Infino vs Qdrant / Pinecone
Qdrant gives you direct control over a vector cluster or runs it in Qdrant Cloud. Pinecone provides managed vector indexes. Infino keeps embeddings in Parquet and runs BM25 and SQL over the same rows.
Try it on your own vectors.
pip install infinoquickstart →
Go deeper: architecture · vs elasticsearch / opensearch · vs postgres / clickhouse
When the vector index is part of retrieval
Infino is an alternative when the vector index is one part of a broader retrieval workload and you want BM25, vectors, and SQL over the same rows.
- Qdrant lets you place HNSW and vectors across RAM and disk on nodes you size.
- Pinecone offers managed on-demand and dedicated indexes.
- Infino pins HNSW when the working set fits in RAM, and uses OPANN + Sq16 when the vectors live on object storage.
- BM25 and vectors are fused with RRF in one query, in one pass.
- The file stays standard Parquet.
Where the data lives
Qdrant and Pinecone maintain a vector index for serving. Infino stores the vector index with the Parquet rows it belongs to.
- Qdrant’s index lives on nodes you provision or on Qdrant Cloud, with explicit controls for RAM, disk, and quantization.
- Pinecone manages the index and serving layer. On-demand and dedicated read capacity are separate deployment choices.
- Infino writes the codes into the same Parquet superfile as the columns and the BM25 index. The engine searches that file in place and caches hot slices locally.
Retrieval surface and controls
| capability | qdrant | pinecone | infino |
|---|---|---|---|
| vector search | HNSW across RAM or disk | managed ANN; on-demand or dedicated | HNSW when pinned in RAM; OPANN + Sq16 on object storage |
| quantization | selectable codecs and bit widths | managed codec | fp32 in; Sq16 internally |
| query parameters | HNSW, exact, rescore, oversampling | scoring controls; scan factor on dedicated | automatic index and query tuning |
| text retrieval | sparse vectors | sparse vectors | BM25 in the same file, same pass |
| hybrid ranking | dense + sparse; RRF or DBSF | dense + sparse | RRF in one query, one pass |
| sql | none | none | DataFusion; search as table functions |
| filters | payload filters on the HNSW walk | metadata filters on the hosted index | scalar predicates pushed into the same pass |
| data format | engine-private | engine-private | standard Parquet |
| where data lives | RAM and disk on the nodes you size | managed index storage | object or block storage |
| runs as | cluster or Qdrant Cloud | managed service | embedded library or managed service |
| license | Apache-2.0 | closed | Apache-2.0 engine |
elasticsearch / opensearch · qdrant / pinecone · postgres / clickhouse · snowflake / databricks · iceberg / hudi
Measured vector latency
These are Infino top-10 measurements over 10M Cohere vectors at 768 dimensions. Warm is steady state; cold is the first query against an idle table.
- Warm
- Cold
Internal 10M-document run. Its full results are not published yet. The published engine benchmarks, with a way to reproduce each one, are on the benchmarks page.
External benchmark reference for vector dbs: VDBBench leaderboard →
Index and query controls
Qdrant exposes index internals. Pinecone exposes managed retrieval and scoring controls.
- Qdrant exposes quantization codecs, memory tiers,
hnsw_ef, exact search, rescoring, and oversampling. - Pinecone exposes dense and sparse scoring, hybrid weighting, and a scan factor on dedicated indexes while managing quantization internally.
- Infino accepts fp32 vectors, uses Sq16 internally, and derives its index and query parameters from the data.
How migration works
Export the vectors and the metadata you filter on. Append them into Infino tables. Dual-run, then move reads.
- Dump points from Qdrant or fetch from Pinecone with the metadata you need to filter.
- Append into Infino over REST, Arrow or JSON. Indexing happens on write.
- Point the same queries at
vector_searchorhybrid_search, compare, then cut over.
Qdrant and Pinecone questions
Does Infino replace Qdrant or Pinecone?
Yes. Infino provides vector search over Parquet and combines it with BM25, reciprocal-rank fusion, and SQL in the same engine. HNSW serves resident working sets; OPANN + Sq16 serves vectors from object storage.
How much does Infino reduce vector retrieval cost?
The result depends on vector count, dimensions, traffic, availability, and whether the vector store is an additional copy of data you already retain elsewhere.
Can I run SQL over the same vectors?
Yes. vector_search and hybrid_search are table-valued functions in DataFusion, so a ranked result can feed joins, aggregates, and windows. Qdrant and Pinecone expose retrieval APIs; SQL composition happens elsewhere in the application stack.
Why would I use Qdrant or Pinecone instead?
In addition to reducing cost, Infino automates several parameters to simplify vector scaling and optimization. If you need granular control over HNSW or IVF query parameters, compute placement, or scoring, and that control is worth the cost difference, Qdrant or Pinecone may be a better choice.
How do I migrate from Qdrant or Pinecone?
Export the vectors and filter metadata, append them into Infino tables over REST in Arrow or JSON, and dual-run representative traffic before routing reads differently. Migration rebuilds the index from those records because each engine uses its own storage format.