Where Does Your Data Live?

If you’re a startup looking to create your own model, or are new to AI and ML, have you given any thought to where your data lives?

There are many players in the data business but to be honest, if you’re at all serious about implementing a AI/LLM solution and are not tied to Salesforce’s ecosystem, you’re going to be looking at one of the Big Three.

The “Big Three” when we talk about data warehousing right now are Databricks, Amazon’s Bedrock and Snowflake. Let’s take a look at the benefits and limitations of each platform and what I have seen be the best approach to managing your datasets

Amazon Bedrock:

• Primarily focused on foundation model deployment and integration

• Strong integration with AWS ecosystem (S3, SageMaker, etc.)

• Less comprehensive for full ML lifecycle management

• Better suited for companies already invested in AWS

• More focused on model serving than data preparation

Databricks:

• More comprehensive end-to-end ML platform (Lakehouse architecture)

• Strong support for both traditional ML and LLMs through MLflow and Databricks AI

• Excellent for data processing and feature engineering

• Deep integration with Apache Spark for distributed computing

• Native support for collaborative notebooks and experiment tracking

• Strong version control and reproducibility features

• Recently launched Databricks AI Research Platform specifically for LLM development

Snowflake:

• Primarily a data warehouse that has expanded into ML capabilities

• Strong data governance and security features

• Native support for Python and SQL-based ML workflows

• Snowpark for ML development in multiple languages

• Less mature ML tools compared to Databricks

• Better suited for organizations that prioritize data governance

• Recently added support for LLM development through Snowflake Cortex

So what are the key differentiators between these solutions?

Databricks is what most dedicated ML teams doing sophisticated development prefer to use. If your data is really sensitive such as medical data, Snowflake is superior for teams that need tight data governance and security. Bedrock is best for teams focusing specifically on foundation model deployment.

When it comes to how your data infrastructure will be constructed, Databricks provides better support for unstructured data and complex ETL, Snowflake excels at structured data management and SQL-based workflows and Bedrock relies on other AWS services for data management, so it adds a layer of overall complexity to solutions based there.

For cost structure, Databricks can be more expensive but offers more comprehensive features, Snowflake typically has more predictable pricing based on storage and compute and Bedrock’s pricing is based on API calls and compute time.

While personally, I think DataBricks is probably going to be the winner of most RFPs, I believe hybrid approach using all three platforms could be designed to leverage each product's strengths.