Tray alternative — Tray.ai vs Skippr

Tray.ai is workflow iPaaS. You draw a workflow. A Task is one operation on a piece of data: the trigger, each connector or service step, and each pass through a loop or boolean. A connector step that makes several API calls is still one Task. Retries are free. Plans are Pro (3 workspaces, 7-day insights), Team (20 workspaces), and Enterprise (unlimited workspaces, 180-day insights, SSO, HIPAA, regional hosting, log streaming, and a data-engineering add). Helix Builder Edition is the public floor: 50 USD a month. iPaaS is a plan plus Tasks (and Helix Compute Units when you run Helix). There is no per-user or per-connection fee. A 30-day trial includes 1 million Tasks. Merlin Agent Builder is an add-on; agent actions still burn Tasks. AI text connectors convert tokens to Tasks; Merlin IDP bills per page. There is no Iceberg destination. CDC is not a log tail. This is not a Streamhouse. There is no Iceberg ∪ WAL.

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 Task. ELT into a warehouse is one path, not the product.

Feature	Skippr	Tray.ai
Deploy	Single Rust binary, your VPC or on-prem	Tray cloud. Advanced on-prem is Enterprise
Schema	In-stream evolution, sibling columns	Workflow mapping
Storage	Open Iceberg on Parquet you own	Target you wired. No Iceberg dest
CDC	Log-based; fails closed if the dest cannot honour	App triggers and polls. Not a log tail
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Downstream after the workflow
Pricing	Free self-hosted engine	Plan plus Tasks. Helix Builder from 50 USD/month. Merlin extra.

Talk through skipprd now

Paul Bot knows the docs. Google Meet as soon as you join, any time of day.

Chat now →

Any time · 20 minutes · Google Meet now

What Tray.ai is good at

A workflow that already fans a Salesforce event into Slack, a ticket, and a warehouse load. That is the product. Task math that ignores user seats is why some teams compare it to Boomi Messages. Merlin is the agent SKU on the same canvas. If the job is "SaaS workflow we already staff," that is a real product.

Where the job is wider

Tray is an automation canvas. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. The workflow ends; then you query the next system.

Task math is the ops. A loop over 100 records is 100 Tasks plus the other steps. Token-to-Task conversion for Merlin text sits on the same entitlement. Overages true-up; Tray does not stop the account.

Skippr will not run a Tray workflow. Skippr also will not match the Merlin agent catalog. If the job is "iPaaS we already staff," Tray is the narrower tool. If the job is live SQL on Iceberg you run, Tray 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 Tray if you want workflows from app events into SaaS and warehouses, and the next system after the workflow is enough.

Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not run a Tray workflow.

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