I'm about to say something that might get me kicked out of a few Silicon Valley country clubs. Stop obsessing over your infrastructure. For real. I've seen way too many brilliant founders torch their seed funding trying to build the "perfect" scalable backend before they even have one person paying them.
Back when we were building RemoteTeam, we burned weeks arguing about Postgres vs. MySQL, setting up replication, and stressing about sharding. It was a total waste of time. Time we should have spent talking to users and, you know, actually building the product. We eventually sold that company to Gusto, but the lesson was burned into my brain: your database is not your product.
Now, as an angel investor in over 200 companies—some you might have heard of, like Anthropic, OpenAI, and Scale AI—I see the same mistake on repeat. Founders are so scared of hitting a scaling wall that they over-engineer everything from the get-go. This is especially true with AI, where the workloads are just plain weird and unpredictable. The fear is palpable.
This is where serverless databases come in, and no, it's not just another line on your buzzword bingo card. It's a fundamental shift in how you think about your data. It’s about paying for what you use, scaling on a dime, and most importantly, letting your engineers get back to building your actual AI app.
Why Serverless is a Complete No-Brainer for AI Startups
AI apps are a different beast. They aren't your typical SaaS application. The data patterns are erratic. One day you’re running a monster batch job to train a model, the next you’re getting slammed with a tsunami of inference requests. Trying to provision servers for that is a special kind of hell.
I remember one of my portfolio companies, a vertical SaaS startup in the legal space. They were using AI to analyze contracts and were running their own Elasticsearch cluster. A new customer uploaded a mountain of documents, and their whole cluster just tipped over. It took them two full days to bring it back online. Two days of angry customer emails and zero revenue, all because they were playing sysadmin with their own database.
With a serverless database, that whole drama would have been a non-event. The database would have just scaled to handle the load, and they would have paid a bit more for that usage. No downtime, no frantic 2 a.m. calls to AWS support, no emergency board meetings.
It’s not just about uptime; it’s about speed. Your engineers should be tweaking prompts and fine-tuning models, not patching database servers. When you’re a five-person team, every single hour is precious. A serverless database is like having a world-class SRE team on payroll, for a tiny fraction of the cost.
My Personal Serverless Stack for AI
I get this question constantly: “Sahin, what’s the magic database for my AI app?” The real answer is, it depends. But I’ve seen a few clear winners emerge from the trenches.
1. Neon: Postgres Without the Headaches
I’m a huge fan of Postgres. It’s solid, it’s versatile, and the community is massive. But let’s be honest, managing it at scale can be a real chore. Neon fixes this. It’s a completely serverless Postgres that decouples storage from compute. This is huge. It means you can scale your compute down to zero when you’re not using it and only pay for the bytes you store.
Why it’s great for AI: Most AI apps still need a good old-fashioned relational database for user accounts, metadata, and all that structured stuff. Neon gives you all the power of Postgres without the operational nightmare. Plus, with extensions like pgvector, you can use it for vector search, which is the backbone of most Retrieval-Augmented Generation (RAG) apps today.
Real-world proof: A ton of the new AI-powered SaaS companies I’m investing in are building on Neon. It’s just a solid, reliable default choice for your main database.
The fine print: It’s still Postgres. If you’re building the next TikTok and are staring down the barrel of petabyte-scale data, you might need something more exotic.
2. PlanetScale: The MySQL That Never Sleeps
PlanetScale is built on Vitess, the same tech that keeps YouTube’s database from melting down. It’s a MySQL-compatible database that offers mind-bending horizontal scaling. We’re talking about sharding your database across thousands of servers without even thinking about it.
Why it’s great for AI: If you’re building a consumer AI app that you hope will go viral, PlanetScale is your safety net. It’s built for insane growth. It’s also fantastic for apps that need to be always-on, thanks to its built-in replication and failover.
Real-world proof: I’ve seen PlanetScale powering everything from AI-driven social networks to e-commerce sites with hyper-personalized recommendations.
The fine print: The branching and merging workflow for schema changes is powerful, but it can feel a little foreign if your team is used to the old way of doing database migrations.
3. Momento: The Blazing-Fast Serverless Cache
Momento is my secret weapon. It’s a serverless cache built for one thing: speed. We’re talking single-digit millisecond latency. It’s fully managed, so you set it and forget it.
Why it’s great for AI: So many AI features need a screaming-fast cache. Think session state, real-time leaderboards, or feature stores. Momento is purpose-built for this. It’s also a game-changer for caching the results of expensive model inferences.
Real-world proof: I advised a startup building an AI code completion tool to use Momento. They cut their inference latency by more than half just by caching the most frequent code completions. That’s a massive improvement in user experience.
The fine print: It’s a cache, not a permanent database. You still need a primary database like Neon or PlanetScale for the data you can’t afford to lose.
How to Choose Your Weapon
So, how do you make the call? Ask yourself these questions:
- What does your data look like? Is it structured, unstructured, or a mix? This is the first and most important question.
- What’s your real scale? Are you building a weekend project or the next Facebook? Be brutally honest with yourself here.
- What does your team know? If your team lives and breathes Postgres, Neon is a natural fit. If they’re MySQL wizards, check out PlanetScale.
- What’s your burn rate? Serverless is cheap, but it’s not free. Understand the pricing model before you swipe your credit card.
Stop Tinkering and Start Shipping
The best database is the one that gets you to market the fastest. Period. Don’t get lost in the weeds of technical debates. Pick a serverless option that makes sense for you right now, and get back to building something people will pay for.
I’ve made the mistake of gold-plating my infrastructure, and I’ve watched dozens of founders make the same costly error. Don’t be one of them. Your only goal right now is to find product-market fit. The scaling challenges are a good problem to have. You can deal with them when you have revenue and a team to help.
Now stop reading and go build something incredible.
Frequently Asked Questions
What if I disagree with some of the advice?
Good. That means you're thinking critically, which is exactly what a good founder should do. Take what resonates, test it, and discard what doesn't work for your specific situation. No advice is universal.
How often is this guide updated?
I revisit and update my guides regularly as I learn new things and as the market evolves. The core principles tend to stay stable, but specific tactics and tools get refreshed based on what's working right now.
How should I work through this guide?
Don't try to absorb everything in one sitting. Read through once to get the big picture, then go back and work through each section as it becomes relevant to your current challenges. Bookmark it and return to it regularly.
Is this guide based on real experience?
Every recommendation in this guide comes from direct experience, either from building and selling my own companies, or from patterns I've observed across 200+ angel investments. I don't write about things I haven't personally tested.