Milvus
6.3#13 in vector databasesFree planFree planAPILinuxSelf-hosted
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| Fact | Milvus | Vespa |
|---|---|---|
| Deployment | Milvus offers Lite as a Python library, Standalone as a single-machine server, and Distributed for Kubernetes clusters. | Vespa can be self-managed, run with the Vespa Kubernetes Operator, or deployed on Vespa Cloud. |
| Free plan | Yes | No |
| Integrations | The integrations documentation lists tools including LangChain, LlamaIndex, Haystack, DSPy, Dify, Langflow, OpenAI, Cohere, and HuggingFace. | Vespa Cloud Enclave can run applications in customer accounts on AWS, Azure, or Google Cloud Platform. |
| Product | Milvus is an open-source vector database built for GenAI applications. | Vespa is an AI search platform for online data-driven applications that combines retrieval, ranking, machine-learning inference, and real-time serving. |
| Search | Milvus supports approximate nearest neighbor, filtering, range, hybrid, and full-text search, plus reranking, fetch, and query operations. | Vespa supports vector and tensor search, positional text search, and search over structured data. |
| Security | Milvus documents user authentication, TLS encryption for gRPC and REST traffic, and role-based access control. | The Vespa Cloud security guide describes data-plane requests protected with mutual TLS or, optionally, tokens, and says Vespa.ai has a SOC 2 attestation. |
| Support | The Milvus site lists community channels including Slack, Discord, GitHub, and Milvus Office Hours. | The Startup plan includes community support only with no SLA; Cloud plans list unlimited support cases, with response times varying by plan. |
| What it does | Milvus is an open-source vector database developed by Zilliz for high-performance, scalable vector search. | Vespa is a data platform for storing, searching, and processing large structured and unstructured datasets with real-time indexing, filtering, recommendations, and personalization. |
Milvus suits developers and teams building vector search into GenAI applications, from local experiments to large Kubernetes deployments. Its Lite option can also suit users who need local search over private or sensitive data.
Vespa suits teams building AI applications that depend on search quality, ranking, fresh real-time data, or increasing retrieval traffic. It offers local, self-managed, and cloud deployment options.