Same object-storage architecture. Your cloud, not theirs.
Turbopuffer pioneered object-storage-native vector search. ManyVector takes it one step further — your data lives in a bucket you own, you pay your cloud provider directly, and you can self-host the entire stack with a single binary.
Your bucket
data never leaves your cloud account
Self-hosted
single binary, zero dependencies
Drop-in
OpenAI-compatible API
ManyVector vs Turbopuffer, side by side.
Feature
ManyVector
Turbopuffer
Storage architecture
✓ Object storage (S3 / GCS / R2) — your bucket, your account
✓ Object storage — Turbopuffer's cloud account
Self-hosting
✓ Single binary — deploy on any VM, container, or Kubernetes cluster
✗ SaaS only — no self-hosted option available
Data sovereignty
✓ Data never leaves your cloud account or region
✗ Data stored on Turbopuffer infrastructure
Pricing transparency
✓ Pay your cloud provider directly — S3 / GCS / R2 list and GET rates
✗ Managed-service pricing with Turbopuffer markup on top of storage costs
Namespace branching
✓ Copy-on-write instant branching built in
✗ Not documented — not available
Full-text search
✓ BM25 inverted index included
✗ Limited full-text capabilities
Hybrid search
✓ BM25 + vector with RRF fusion
✗ Limited hybrid search support
OpenAI-compatible API
✓ Drop-in /v1/vectors endpoint
✗ Proprietary API
Setup
✓ Connect your bucket, deploy binary — ready in minutes
✓ Managed — create namespace via API or console
Why teams switch from Turbopuffer.
Data control
Your cloud, not theirs.
Turbopuffer uses object storage under the hood, but the bucket belongs to them. With ManyVector, you point to a bucket in your own AWS, GCP, or Cloudflare account. Your credentials, your access logs, your retention policy — no third-party touching your vectors.
Cost structure
Pay storage rates, not service rates.
Managed object-storage services add a layer of pricing on top of actual storage costs. With ManyVector, there is no intermediary — your application talks directly to your bucket. You see exactly what you owe on your cloud bill, with no markup for routing through a vendor.
Deployment flexibility
Self-host anywhere.
Turbopuffer is SaaS-only. ManyVector ships as a single binary with no external process dependencies. Run it on-premises for air-gapped environments, in a private VPC for data-residency compliance, or in the ManyVector cloud when managed is preferred.
Common questions
If both use object storage, why does the difference matter?
The architecture is similar, but ownership diverges entirely. With Turbopuffer your data lives in their bucket on their cloud account — you have no direct access to the underlying objects. With ManyVector the bucket is yours: you can read, audit, copy, or delete objects independently of ManyVector. This matters for compliance, disaster recovery, and vendor-exit planning.
How does ManyVector's pricing compare to Turbopuffer?
ManyVector charges nothing on top of your cloud storage bill. You pay S3 / GCS / R2 PUT, GET, and storage rates directly. Turbopuffer charges a managed-service fee that includes their infrastructure and operational overhead on top of underlying storage costs. For large, stable datasets the difference compounds month over month.
Does ManyVector match Turbopuffer's query performance?
ManyVector uses an HNSW graph stored in object storage with an in-process cache for hot namespaces. Query latency is comparable for cached namespaces and slightly higher on cold reads, which is a property shared by any object-storage-native vector database. For latency-sensitive workloads both products warm the cache on namespace load — the practical p99 difference for cached queries is under 5 ms.
How long does migration from Turbopuffer take?
Typically one day. Export your vectors from Turbopuffer via their fetch or export API, re-upsert them into a ManyVector namespace backed by your bucket, and update the endpoint in your client code. Because ManyVector exposes an OpenAI-compatible API, the client-side change is usually a single base-URL update rather than a library swap.