DeepSeek vs Weaviate
Side-by-side comparison of DeepSeek and Weaviate.
Quick summary
DeepSeek — Chinese open-weight frontier models. DeepSeek R1 is an open-weight reasoning model competitive with OpenAI's o1, at a fraction of the price. DeepSeek V3 is a strong general-purpose LLM.
Weaviate — Open source vector database with built-in ML. Weaviate is an open-source vector database with native multi-modal search, built-in vectorizer modules, hybrid search, and cloud or self-hosted deployment.
Feature comparison
| Feature | DeepSeek | Weaviate |
|---|---|---|
| Pricing model | Paid | Freemium |
| Starting price | Pay per token (cheap) | $25/mo |
| Free tier | No | Yes |
| Open source | Yes | Yes |
| Vision | No | — |
| Streaming | Yes | — |
| Embeddings | No | — |
| Max Output | 8K | — |
| Fine-tuning | No | — |
| Context Window | 128K | — |
| Flagship Model | DeepSeek V3 | — |
| Reasoning Model | DeepSeek R1 | — |
| Function Calling | Yes | — |
| EU Data Residency | No | — |
| Type | — | Hybrid |
| Free Tier | — | 14-day sandbox |
| Serverless | — | Yes |
| Self-hosted | — | Yes |
| Multi-tenant | — | Yes |
| Hybrid Search | — | Yes |
| Max Dimensions | — | 65535 |
| Metadata Filtering | — | Yes |
DeepSeek
Chinese open-weight frontier models
Pros
- Frontier reasoning at ~5% of OpenAI prices
- Open weights — can self-host
- Very competitive benchmarks
Cons
- China-based (geopolitical/compliance concerns for some)
- No vision yet
- Smaller SDK ecosystem
Weaviate
Open source vector database with built-in ML
Pros
- Fully open source, self-hostable
- Built-in vectorizer modules
- Native multi-modal (text + image)
- GraphQL API
Cons
- Heavier ops than Pinecone if self-hosting
- Cloud tier pricing more complex
- Community smaller than Pinecone's
Which should you choose?
Choose DeepSeek if you value open source and want the option to self-host, and you need production-grade features and are ready to pay. Choose Weaviate if you value open source and want the option to self-host, and a free tier is important for your stage.