Turbopuffer is built on a single, aggressive premise: vector storage should cost the same as object storage ($0.03/GB), not RAM ($5.00+/GB). It achieves this by decoupling compute from storage entirely, keeping your index in S3 and only loading what it needs into memory when you query it. This architecture makes it approximately 10x to 100x cheaper than memory-resident alternatives like Pinecone or Milvus for massive datasets, but it demands a specific trade-off: you accept cold-start latencies of ~400ms in exchange for a drastically lower bill.
For a workload storing 50 million vectors (1536 dimensions), you are looking at roughly 300GB of data. On Pinecone Serverless (at $0.33/GB), storage alone costs ~$100/month before you pay for a single read unit. On a pod-based managed instance, you’d likely need multiple high-memory pods costing $1,000+/month. With Turbopuffer, that same 300GB of storage costs about $9/month. Even with the $64/month minimum spend and read/write costs added, you are essentially paying a flat rate for a dataset that would bankrupt you on other platforms.
The API is refreshingly simple. It supports full BM25 keyword search alongside vector search, handling the hybrid search requirements of modern RAG apps without needing a secondary database like Elasticsearch. The SDKs (Python, TS, Go) are lightweight and don't require managing clusters, shards, or replication factors. It’s truly serverless—you just push vectors and query them.
The catch is the "cold" query. If your data hasn't been accessed recently, Turbopuffer fetches it from S3, resulting in a ~400ms delay. Hot queries (cached in RAM/NVMe) hit the standard 10-20ms range. This makes it excellent for search bars or RAG workflows where a half-second delay is imperceptible to a human, but terrible for high-frequency recommendation engines requiring sub-20ms guarantees.
Turbopuffer is the wrong choice for hobbyists due to its $64/month minimum; for small datasets, Pinecone’s free tier or standard serverless pricing is actually cheaper. But if you are sitting on terabytes of embeddings or building a multi-tenant SaaS where every customer needs their own isolated index, Turbopuffer is the most cost-efficient architecture on the market.
Pricing
Turbopuffer has no free tier. The entry point is a strict $64/month minimum commitment, which covers a significant amount of usage (millions of vectors) but effectively walls off hobbyists and small prototyping projects. Storage is priced at effectively raw S3 rates (~$0.03/GB), while writes and reads are billed by throughput. The cost cliff is inverted compared to competitors: it is expensive to start (vs. Pinecone's $0 free tier), but becomes dramatically cheaper at scale. A 100GB dataset that costs $33/mo on Pinecone Serverless storage costs $3 on Turbopuffer, making the $64 minimum the only real ceiling for mid-sized apps.
Technical Verdict
The Developer Experience is polished and minimal. The Python SDK is a thin wrapper that feels like interacting with a local list rather than a distributed system. Documentation is concise but covers the essentials like BM25 and metadata filtering well. The primary technical constraint is the latency floor: you must architect your application to tolerate 200-500ms cold starts. Reliability is high due to the S3 backend (99.999999999% durability), but it is a closed-source proprietary backend, so you cannot self-host it if compliance requires on-premise air-gapping.
Quick Start
# pip install turbopuffer
import turbopuffer as tpuf
tpuf.api_key = "your-key"
ns = tpuf.namespace("test-index")
# Upsert and query in two lines
ns.upsert(ids=[1], vectors=[[0.1, 0.2]], attributes={"text": "hello"})
results = ns.query(vector=[0.1, 0.2], top_k=1)
print(results[0].attributes["text"])Watch Out
- The $64/month minimum applies immediately; there is no free tier for testing.
- Cold queries (first hit after inactivity) will consistently take ~400ms.
- Prefix queries on namespaces are supported, but wildcards elsewhere can be slow.
- Schema updates are not as flexible as document stores; plan your metadata attributes carefully.