Serverless vector database with pay-per-request pricing, REST API for edge runtimes, and built-in embedding generation. Free tier includes 10K queries/day.
A serverless vector database that charges per request with no idle costs. Works from edge runtimes like Cloudflare Workers. Free tier includes 10K queries per day.
Upstash Vector is a serverless vector database built for developers who deploy on edge runtimes and serverless platforms. Its defining feature: a stateless REST API that works everywhere, including Cloudflare Workers, Vercel Edge Functions, and Deno Deploy, where traditional database drivers with persistent TCP connections cannot run.
The pricing follows Upstash's pay-per-request model. The free tier gives you 10,000 queries per day and stores up to 10,000 vectors, enough for prototyping and small RAG applications. Beyond that, the pay-as-you-go plan charges $0.40 per 100K requests. Fixed plans start at $60/month for higher throughput and dedicated resources. You pay for what you use, with no idle costs.
A useful shortcut for simpler RAG setups: Upstash Vector can generate embeddings for you. Send raw text instead of pre-computed vectors, and it handles the embedding using models like BGE or multilingual E5. This eliminates the need to manage a separate embedding service for straightforward applications.
Namespace-based isolation supports multi-tenant scenarios. Metadata filtering handles equality, range, and set membership operators for hybrid search patterns. The index supports configurable distance metrics (cosine, euclidean, dot product) and dimension sizes up to 3072.
The integration story leans into the AI framework ecosystem. LangChain and LlamaIndex connectors exist, and the @upstash/rag-chat package bundles vector search, LLM calls, and conversation history into one API. SDKs cover Python, TypeScript, and Go.
Where it falls short compared to dedicated vector databases: query latencies typically run 10-50ms (slower than in-memory solutions like Pinecone), maximum index sizes are smaller than distributed systems like Milvus, and there is no self-hosting option. Advanced features like GPU-accelerated search, multi-vector indexing, or sophisticated reranking are absent.
The trade-off is clear. If you are building on serverless infrastructure and need a vector store that matches your deployment model, Upstash Vector eliminates operational complexity. If you need sub-millisecond latency or billion-scale indexes, look at Pinecone, Qdrant, or Weaviate instead.
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Upstash Vector solves a specific problem well: vector search for serverless and edge deployments where traditional databases cannot run. The REST API, pay-per-request pricing, and built-in embedding generation make it the easiest vector database to adopt for small to mid-size RAG applications. It cannot compete with Pinecone or Qdrant on raw performance or scale, but for teams already using Upstash Redis on edge platforms, it is the natural choice.
Stateless HTTP-based API that requires no persistent connections or native drivers. Works from any environment that can make HTTP requests, including edge runtimes where TCP-based database clients fail.
Use Case:
A Cloudflare Worker serving a RAG chatbot queries Upstash Vector on every user message without needing connection pools or WebSocket workarounds.
Send raw text instead of pre-computed vectors. Upstash generates embeddings server-side using models like BGE-base or multilingual E5, removing the need for a separate embedding pipeline.
Use Case:
A small development team building a docs search tool skips setting up an OpenAI embedding endpoint and lets Upstash handle text-to-vector conversion directly.
Attach JSON metadata to vectors and filter search results using equality, range, IN, and NOT IN operators. Combine semantic similarity with structured attribute filters in a single query.
Use Case:
An e-commerce recommendation engine searches for semantically similar products while filtering by price range, category, and availability status.
Isolate vectors into namespaces within a single index. Each namespace operates independently for queries and upserts, enabling tenant separation without provisioning separate indexes.
Use Case:
A SaaS platform stores each customer's document embeddings in separate namespaces, ensuring data isolation while sharing one Upstash Vector index.
No minimum fees, no idle costs. Free tier covers 10K queries/day and 10K vectors. Pay-as-you-go charges $0.40 per 100K requests. A price cap guarantees you never exceed the fixed plan cost.
Use Case:
An AI agent that handles sporadic queries pays near-zero during quiet periods and scales costs linearly during burst activity without capacity planning.
Native connectors for LangChain, LlamaIndex, and Vercel AI SDK. The @upstash/rag-chat package combines vector search, LLM calls, and conversation history into a single high-level API.
Use Case:
A developer builds a conversational RAG agent using LangChain with Upstash Vector as the retriever, adding persistent chat history through rag-chat in under 50 lines of code.
$0/month
$0.40 per 100K requests
From $60/month
Custom
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Upstash Vector added built-in embedding generation supporting BGE and multilingual E5 models, expanded metadata filtering operators, and introduced namespace support for multi-tenant isolation within a single index.
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