For most new products, the right database is Postgres. It handles relational and document data, has exceptional tooling, runs on every major cloud, and the ecosystem around it in 2026 is extraordinary. If you have a genuine reason to reach for something else, this guide will help you figure that out. If you don't, it will save you a few hours of agonising and get you back to building.
Why Database Decisions Go Wrong Early On
The most common mistake I see founders make isn't choosing the wrong database. It's spending three days reading comparison articles and coming away more confused, or worse, picking something exotic because it felt more serious. Neither outcome serves you. The database decision matters, but it matters far less than shipping something real and learning from users. The goal is to make a good enough call quickly, based on actual constraints, not theoretical ones.
The second mistake is optimising for the scale you hope to reach rather than the scale you're at. Most products never hit the load that would expose a database bottleneck. The ones that do will have far more engineering capacity by then to address it. Choosing a distributed database for a product with twelve users is not forward-thinking. It's a tax on every day of development until you do have twelve users.
The Decision Framework I Actually Use
Before reaching for any comparison matrix, I ask four questions in order. The first answer that creates a hard constraint stops the process.
- Do I have genuinely unstructured or highly variable data that would require constant schema migrations? If yes, a document store like MongoDB is worth considering. If the answer is 'maybe', it's almost always no.
- Am I building something where relationships between entities are the core of the product (billing, permissions, multi-tenant SaaS)? If yes, a relational database is not optional. Postgres.
- Do I need full-text search as a primary feature, not a nice-to-have? Postgres handles this adequately for most products now. If search is the entire product, look at a dedicated search layer like Typesense or OpenSearch alongside your primary store.
- Is my team already productive in a specific ecosystem? A small team that knows MongoDB deeply will outship a team learning Postgres from scratch. Don't discount this. Familiarity compounds over months.
If none of those four questions produce a strong signal, pick Postgres. You can always add a specialist store later. You cannot easily undo a poor foundational choice six months in when the codebase depends on it.
Postgres vs MongoDB for Startups: An Honest Comparison
I've shipped products on both. Here is what actually differs in practice, stripped of the benchmarking theatre you'll find on most comparison posts.
| Factor | Postgres | MongoDB |
|---|---|---|
| Data shape | Structured, relational; JSONB column handles semi-structured data well | Document-oriented; great when each record genuinely varies in shape |
| Schema discipline | Enforced; migrations required for changes | Flexible by default; a gift early on, a liability later without discipline |
| Query power | SQL is battle-tested and extremely expressive for relational queries | Aggregation pipeline is powerful but steeper to learn |
| Tooling in 2026 | Exceptional: Supabase, Neon, Railway, pgvector, PostgREST | Good: Atlas, Mongoose, but less native vector/AI tooling maturity |
| Hosting cost on small teams | Very cheap; Neon's free tier and Supabase's free tier make this nearly zero to start | Atlas free tier is fine; costs rise faster at mid scale |
| Transactions | ACID transactions are first-class and reliable | Multi-document transactions exist but are a later addition and add complexity |
| Vector/embedding support | pgvector makes Postgres a solid single-store option for AI-adjacent features | Atlas Vector Search exists but is less integrated with the wider OSS ecosystem |
The honest read: MongoDB made a lot of sense between roughly 2012 and 2018 when schema flexibility was a genuine unlock and Postgres's JSON support was immature. That gap has largely closed. Postgres with a JSONB column gives you most of MongoDB's flexibility with none of the relational trade-offs. The main reason to choose MongoDB today is if your team already knows it well, or if your data is so genuinely document-shaped that modelling it relationally would be actively painful.
When to Reach for Something Else Entirely
There are cases where neither Postgres nor MongoDB is the right primary store. These are rarer than the internet would have you believe, but they're real.
- Time-series data at volume: If you're ingesting sensor readings, financial ticks, or event streams at high frequency, a time-series database like TimescaleDB (which is Postgres-based) or InfluxDB is worth the extra setup.
- Graph relationships as the core product: Social networks, recommendation engines, or anything where traversing connections is the primary query pattern. Neo4j or a similar graph store becomes a real option here, though most founders overestimate how graph-shaped their data actually is.
- Caching and session storage: Redis or Valkey (the open-source Redis fork) alongside your primary database, not instead of it. This isn't an either/or.
- Analytics on large datasets: If you need to run complex aggregations over millions of rows for a reporting feature, a columnar store like DuckDB (serverless, runs locally or in the browser) or BigQuery is worth separating from your operational database.
