Every generation of engineers has its exotic stack. In the early 2010s it was Cassandra and Riak. In the late 2010s it was Kafka for everything and microservices for a CRUD app. Today it is vector databases, edge compute for things that should be server-rendered, and distributed systems patterns applied to problems that a single Postgres instance would solve in milliseconds. The pattern is consistent: novel technology gets adopted for reasons of fashion, then paid for in years of operational pain.
What "boring" actually means
Boring technology is not old technology. It is technology that is well-understood, well-documented, and well-staffed. It has known failure modes, mature operational tooling, and a large pool of engineers who can work with it. When something goes wrong at 2am — and it will — boring technology means you can find the answer in the docs or Stack Overflow in minutes, not wait for a vendor to respond to a support ticket.
Before adopting a new piece of infrastructure, ask: if this breaks in production at 3am, can I diagnose and fix it without the vendor? If the answer is no, you are outsourcing reliability to someone who is not on your on-call rotation.
PostgreSQL is underrated at every scale
We have built multi-tenant SaaS products serving millions of rows on a single Postgres instance with no performance issues. Postgres handles JSONB for flexible schemas, full-text search, time-series data with partitioning, geospatial queries with PostGIS, and queue-like workloads with SKIP LOCKED. Before reaching for Redis, Elasticsearch, or a purpose-built time-series database, we always ask: can Postgres do this? More often than not, the answer is yes — and the operational simplicity of one database instead of three is worth the marginal performance trade-off.
The hiring tax on exotic stacks
Every unusual technology in your stack is a hiring filter. Not the good kind. It narrows your candidate pool, increases onboarding time, and makes every future engineer's first three weeks harder. If your data pipeline requires deep knowledge of Apache Flink, you have just eliminated 95% of available engineers from being able to maintain it. Choose technology that the next person you hire can understand on day one.
When novel is the right choice
This is not an argument against ever adopting new technology. Vector databases are genuinely useful for semantic search in ways that Postgres cannot fully replicate today. Edge compute solves real latency problems for global products. The question is not "is this technology good?" but "is this technology the right trade-off for this business, at this stage, with this team?" In almost every early-stage SaaS product, the answer favours boring — and the time saved on infrastructure is better spent on the product.