AWS Native, Databricks or Snowflake: Choosing the Right Data Platform

Picking a data platform is one of the most expensive decisions you’ll make. Get it right and your team moves fast, governance holds, and your AI products have something solid underneath them. Get it wrong and you’re spending years on a stack that creates more problems than it solves.

If you’re on AWS — or planning to be — this is what you need to know before committing to a platform.

The three realistic options

Not all data platforms are the same thing. AWS gives you building blocks. Databricks gives you a complete lakehouse platform that runs on top of AWS. Snowflake gives you a managed analytics engine. Understanding that distinction before choosing is the difference between a good decision and an expensive one.

AWS native: maximum flexibility, maximum engineering overhead

The AWS-native stack — S3, Glue, Athena, Lake Formation, Redshift — is powerful, and there’s no platform licence fee. But you’re building everything yourself. Data cataloguing, governance, lineage, quality checks, column-level access control — your team builds all of it from scratch, then maintains it.

That works well if you’ve got a strong, experienced data engineering team and needs that off-the-shelf tools can’t meet. It gets expensive fast when the same people building your pipelines are also responsible for the catalogue and governance layer — that’s months of work before you’ve done anything useful with the data.

Databricks: the turn-key lakehouse

Databricks is what happens when you take Delta Lake — the open table format it invented and which is now the industry standard for lakehouse architecture — and build a complete platform around it. Unity Catalog gives you cross-workspace data discovery, lineage, and access control in a single place. MLflow handles experiment tracking and model serving. Job orchestration, notebook environment, auto-scaling compute, and data quality monitoring are all included.

The reason data engineers describe it as turn-key is accurate in the ways that matter: the things that take months to build on AWS native — catalogue, lineage, governance, quality — are available on day one. You are configuring and using, not building.

Critically, Databricks runs on your AWS account. Your data stays in your S3 buckets. You are not leaving AWS; you are adding a managed platform layer on top of it. And because Delta Lake is an open format, you are not locked in to Databricks — the data is readable by Athena, Spark, and any other compatible engine.

Snowflake: SQL-first analytics

Snowflake excels at making large-scale SQL analytics accessible without deep infrastructure knowledge. Compute and storage are separated, scaling is automatic, and sharing data across organisational boundaries is genuinely straightforward.

What it does not do well is the full data platform picture. ML and AI workloads, complex transformation pipelines, streaming data, and enforcement of governance at the data layer all require additional tooling. If your primary use case is analytics and reporting for a SQL-literate team, Snowflake is excellent. If you are building a governed data platform that will power AI products as well as analytics, you will reach the limits of Snowflake quickly and find yourself bolting on tools to fill the gaps.

What the numbers actually look like

This is where the conversation surprises people. AWS native has no licence fee, which sounds like the cheaper option. The real cost is always total cost of ownership: infrastructure plus the engineering time to build and maintain what the platform does not give you.

The practical conclusion: Databricks often delivers a lower total cost of ownership than AWS native for organisations that do not already have a mature data engineering practice. The licence replaces a significant portion of engineering time rather than adding to it. You are buying built capability instead of building it yourself.

Who needs what

Choose AWS native if you have a large, experienced data engineering team; you have specific compliance or integration requirements that demand custom architecture; you are deeply invested in AWS operational tooling and want no additional vendor dependency.

Choose Databricks if you need governance, catalogue, and lineage without a long build phase; ML and AI are on your roadmap; your team is smaller and needs to deliver quickly; you want open formats (Delta Lake) without cloud lock-in at the data layer. For most organisations building a modern data platform today, Databricks on AWS is the strongest default choice.

Choose Snowflake if your primary use case is SQL analytics and BI; your team is more analyst than engineer; data sharing with external partners is a key requirement. Be clear-eyed about what you will need to add when the AI use cases arrive.

You do not need to start with a platform at all

One of the most underused options is the simplest one: start with storage only.

S3 costs around £18 per terabyte per month. Drop your data there — raw, unprocessed, exactly as it comes off the source systems — and you’ve already done the most important thing: it’s in a place you own and control. No pipelines. No schema decisions. No catalogue. Just the data, sitting there, ready when you need it.

This is the foundation of the medallion architecture Bronze layer, and it is a legitimate starting point in its own right. You can query it ad-hoc with Athena for almost nothing. You can see what you have, understand the volume and variety, and make platform decisions based on actual data rather than assumptions.

From there you build. A governance layer when you need one. Structured ingestion when the volume justifies it. A catalogue when teams start struggling to find things. An AI layer when the data is clean enough to support it. Each stage adds complexity and cost — but only when you actually need it, not upfront as a prerequisite.

Organisations that start this way almost always make better platform decisions. They know what their data actually looks like before they commit to how they’ll process it. That’s not a small advantage.

The right starting point is not a platform

Before you pick a platform, answer three questions honestly: what does your data actually look like right now, what can your team realistically build and own, and what do you need from AI and analytics in the next two years. Skip that step and you’ll be migrating everything eighteen months from now.

We work with organisations on AWS on both AWS-native builds and Databricks. The platform matters less than choosing the right one for your situation — and building it to last.