LlamaIndex vs Turbopuffer

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	LlamaIndex RAG	Turbopuffer RAG
Tagline	Data framework for connecting LLMs to your data.	Fast search on object storage
Category	RAG	RAG
Pricing	Freemium· Free open-source; LlamaCloud paid	Paid· Launch $16/mo minimum · Scale $256/mo minimum · Enterprise $4,096/mo minimum (35% usage premium). Usage-based above the commitment; no free tier. Cost calculator on pricing page for storage/writes/queries.
Model	BYO (Claude / GPT / open)	bring-your-own embeddings (any provider)
Editorial score	8.7 / 10	—
Use cases	RAGdata ingestionindexing	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
Pros	Focused on retrieval (not general agent stuff) Many ingestion connectors Strong production patterns LlamaCloud for managed ingestion	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) Serverless — no clusters, shards, or replicas to manage; scales namespaces automatically Used in production by demanding AI teams (Cursor, Notion, Anthropic, Linear), which is meaningful social proof Comprehensive REST API and clear latency/recall SLIs published rather than hand-waved
Cons	API surface is large Documentation can be hard to navigate	No free tier and a $16/mo floor even on the smallest plan, so it is not a fit for hobby projects or evaluation on a shoestring Closed-source, managed-only — no self-host option, which rules out air-gapped or fully sovereign deployments below the Enterprise BYOC tier Object-storage cold reads mean tail latency and cache-miss behaviour matter more than in a purely in-memory system; tuning matters for latency-critical UX You bring your own embeddings — no built-in embedding model, ingestion pipeline, or chunking, unlike higher-level RAG platforms Enterprise features people often need in regulated industries (SSO, HIPAA BAA, audit logs) start at the $256/mo Scale plan and above
Website	www.llamaindex.ai	turbopuffer.com

Pick LlamaIndex if

✅ Focused on retrieval (not general agent stuff)
✅ Many ingestion connectors
✅ Strong production patterns
✅ LlamaCloud for managed ingestion

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)

Compare a different pair →