Migrate from SAS with
your coding agents
Alchemist MCP and Skills enable deterministic SAS-to-PySpark migration you can trust. Proven at enterprise scale on Databricks and Snowflake.
See It In Action
SAS in. PySpark out.
set sashelp.cars;
where horsepower > 300;
tax_category = 'Premium';
adjusted_msrp = msrp * 1.05;
run;
from pyspark.sql import functions as F
df = spark.table("sashelp.cars")
df = df.filter(F.col("horsepower") > 300)
df = df.withColumn("tax_category", F.lit("Premium"))
df = df.withColumn("adjusted_msrp", F.col("msrp") * 1.05)
df = df_high_power_cars.createOrReplaceTempView(
"HIGH_POWER_CARS")
The Problem
Manual migration doesn't scale
Organizations sitting on thousands of SAS programs face the same wall: too slow, too inconsistent, too dependent on specialized engineers.
Time-consuming
Manual SAS rewrites take months. Teams burn engineering cycles on repetitive translation work instead of building.
Inconsistent
Every developer translates differently. Logic drift and subtle bugs creep in across hundreds of converted programs.
Engineer-dependent
Business and analytics teams can't participate. They wait in queue while engineers handle conversions one by one.
How It Works
Your agent talks to Alchemist
Ask your coding agent to convert SAS code. It delegates to Alchemist via MCP or Skill, and returns production-ready output.
Snowflake Cortex Code
Claude Code · Any Other
Works With Your Stack
Bring your own agent
Genie
Code
Other
Deployment
Cloud or air-gapped — your call
Run conversions through Alchemist's cloud, or deploy the full stack in your own network via Docker.
Alchemist Cloud
Connect your agent to api.getalchemist.io over public internet. Zero infrastructure to manage.
api.getalchemist.io
Air-Gapped (Docker)
Deploy Alchemist in your private cloud network. Full control. No data leaves your environment.
docker load -i alchemist.tar
Ready to accelerate
your SAS migration?
Talk to our team about setting up Alchemist MCP or Skill for your organization.