KDB.AI vs Pinecone
KDB.AI
kdb.aiPinecone
pinecone.ioSide by side
| Figure | KDB.AI | Pinecone |
|---|---|---|
| Price from | Free | $20/mo |
| Free plan | Yes | Yes |
| Free trial | Yes | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 2 | 5 |
| Spec score · Vector Databases | 7.9 (#11) | 8.9 (#2) |
| Spec score · Ai Databases | 7.9 (#18) | 8.9 (#3) |
| Founded | 1993 | 2019 |
| Headquarters | Newry, Northern Ireland, UK | New York City, New York, United States |
| Integrations | The documentation lists LangChain, Vector IO, and LlamaIndex integrations. | The partner ecosystem covers AI and machine-learning tools, search, cloud infrastructure and services integrations. |
| Product | KDB.AI is a vector database and search engine for building AI applications with real-time data. | Pinecone describes its product as a fully managed vector database built for AI workloads. |
| Search | It supports hybrid search combining dense and sparse vector search, metadata filtering, and temporal similarity search. | The database supports dense, sparse, and full-text indexes, metadata filters, and namespaces. |
| Support | The cloud FAQ directs users to the Slack community or [email protected] for assistance. | The Starter plan includes community support via Discord; paid plans include free support, and Enterprise includes Pro support. |
| Use cases | The maker presents AI research assistants, portfolio management, real-time alpha and beta extraction, and image processing as use cases. | Pinecone identifies semantic search, recommendations, and retrieval-augmented generation as use cases for its database. |
Plans
KDB.AI
- KDB.AI Cloud Starter EditionFree
- KDB.AI Server Standard EditionPriced on request
Pinecone
- Builder$20 a month
- Standard$50 a month
- Enterprise$500 a month
- StarterFree
- Bring your own cloud (BYOC)Priced on request
In their own words
KDB.AI
KDB.AI is a vector database and search engine for building AI applications with real-time data. Its hybrid search combines dense and sparse vector search with metadata filtering and temporal similarity search. It supports retrieval-augmented generation using unstructured text, video, audio, and image data. Listed vector indexes include Flat, qFlat, IVF, IVFPQ, HNSW, qHNSW, and BM25. Documentation lists integrations with LangChain, Vector IO, and LlamaIndex, while native NVIDIA cuVS integration enables GPU-accelerated vector search. Deployment options include a managed Cloud Starter Edition and a self-managed Server Standard Edition. The free Cloud Starter Edition provides 4 GB of memory per instance, 30 GB of storage, and a 10 MB query size. Its agreement makes no commitment to availability or uptime and provides the service without warranty. The cloud FAQ points users to a Slack community or [email protected] for assistance. The maker presents research assistants, portfolio management, real-time alpha and beta extraction, and image processing as use cases. KDB.AI is offered through API, web, and self-hosted platforms.
Pinecone
Pinecone is a fully managed vector database for AI workloads, available through web and API access. Its stated uses include semantic search, recommendations and retrieval-augmented generation. It supports dense, sparse and full-text indexes, metadata filters and namespaces; the maker says automatic indexing makes new writes searchable within seconds. Listed integrations include LangChain, LlamaIndex, OpenAI, Cohere, Databricks, Snowflake and Airbyte. The free Starter plan has limits including one project, two users, five indexes and 100 namespaces per index. Builder costs $20 a month. Standard is $50 a month minimum applied to usage, with usage above that minimum charged pay-as-you-go; Enterprise is $500 a month minimum on the same basis. Pinecone says it encrypts data at rest and in transit and supports API-key roles and RBAC. It lists SOC 2 Type II certification, HIPAA compliance with a BAA available on request, GDPR readiness and a 99.95% uptime SLA. Its TypeScript SDK is intended for server-side use because browser use can expose API keys.