Vespa vs Weaviate
A side-by-side comparison of Vespa and Weaviate, two Vector DB tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
Vespa
Vector DBOpen-source serving engine for vector, lexical, and structured search at scale.
View VespaAt a glance
| Attribute | Vespa | Weaviate |
|---|---|---|
| Category | Vector DB | Vector DB |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | API, CLI, Linux | API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Vespa.ai | Weaviate |
| Capabilities (differs) |
|
|
The honest brief
Vespa
Combines vector, lexical and structured search plus ML inference in one query, distributed across nodes at web scale.
- Vector + lexical + structured in one query
- Battle-tested at Yahoo scale
- Apache-2.0, fully self-hostable
- In-engine ML model inference
- Vespa Cloud Enclave runs in your VPC
- Steep learning curve
- Operationally heavy to self-host
- Overkill for simple vector search
- Smaller community than Elastic
Weaviate
Built-in vectorizer modules embed text inline — raw text in, vectors out — so you skip running a separate embedding pipeline.
- Hybrid BM25 + vector search
- Self-hostable or managed cloud
- GraphQL and REST APIs
- Resource-heavy at large scale
- Module config has a learning curve
- Managed tier costs add up
- Newer than some lexical engines
When to pick which
Both cover Vector search, RAG pipeline, and Recommendation engine.
Pick Vespa if you need Unified search.
- Unified search (primary capability)
Pick Weaviate if you need Embeddings.
- Embeddings (primary capability)
They also differ on:
- Platforms
- API, CLI, Linux · API