Upstash Vector vs Weaviate
Upstash Vector
upstash.comWeaviate
weaviate.ioSide by side
| Figure | Upstash Vector | Weaviate |
|---|---|---|
| Price from | $0.40/mo | $45/mo |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 4 |
| Spec score · Vector Databases | 7.9 (#13) | 8.0 (#10) |
| Integrations | The documentation provides integrations for Vercel AI SDK, Flowise, LangChain, Langflow, LlamaIndex, and LlamaParse. | The integration ecosystem lists cloud and model integrations including AWS, Google Cloud, Azure, OpenAI, Anthropic, Cohere, Hugging Face, and Ollama. |
| Support | Enterprise features include professional support with a response-time SLA, a dedicated Slack channel, and a dedicated account manager. | Free includes Basic Support; Flex lists Standard Support with next-business-day Sev 1 response, while Premium lists enterprise support as fast as 1-hour Sev 1. |
Plans
Upstash Vector
- FreeFree
- Pay as You Go$0.40 a month
- Fixed$60 a month
- EnterprisePriced on request
In their own words
Upstash Vector
Upstash Vector is a serverless cloud vector database for applications that need vector search, including NLP, RAG, recommendations, image recognition, clustering and classification. It uses DiskANN for approximate nearest-neighbor queries and also lists FreshDiskANN as an index type. Developers can use its REST API and SDKs, with first-class Python and TypeScript support and additional SDKs for Go and PHP. Similarity options include Euclidean distance, cosine similarity and dot product. Vectors can carry metadata, and query filters can restrict matches using metadata expressions. Hybrid indexes combine dense semantic search with sparse exact-token search and fuse the results. Documented integrations include Vercel AI SDK, Flowise, LangChain, Langflow, LlamaIndex and LlamaParse. The pricing page lists a default top-k limit of 1,000, up to 48 KB of metadata and 1 MB of data per vector. The Free plan allows 10K daily queries or updates and up to 1 GB of data and metadata. Pay as You Go costs $0.40 a month, billed per 100K requests, with separate storage and bandwidth terms. Region availability varies by plan.
Weaviate
Weaviate is an open-source AI vector database for storing, indexing and searching high-dimensional vectors, used as a foundation for search, retrieval-augmented generation (RAG) and agents. Its hybrid search combines vector retrieval with BM25 keyword search, alongside semantic and keyword search. Other listed capabilities include advanced filtering, vectorizer modules, configurable backups, native multi-tenancy, vector-index compression and tenant isolation. It can run as a hosted service or as a self-managed instance locally or with a cloud provider, with Docker and Kubernetes deployment options. Its integrations include AWS, Google Cloud, Azure, OpenAI, Anthropic, Cohere, Hugging Face and Ollama. Weaviate Cloud encrypts data in transit and at rest, supports role-based access controls and provides automated daily backups. The Free plan includes up to 100,000 objects, one collection and three tenants, but no backups or high-availability replication. Flex costs $45 a month; Premium costs $400 a month. Windows use is possible through containers such as Docker or WSL, but native Windows support is not offered. SDKs are listed for Python, TypeScript/JavaScript, Go, Java and C#.