Decision comparison
Milvus vs Turbopuffer
Milvus and Turbopuffer represent fundamentally different approaches to vector search. Milvus is an open-source, self-hosted powerhouse with consistent performance and no per-query costs, while Turbopuffer is a serverless managed service built on object storage that delivers dramatic cost savings for workloads with cold data patterns but introduces latency variability.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are vector databases.
Quick Comparison
| Decision factor | Milvus | Turbopuffer |
|---|---|---|
| Architecture | Cloud-native distributed architecture with stateless components and separated storage and compute layers | Serverless design built on object storage (S3) with automatic tiered caching on NVMe SSD and RAM |
| Pricing Model | Milvus is free and open source, and free to self-host. Fully managed Milvus is a separate product, Zilliz Cloud, which publishes its own serverless and dedicated tiers and quotes enterprise deployments. | turbopuffer's Launch plan has a $16 per month minimum; usage above that is billed on consumption. Verified 2026-09-16 against turbopuffer.com/docs/pricing-log. Rates verified 2026-09-16. turbopuffer publishes monthly usage minimums: Launch $16 per month, Scale $256 per month, and Enterprise from $4,096 per month with a 35% usage premium. Audit log streams add $128 per month on Scale and Enterprise. Verified 2026-10-05 against https://turbopuffer.com/pricing. |
| Deployment | Four options: Milvus Lite (pip install), Standalone, Distributed, and Zilliz Cloud fully managed | Fully managed serverless only; no self-hosted option; automatic scaling with zero infrastructure management |
| Search Capabilities | Vector similarity search with Global Index, metadata filtering, hybrid search, and multi-vector support | Vector similarity search plus full-text search, hybrid search combining both, and metadata filtering |
| Scalability | Scales elastically to tens of billions of vectors with horizontal scaling across distributed nodes | Handles 2.5T+ documents and 10M+ writes/s in production with virtually unlimited global capacity |
| Latency Profile | Consistent low-latency retrieval through Global Index with predictable performance regardless of scale | Sub-10ms p50 warm queries but cold namespace queries can reach 300-500ms or higher from object storage |
Milvus
- Architecture:
- Cloud-native distributed architecture with stateless components and separated storage and compute layers
- Pricing Model:
- Milvus is free and open source, and free to self-host. Fully managed Milvus is a separate product, Zilliz Cloud, which publishes its own serverless and dedicated tiers and quotes enterprise deployments.
- Deployment:
- Four options: Milvus Lite (pip install), Standalone, Distributed, and Zilliz Cloud fully managed
- Search Capabilities:
- Vector similarity search with Global Index, metadata filtering, hybrid search, and multi-vector support
- Scalability:
- Scales elastically to tens of billions of vectors with horizontal scaling across distributed nodes
- Latency Profile:
- Consistent low-latency retrieval through Global Index with predictable performance regardless of scale
Turbopuffer
- Architecture:
- Serverless design built on object storage (S3) with automatic tiered caching on NVMe SSD and RAM
- Pricing Model:
- turbopuffer's Launch plan has a $16 per month minimum; usage above that is billed on consumption. Verified 2026-09-16 against turbopuffer.com/docs/pricing-log. Rates verified 2026-09-16. turbopuffer publishes monthly usage minimums: Launch $16 per month, Scale $256 per month, and Enterprise from $4,096 per month with a 35% usage premium. Audit log streams add $128 per month on Scale and Enterprise. Verified 2026-10-05 against https://turbopuffer.com/pricing.