The most expensive database mistake I've seen on small products is running analytics queries against the operational database in production. It doesn't matter what database you chose. Heavy reads and transactional writes on the same instance under load is a problem waiting to happen. Separate them early, even if it's just a read replica.
A Practical Decision Path for SaaS Founders
If you're building a SaaS product and you want a concrete starting point rather than another set of trade-offs to weigh, here is the path I'd follow in 2026.
- Start with Postgres on Supabase or Neon. Both have generous free tiers, sensible scaling paths, and Supabase gives you auth, storage and a real-time layer on top, which removes several other decisions at once for early-stage products.
- Use a migration tool from day one. Prisma, Drizzle, or plain SQL migrations via a tool like Flyway. Do not skip this step. Schema changes without version-controlled migrations become painful fast, and painful quickly on a small team with no dedicated DBA.
- Don't add Redis until you have a specific problem it solves. Session storage in the database is fine to start. Caching in the database is fine to start. Add Redis when you can name the exact bottleneck it fixes.
- Add pgvector if you're building AI-adjacent features. Semantic search, embeddings, recommendation by similarity: pgvector handles all of this inside your existing Postgres instance. You don't need a separate vector database for most use cases at early scale.
- Plan your read/write split before you need it, not after. Most managed Postgres providers make adding a read replica straightforward. Know where that switch is before you're under pressure to pull it.
The Deeper Point Most Database Guides Miss
Database choice is a bet on your team's operational fluency as much as it is a technical decision. A database your team understands deeply will outperform a theoretically superior one your team is still learning to operate. Operational incidents happen. Migrations go wrong. Query performance degrades in unexpected ways. When those moments arrive, you want a database technology your team can diagnose and fix quickly, not one where you're Googling the error message while customers are waiting.
The other thing most guides skip: your database choice affects your hiring pool. In the UK, there are considerably more engineers who are fluent in Postgres than engineers who are fluent in, say, Cassandra or DynamoDB. If you're planning to grow a team in the next twelve months, that matters. Niche technology choices narrow your candidate pool and raise the cost of each hire. Sometimes that cost is worth paying. Usually it isn't, especially before you've shipped a first version.
If you're evaluating database options as part of a broader architecture decision for a new product and want a second opinion from someone who has made these calls on real shipped products, BedrockTeam does early-stage technical advisory work. No obligation to read further into it, but it's there if it's useful.
Is Postgres really good enough for most SaaS products in 2026?
Yes, comfortably. Postgres handles relational data, semi-structured JSON, full-text search, vector embeddings via pgvector, and time-series data via TimescaleDB. The managed hosting options available now (Supabase, Neon, Railway) make it trivial to operate. The vast majority of SaaS products will never encounter a problem that Postgres can't handle, and the ones that do will have the engineering capacity to address it by the time they get there.
When does MongoDB make sense over Postgres for a startup?
MongoDB is a reasonable choice if your team already has strong operational experience with it, or if your data is genuinely document-shaped in a way that makes relational modelling actively painful. Content management systems with highly variable document structures are a realistic example. For most SaaS products with entities that relate to each other (users, organisations, subscriptions, permissions), a relational model is a better fit and Postgres handles it well.
Do I need a separate vector database if I'm building AI features?
Probably not at early scale. The pgvector extension for Postgres handles vector similarity search well for most use cases. A dedicated vector database like Pinecone or Weaviate makes sense when you're indexing very large numbers of embeddings and vector search is a core, high-frequency operation. If you're adding semantic search or RAG to a product as a feature, pgvector inside your existing Postgres instance is the lower-complexity starting point.
What's the biggest database mistake founders make on small teams?
Not using migrations from the start. It sounds unglamorous, but the absence of version-controlled schema migrations is the single thing I've seen cause the most avoidable pain on small products. The second biggest mistake is running analytics workloads directly against the production database, which causes performance degradation that looks like an application bug and takes time to diagnose under pressure.
Does the database choice affect hiring in the UK?
Meaningfully, yes. Postgres and MySQL expertise is widespread in the UK engineering market. Fluency in more niche databases (Cassandra, DynamoDB, CockroachDB) is rarer and commands a premium. For a team that plans to hire in the next year, choosing a mainstream database technology keeps your candidate pool broader and your onboarding lighter. This isn't a reason to avoid specialist tools when they're genuinely the right fit, but it's a real cost that's worth factoring in.