Infino vs Qdrant / Pinecone
Qdrant runs on a cluster you size or on Qdrant Cloud. Pinecone manages the cluster as a service. Infino keeps embeddings in Parquet on object or block storage and runs BM25 and SQL over the same rows.
diff --stat qdrant pinecone infino
The short answer
Infino replaces a standalone vector database. The embeddings stay in your Parquet, and keyword search and SQL run on 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.
cat ARCHITECTURE.diff
Where the data lives
A dedicated vector store keeps its own index copy. Infino keeps the vector index in the file you already have.
- Qdrant’s index lives on nodes you provision or on Qdrant Cloud, with explicit controls for RAM, disk, and quantization.
- Pinecone manages the cluster as a service. 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.
infino diff --features
Capability by capability
| 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; no codec selection | fp32 in; Sq16 internally |
| query parameters | HNSW, exact, rescore, oversampling | scoring controls; scan factor on dedicated | chosen from the data |
| keyword search | 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 cluster storage | object or block storage |
| runs as | cluster | cluster | embedded library, or Infino Cloud |
| license | Apache-2.0 | closed | Apache-2.0 engine |
elasticsearch / opensearch · qdrant / pinecone · postgres / clickhouse · the field
infino bench --vector
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 cache
- Cold cache
External benchmark reference for vector dbs: VDBBench leaderboard (different harness) →
infino cost --compare vector
Why the bill is different
Qdrant Cloud bills packages sized to keep the vectors in RAM. Pinecone Serverless bills stored GB plus read and write units. Infino meters the files in your bucket.
Qdrant Cloud · Standard
AWS us-east-1 packages · 2 nodes · 60 GB/replica at 1.5× fp32 in RAM · 40 GB stored
Pinecone Serverless · Standard
us-east-1 list rates · 1 RU per GB stored · $50/mo min
cat TRADEOFFS.md
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. It does not expose a quantization codec.
- Infino accepts fp32 vectors, uses Sq16 internally, and derives its index and query parameters from the data.
infino migrate --from qdrant
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.
cat FAQ.md
Does Infino replace Qdrant or Pinecone?
Yes. Infino runs vector search over the same Parquet files it already searches with BM25 and SQL. HNSW when the working set is pinned in RAM; OPANN + Sq16 when it lives on object storage. Keyword and vector run in one pass and are fused by RRF inside the query.
What is the main difference?
Qdrant and Pinecone are vector-first clusters. You size Qdrant yourself or use Qdrant Cloud; Pinecone manages the cluster as a service. Infino keeps embeddings in Parquet next to the columns and BM25 index, on object or block storage.
How much does Infino reduce vector retrieval cost?
Infino is 10× cheaper than the retrieval stack it replaces. Vectors stay in Parquet, and BM25 and SQL run over the same copy, so there is no separate vector store plus search and analytics systems. The calculator on this page compares Qdrant Cloud and Pinecone Serverless using published prices.
Can I run SQL over the same vectors?
Yes. vector_search and hybrid_search are table-valued functions in DataFusion, so a ranked result joins, aggregates, and windows like any other relation. Qdrant and Pinecone stop at the retrieval API.
Why would I use Qdrant or Pinecone instead?
Use Qdrant when you need direct control over quantization, memory placement, HNSW, or rescoring. Pinecone manages those internals but exposes hosted scoring modes, hybrid weighting, and dedicated read capacity. Infino accepts fp32 vectors, uses Sq16 internally, and chooses index parameters from the data.
How do I migrate from Qdrant or Pinecone?
Export the vectors and the metadata you filter on, append them into Infino tables over REST in Arrow or JSON, run both against real traffic, then move reads. There is no in-place converter for either engine’s private format.