- Deployment:
- Fully managed serverless only; no self-hosted option; automatic scaling with zero infrastructure management
- Search Capabilities:
- Vector similarity search plus full-text search, hybrid search combining both, and metadata filtering
- Scalability:
- Handles 2.5T+ documents and 10M+ writes/s in production with virtually unlimited global capacity
- Latency Profile:
- Sub-10ms p50 warm queries but cold namespace queries can reach 300-500ms or higher from object storage
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Milvus | Turbopuffer |
|---|---|---|
| Docker Hub pulls(Product adoption) | 79.5M | Not available |
| GitHub commits, 90d(Product adoption) | 647 | Not available |
| GitHub stars(Product adoption) | 46,000+ | Not available |
| Search interest(Market interest) | 1 | 0 |
| Hacker News mentions, 90d(Community interest) | 0 | 11 |
| npm weekly downloads(Developer adoption) | 89.9k | 566.9k |
| PyPI weekly downloads(Developer adoption) | 710.4k | 1.5M |
| Stack Overflow questions(Community interest) | 208 | Not available |
| GitHub commits, 90d(Developer adoption) | Not available | 45 |
| GitHub stars(Developer adoption) | Not available | 178 |
As of October 19, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Milvus
October 19, 2026Package vulnerabilities
npm · @zilliz/milvus2-sdk-node@3.0.6 · PyPI · pymilvus@3.0.2
0 vulnerabilities
across 2 packages
Repository security score
Not available
Turbopuffer
October 19, 2026Package vulnerabilities
npm · @turbopuffer/turbopuffer@2.11.1 · PyPI · turbopuffer@2.13.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
Milvus
Turbopuffer
Feature Comparison
| Feature | Milvus | Turbopuffer |
|---|---|---|
| Core Search & Indexing | ||
| Vector Similarity Search | Global Index provides blazing fast retrieval with high recall across billions of vectors | SPFresh centroid-based index on object storage with 90-100% recall@10 for vector search |
| Full-Text Search | Not a primary capability; focused on vector-based similarity search operations | Native full-text search built in alongside vector search with dedicated performance benchmarks |
| Hybrid Search | Supports hybrid search combining vector similarity with metadata filtering in queries | Combines vector similarity, full-text search, and metadata filtering in single queries |
| Architecture & Infrastructure | ||
| Storage Architecture | Cloud-native with separated storage and compute; stateless components for elasticity | Built on object storage (S3/GCS/Azure Blob) with tiered caching to NVMe SSD and RAM |
| Deployment Model | Self-hosted Lite, Standalone, or Distributed modes plus Zilliz Cloud managed service | Serverless managed service only with automatic scaling and zero infrastructure management |
| Scaling Approach | Horizontal scaling with fully distributed architecture supporting tens of billions of vectors | Automatic serverless scaling handling 2.5T+ documents and 10M+ writes/s in production |
| Performance & Reliability | ||
| Warm Query Latency | Consistent low-latency retrieval via Global Index regardless of dataset scale | Sub-10ms p50 latency and approximately 30ms p99 for warm cached namespaces |
| Cold Query Latency | No cold start penalty since data stays indexed on dedicated compute infrastructure | Cold queries hit object storage at 300-500ms typical; cold p99 can reach up to 4 seconds |
| Write Throughput | High write throughput with near-real-time indexing across distributed nodes | Writes go to WAL on object storage first at ~285ms p50; supports 10k+ vectors/sec per namespace |
| Pricing & Cost Structure | ||
| Base Cost | Open-source and free to self-host; Zilliz Cloud managed service requires enterprise contact | Launch plan starts at $16/month; Enterprise requires custom quote |
| Storage Pricing | Self-hosted infrastructure costs only; Zilliz Cloud pricing available on request | Object storage with tiered caching costs scaling by access pattern |
| Query Pricing | No per-query charges for self-hosted; Zilliz Cloud uses capacity-based pricing model | Per GB queried plus returned with volume discounts; query prices reduced by up to 94% in 2026 |
| Security & Compliance | ||
| Compliance Certifications | Self-hosted deployments inherit your own security posture; Zilliz Cloud offers enterprise compliance | SOC2 report and GDPR-ready DPA on all plans; HIPAA-ready BAA on Scale and Enterprise plans |
| Access Control | Full control over authentication and authorization in self-hosted deployments | SSO available on Scale and Enterprise plans; CMEK and private networking on Enterprise only |
| Multi-Tenancy | Supports multi-tenancy through collection and partition-level isolation in deployments | Native multi-tenancy with namespace isolation on all plans including the Launch tier |
Core Search & Indexing
Vector Similarity Search
Full-Text Search
Hybrid Search
Architecture & Infrastructure
Storage Architecture
Deployment Model
Scaling Approach
Performance & Reliability
Warm Query Latency
Cold Query Latency
Write Throughput
Pricing & Cost Structure
Base Cost
Storage Pricing
Query Pricing
Security & Compliance
Compliance Certifications
Access Control
Multi-Tenancy
Which to choose
Milvus and Turbopuffer represent fundamentally different approaches to vector search. Milvus is an open-source, self-hosted powerhouse with consistent performance and no per-query costs, while Turbopuffer is a serverless managed service built on object storage that delivers dramatic cost savings for workloads with cold data patterns but introduces latency variability.
