Pathway vs Turbopuffer
A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.
Pathway RAG | Turbopuffer RAG | |
|---|---|---|
| Tagline | Live data framework for production RAG and streaming ETL pipelines in Python. | Fast search on object storage |
| Category | RAG | RAG |
| Pricing | Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key | Paid· launch: $16/month · scale: $256/month · enterprise: >=$4,096/month |
| Model | Multi-model | bring-your-own embeddings (any provider) |
| Editorial score | 7.3 / 10 | — |
| Use cases | live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection | Production RAG chatbotsMulti-tenant semantic searchAgent long-term memorySemantic code searchRecommendation systemsLog and observability searchHybrid keyword + vector product searchLarge-scale document retrievalRe-embedding experiments via namespace branching |
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| Website | pathway.com | turbopuffer.com |
Pick Pathway if
- ✅ Genuinely live indexing - documents update without rebuild jobs
- ✅ Self-hosted under BSL 1.1, no data leaves your infra
- ✅ Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake)
- ✅ Same pipeline handles batch and streaming
Pick Turbopuffer if
- ✅ Object-storage-first architecture is dramatically cheaper than RAM-resident vector DBs at billion-vector scale
- ✅ Native hybrid search (vector + BM25) with metadata filters in a single query
- ✅ Namespace model plus copy-on-write branching maps cleanly to multi-tenant RAG and re-embedding workflows
- ✅ Very high write and query throughput demonstrated in production (10M+ writes/s, 25k+ QPS)