New Postgres Feature Destroys the Vector Database Hype

Written by Ark Protocol - Nov 14, 2025 Original, nicely formatted version here (if you are a medium member) https://medium.com/@ArkProtocol1/the-new-postgres-feature-that-just-killed-the-vector-database-hype-cycle-556146ed5961

Your CTO just approved a $50k annual contract for Pinecone.

Two weeks later, your intern added vector search to your existing Postgres database in 47 minutes. For free.

That sound you hear is the entire vector database market collectively holding its breath.

The bubble just popped.

Remember when everyone said you needed a specialized vector database? When Pinecone raised $138 million and Weaviate hit unicorn status? When every startup pitch deck had “powered by [insert vector DB here]” on slide three?

PostgreSQL just released pgvector 0.8.0 with features that make dedicated vector databases look like expensive overkill. And the timing could not be worse for the billion-dollar vector database industry.

Here is what actually happened.

The Setup: How We Got Here When ChatGPT exploded in late 2022, enterprises discovered they needed somewhere to store embeddings for retrieval-augmented generation. Suddenly, storing and searching through millions of high-dimensional vectors became a must-have capability.

The pitch was simple: your traditional database cannot handle this. You need purpose-built infrastructure. You need us.

In April 2023 alone, Pinecone and Chroma raised massive funding rounds. The message was clear: vector databases are the future, and if you want to build AI applications, you better get on board.

But there was a problem.

Most companies already had a database. A really good one. PostgreSQL has been the workhorse of web applications for decades. It handles billions of transactions daily for companies from Instagram to Spotify.

So developers asked the obvious question: can we just add vector search to Postgres?

The vector database vendors said no. They wrote blog posts explaining why you needed specialized infrastructure. They talked about HNSW algorithms and approximate nearest neighbor search like it was rocket science.

Then pgvector came along and said: actually, yes you can.

What Changed: The New Release The 0.8.0 release added iterative index scans and smarter query planning. These are not flashy features. They are the kind of boring infrastructure improvements that completely change the game.

Here is what these features actually do:

Iterative Index Scans: When you filter vectors by metadata (show me similar products that cost less than $50), pgvector can now scan incrementally until it finds enough matches. No more returning zero results because your filters were too strict.

Better Query Planning: Postgres now knows when to use your regular indexes instead of the vector index. Sometimes a B-tree index on your filter condition is faster than scanning vectors. Postgres figures this out automatically.

Let me show you what this looks like in practice.

CREATE TABLE products (

 id SERIAL PRIMARY KEY,
 name TEXT,
 price DECIMAL,
 embedding VECTOR(1536)

);

CREATE INDEX ON products USING hnsw (embedding vector_cosine_ops); CREATE INDEX ON products (price); -- This query automatically picks the best strategy SELECT * FROM products WHERE price < 50 ORDER BY embedding <=> '[0.1, 0.2, ...]' LIMIT 10;

Postgres looks at your query, sees you have a tight price filter, and might use the price index to filter first before touching vectors. It just works.

The vector database vendors have been selling you complexity. Postgres just made it simple.

The Real Cost Comparison Let me break down what this actually costs.

Pinecone (Dedicated Vector DB)

$70 per million vectors per month Separate infrastructure to manage Data sync complexity between your main database and vector store Learning curve for yet another database pgvector (Postgres Extension)

$0 for the extension itself You are already paying for Postgres Zero data sync because everything is in one place Your team already knows SQL For applications with thousands to millions of vectors, pgvector offers excellent performance without specialized infrastructure.

I am not cherry-picking numbers here. These are real production workloads.

The Architecture That Actually Makes Sense Here is how most companies were building AI applications:

User Query

   ↓

Application Server

   ↓
   ├──> Postgres (structured data)
   └──> Pinecone (vectors)
   

Data Sync Process

   ↓

Eventual Consistency Problems Here is what it looks like with pgvector:

User Query

   ↓

Application Server

   ↓

Postgres (everything) When your data is already in PostgreSQL, introducing pgvector is a natural extension rather than adopting entirely new database technology.

No data sync. No eventual consistency bugs. No second database to monitor, backup, and scale. Just one database doing what databases are supposed to do: managing data.

When pgvector Wins You do not need a specialized vector database if:

Your vectors live next to your data — And they almost always do. Product embeddings belong with product data. Document embeddings belong with documents. You filter on metadata — Finding similar products under $50? Similar articles from last month? You need ACID transactions and proper indexes. Postgres has both. You value operational simplicity — Every additional database doubles your operational complexity. One database means one thing to scale, backup, and secure. Your scale is realistic — Most companies have millions of vectors, not billions. For moderate-scale vector operations, pgvector performs excellently without specialized infrastructure. I see teams with 10 million vectors paying for dedicated vector databases when pgvector would handle their load without breaking a sweat.

The Performance Reality But what about performance? Vector database companies love talking about benchmarks.

Here is what they do not tell you: pgvector 0.7.0 added support for half-precision vectors, indexing up to 4,000 dimensions, and binary quantization for up to 64,000 dimensions. The performance gap is closing fast.

More importantly, raw search speed is often not your bottleneck. Network latency between your app server and your vector database? That is a bottleneck. Data sync lag between Postgres and your vector store? That is a bottleneck. Query planning that does not understand your filters? That is a bottleneck.

pgvector solves these by keeping everything local.

When You Actually Need a Specialized Vector Database I am not saying vector databases are dead. There are legitimate cases where you need them:

Billions of vectors — If you have 10 billion embeddings, sure, look at Pinecone or Milvus. Pure vector workload — If 90% of your queries are vector similarity with minimal filtering, a specialized system might help. Need managed infrastructure — If you cannot run your own Postgres, managed vector databases offer value. But here is the thing: most companies do not fit these profiles. Most companies have millions of vectors, complex filters, and existing Postgres infrastructure.

For them, pgvector is not a compromise. It is the better choice.

What This Means for the Industry The market has shifted away from the notion of a vector database, because frankly who cares about the database type? What matters is whether you can build your AI application quickly and maintain it without burning money.

The vector database vendors saw this coming. Weaviate now calls itself an AI-native database, and Pinecone became a knowledge platform. They are moving up the stack, adding features beyond just storing vectors.

That is probably the smart play. Because competing with Postgres on being a database is a losing battle. Postgres has 30 years of development, millions of users, and an ecosystem that vector databases cannot match.

How to Actually Use pgvector If you are already on Postgres, here is how to start:

-- Enable the extension CREATE EXTENSION vector;

-- Add vector column to existing table ALTER TABLE articles ADD COLUMN embedding VECTOR(1536); -- Create index CREATE INDEX ON articles USING hnsw (embedding vector_cosine_ops); -- Search for similar articles SELECT title, content FROM articles ORDER BY embedding <=> $1 LIMIT 5; That is it. No new database. No data migration. No operational complexity.

The Bottom Line The vector database hype cycle is over. Not because vector databases are bad, but because for most use cases, they are unnecessary.

PostgreSQL with pgvector allows you to leverage robust features like transactions, backups, and security while adding vector capabilities. That combination is hard to beat.

Before you sign that Pinecone contract, ask yourself: do we really need another database? Or are we just following the hype?

Because chances are, you already have everything you need. It has been sitting in your infrastructure the whole time, quietly powering your applications while everyone else chased the new shiny thing.

Postgres just reminded us why boring technology wins.