Best-fit scenarios
Choose Milvus if:
We recommend Milvus for teams that need full control over their vector database infrastructure, require consistent low-latency performance without cold start penalties, or want to avoid per-query billing. It is the stronger choice when you have the engineering capacity to manage distributed deployments and need predictable performance for always-hot workloads. The open-source model means zero licensing costs and complete flexibility in deployment environments.
Choose Turbopuffer if:
We recommend Turbopuffer for teams that prioritize operational simplicity and cost efficiency over absolute latency consistency. It excels when most of your vector data follows a hot-cold access pattern, such as code search indexes or multi-tenant RAG systems where data sits idle most of the time. The serverless model eliminates infrastructure management entirely, and object storage pricing delivers order-of-magnitude savings for large cold datasets compared to SSD-first alternatives.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
How does Turbopuffer achieve 10x cost savings compared to traditional vector databases like Milvus?
Turbopuffer achieves dramatic cost savings by building its storage layer on object storage services like S3, which costs less per GB than SSD-based storage. The system uses a tiered caching approach they call the "pufferfish effect" where data automatically moves between object storage, NVMe SSD, and RAM based on access frequency. Cold data sits on cheap object storage while hot data gets promoted to faster tiers. For workloads where 90% of data is rarely accessed, this architecture saves an order of magnitude compared to databases that keep all data on expensive SSDs regardless of access patterns.
Can Milvus be used as a fully managed service, or is it self-hosted only?
Milvus offers both self-hosted and fully managed deployment options. For self-hosting, you can choose between Milvus Lite (a lightweight library installable via pip, ideal for prototyping), Milvus Standalone (a single-machine deployment for production workloads with up to millions of vectors), and Milvus Distributed (a scalable enterprise-grade deployment handling billions of vectors). Additionally, Zilliz Cloud provides a fully managed Milvus experience available in both serverless and dedicated cluster configurations, with SaaS and BYOC options for different security and compliance requirements.
What are the cold start latency implications when choosing Turbopuffer over Milvus?
Cold start latency is one of the most significant tradeoffs when choosing Turbopuffer. When a namespace has not been recently accessed, queries must fetch data from object storage, resulting in latencies of 300-500ms typically, with cold p99 reaching up to 4 seconds in some cases. Once the namespace warms up through repeated access, subsequent queries hit the NVMe SSD or RAM cache at sub-10ms p50. Milvus, by contrast, keeps data indexed on dedicated compute infrastructure and does not suffer from cold start penalties, delivering consistent latency regardless of access recency. This makes Milvus better suited for workloads requiring guaranteed low-latency responses at all times.
Which notable companies use Turbopuffer in production, and what are their use cases?
Turbopuffer has been adopted by several high-profile technology companies for production workloads. Cursor, the AI code editor, uses Turbopuffer to index millions of developer codebases for semantic code search, choosing it over alternatives because most codebase embeddings sit idle between coding sessions. Notion uses Turbopuffer for connecting data to users and LLMs, with their co-founder noting that Turbopuffer's economics changed how they think about building products. Linear adopted it for embedding-based search on issues, replacing keyword search with more useful results. Other production customers include Anthropic, Atlassian, Grammarly, Ramp, and Superhuman.