vs
OpenAI vs pgvector
Side-by-side comparison of OpenAI and pgvector.
Quick summary
OpenAI — The company behind ChatGPT, GPT-4o, and o1. OpenAI provides the most widely used LLM API with GPT-4o, GPT-4o mini, o1 reasoning models, embeddings, DALL-E image generation, Whisper speech-to-text, and Assistants API.
pgvector — Vector similarity search for Postgres. pgvector is an open-source Postgres extension that adds vector similarity search to any Postgres database. Runs anywhere Postgres runs (Supabase, Neon, RDS).
Feature comparison
| Feature | OpenAI | pgvector |
|---|---|---|
| Pricing model | Paid | Free |
| Starting price | Pay per token | Free (just Postgres) |
| Free tier | No | Yes |
| Open source | No | Yes |
| Vision | Yes | — |
| Streaming | Yes | — |
| Embeddings | Yes | — |
| Max Output | 16K | — |
| Fine-tuning | Yes | — |
| Context Window | 128K | — |
| Flagship Model | GPT-4o | — |
| Reasoning Model | o1 | — |
| Function Calling | Yes | — |
| EU Data Residency | Yes | — |
| Type | — | Postgres extension |
| Free Tier | — | Unlimited |
| Serverless | — | No |
| Self-hosted | — | Yes |
| Multi-tenant | — | Yes |
| Hybrid Search | — | Yes |
| Max Dimensions | — | 16000 |
| Metadata Filtering | — | Yes |
OpenAI
The company behind ChatGPT, GPT-4o, and o1
Pros
- Most capable general-purpose LLM
- Huge ecosystem and SDK support
- Battle-tested at scale
- Best function calling reliability
Cons
- Most expensive at scale
- No free tier
- Rate limits can bite
- Vendor lock-in risk
pgvector
Vector similarity search for Postgres
Pros
- Use your existing Postgres
- No new infrastructure
- Transactional guarantees with vectors
- Free — pay only for Postgres
Cons
- Slower than purpose-built vector DBs at scale
- Index build times grow with data
- Not ideal for 100M+ vectors
Which should you choose?
Choose OpenAI if you need production-grade features and are ready to pay. Choose pgvector if you value open source and want the option to self-host, and a free tier is important for your stage.