Prompt

I'm building a startup prototype with embeddings, what is simplest to run?

Technology / Databases1 observationLast seen Oct 7, 2026

Latest observation

Oct 7, 2026OpenAI APIWeb search: off

If you want the simplest thing to run for a startup prototype, the answer is usually:

Best default: a hosted embedding API

Use a managed embedding model from:

  • OpenAI
  • Cohere
  • Voyage
  • Anthropic? (less common for embeddings)
  • AWS / Azure / GCP model endpoints if you’re already on one cloud

Why this is simplest

  • No GPU setup
  • No model deployment
  • No scaling headaches
  • Fast to integrate
  • Good quality out of the box

What it looks like

Typical flow:

  1. Send text to embedding API
  2. Store vectors in a vector DB
  3. Query with a new embedding
  4. Retrieve nearest neighbors

Good vector stores for a prototype

  • Postgres + pgvector — simplest if you already use Postgres
  • Supabase pgvector — very easy startup-friendly option
  • Pinecone — managed and straightforward
  • Qdrant Cloud — easy and solid
  • Weaviate Cloud — good but a bit heavier
  • Chroma — very easy locally for demos, less ideal long-term

Simplest stack overall

If you want minimal moving parts:

Option A: very simple MVP

  • OpenAI embeddings
  • Postgres + pgvector
  • Your app server

This is probably the best “just get it working” stack.

Option B: fastest demo

  • OpenAI embeddings
  • Chroma locally
  • Small app

Great for a prototype, but you may outgrow it.

Option C: managed end-to-end

  • OpenAI embeddings
  • Pinecone
  • App server

Very low ops burden.

If you want to run embeddings locally

Use a small open-source model like:

  • bge-small-en
  • e5-small
  • all-MiniLM-L6-v2

Run them with:

  • sentence-transformers
  • or Ollama for some models

Local pros

  • Cheap
  • Privacy
  • No API dependency

Local cons

  • More setup
  • Quality may be lower than top hosted models
  • Need to manage compute

My recommendation

For a startup prototype, I’d choose:

OpenAI embeddings + pgvector (Postgres)

or if you want the least ops possible:

OpenAI embeddings + Pinecone

If you want, I can also give you:

  1. a recommended architecture
  2. a minimal code example
  3. a cost comparison
  4. a decision tree based on your app type

Observation history

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