After working with dozens of AI startups on their data infrastructure, we've seen the same mistakes over and over. These aren't obscure edge cases—they're patterns that trip up smart teams regularly. Here are the five most common, and how to avoid them.
Mistake #1: Storing Everything in Postgres
Postgres is great. It's reliable, well-understood, and handles most workloads well. But it's not designed for analytics.
We see startups dump millions of rows of event data into Postgres, then wonder why their dashboards take 30 seconds to load. The problem: Postgres is row-oriented, optimized for transactional queries (get me this specific user). Analytics queries (aggregate all events from last month) scan entire tables.
The fix: Use Postgres for your application data, but move analytics to a columnar database like ClickHouse or DuckDB. The same query that takes 30 seconds in Postgres often completes in under 1 second in ClickHouse.
Mistake #2: Building Pipelines Without Understanding the Questions
We've seen teams spend months building elaborate data pipelines before they know what questions they need to answer. The result: pipelines that capture the wrong data, structure it incorrectly, or miss critical context.
The fix: Start with the dashboard. What questions do you need answered? Work backwards from there. Before writing any pipeline code, mock up the visualizations you want. Then figure out what data you need to power them.
Mistake #3: Over-Normalizing Analytics Data
In application databases, normalization is good—it reduces redundancy and maintains consistency. In analytics databases, it's a performance killer.
Every JOIN in a query adds latency. If your analytics query requires joining 5 tables, you're paying that cost on every request. For dashboards that need to load in seconds, that's unacceptable.
The fix: Denormalize your analytics data. Pre-compute the joins at ingestion time. Yes, this means some data redundancy. But storage is cheap; slow dashboards are expensive (in user frustration and lost insights).
Mistake #4: Not Planning for Cost Attribution
AI costs are notoriously unpredictable. We see startups get surprised by their monthly OpenAI bill, then realize they have no way to figure out where the money went.
The fix: Build cost attribution into your data model from day one. Every LLM call should be tagged with context: which user triggered it, which agent/feature, which customer (if B2B). When your bill spikes, you need to answer "who/what caused this" immediately.
Mistake #5: Ignoring Data Until It's a Crisis
The most common pattern: a startup focuses on product, ignores data infrastructure, then suddenly needs insights (for investors, for debugging, for cost control). By then, they're playing catch-up with months of unstructured logs.
The fix: Invest in basic observability early. You don't need a sophisticated data warehouse on day one. But you do need structured logging, basic metrics, and a way to query them. The best time to start is before you need it.
Key Takeaways
- Use columnar databases (ClickHouse, DuckDB) for analytics, not Postgres
- Start with the questions you need answered, then build pipelines
- Denormalize analytics data—storage is cheap, slow queries aren't
- Build cost attribution into your data model from the start
- Basic observability early beats sophisticated data later