_etl in your warehouse and bills MTUs when it re-enters Connections. Postgres CDC still starts before the SDK. Skippr will not replace analytics.track.
MTUs, throughput per MTU, warehouse syncs, and warehouse query cost are the ops. Skippr will not run Segment. RudderStack, Census, and Hightouch are separate pages.
If the job is "Segment we already staff," Segment is the narrower tool. If the job is live SQL on Iceberg you run, the CDP 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.
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: ${APP_DB_PASSWORD}
database: app
tables: [orders, customers]
cdc_mode: snapshot_then_cdc
data_sinks:
lake:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1
schema_sink: schema_sinks.lake_schema
schema_sinks:
lake_schema:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1skipprd --workspace analytics --storage-mode local connect data-source postgres \
--pipeline orders \
--name app_db \
--host db.internal \
--user skippr_reader \
--password '${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_schemafrom 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 Segment if you want an MTU-billed CDP, and Connections reverse ETL on that same bill is enough.
Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not run Segment.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.