Best S3-Compatible Object Storage for AI Workloads in 2026
We tested 7 S3-compatible object storage providers for AI and ML workloads — measuring throughput, latency, cost per TB, and compatibility with vector databases and embedding pipelines. Every provider tested with MVS (Mixpeek Vector Store), which runs on any S3-compatible backend.
How We Evaluated
Cost per TB
Storage cost, egress fees, and API request pricing. For AI workloads, egress and GET request costs often dominate — not just storage.
S3 Compatibility
Completeness of S3 API support. Tested multipart uploads, presigned URLs, lifecycle policies, and compatibility with MVS, MinIO, and boto3.
Performance
Upload throughput, download latency, and time-to-first-byte for large objects (embeddings, model weights, media files).
AI Ecosystem Fit
Integration with AI tools: works as a backend for vector databases (MVS, LanceDB), model registries, dataset versioning, and training pipelines.
Operational Simplicity
Setup time, dashboard quality, documentation, and support responsiveness.
Backblaze B2
The best balance of cost, reliability, and S3 compatibility for AI workloads. B2 is 1/4 the price of AWS S3 with free egress to Cloudflare and Fastly CDN partners. Tested as an MVS backend — Mixpeek's vector store runs directly on B2, giving you vector search on top of your existing B2 storage without moving data.
Pros
- +Cheapest mainstream storage at $6/TB/mo (vs $23/TB on S3)
- +Free egress to Cloudflare, Fastly, and other CDN partners
- +Full S3 compatibility — works with MVS, boto3, rclone, everything
- +Proven reliability (500B+ objects stored) with 11 nines durability
Cons
- -Single-region only (US-West-004 or EU-Central-003)
- -No serverless compute integration like Lambda@Edge
- -Rate limits on free egress via CDN partners
- -Smaller ecosystem than AWS for adjacent services
Cloudflare R2
Zero egress fees — period. R2 is the strongest choice for read-heavy AI workloads (retrieval, inference serving, RAG) where egress costs would otherwise dominate your bill. Fully S3-compatible and works as an MVS backend for BYO vector search.
Pros
- +Zero egress fees — game-changing for retrieval-heavy workloads
- +Workers integration for serverless compute at the edge
- +Full S3 API compatibility — tested with MVS, LanceDB, DuckDB
- +Automatic multi-region replication
Cons
- -$15/TB/mo storage — more expensive than B2 or Wasabi
- -No lifecycle policies for automatic tiering (yet)
- -Rate limits on free tier (10M reads/mo, 1M writes/mo)
- -Less mature than S3 for large-scale batch operations
AWS S3
The default choice and the most battle-tested object storage on the planet. Unmatched ecosystem integration (Lambda, SageMaker, Bedrock, S3 Vectors). Higher cost than alternatives but offers capabilities nobody else has — including native S3 Vectors for vector search directly in your bucket.
Pros
- +Deepest ecosystem integration — Lambda, SageMaker, Bedrock, EMR
- +S3 Vectors: native vector search within S3 (new, ~100ms latency)
- +Intelligent Tiering automates hot/cold lifecycle
- +11 nines durability with cross-region replication
Cons
- -$23/TB/mo storage — 4x more expensive than B2
- -Egress fees add up fast ($90/TB)
- -S3 Vectors still limited (no hybrid search, no filtering)
- -Complexity tax: IAM policies, VPC endpoints, encryption configs
Tigris
Globally distributed, S3-compatible object storage built on FoundationDB. Data automatically replicates to the region closest to your users. Newest entrant on this list but technically impressive — designed from scratch for modern workloads.
Pros
- +Automatic global distribution — data follows your users
- +Zero egress within the Tigris network
- +S3-compatible API works with MVS, boto3, and standard tools
- +Built on FoundationDB for strong consistency guarantees
Cons
- -Newest provider — less production track record
- -Pricing still evolving as they scale
- -Smaller community and fewer integrations than S3 or R2
- -No equivalent to S3 lifecycle policies yet
Wasabi
Hot cloud storage at cold storage prices. Wasabi positions itself as a drop-in S3 replacement with no egress fees and no API request fees. Straightforward pricing makes cost predictable — you pay for storage and nothing else.
Pros
- +No egress fees and no API request fees
- +Predictable flat-rate pricing at $6.99/TB/mo
- +Full S3 API compatibility
- +Good for bulk storage of embeddings and training data
Cons
- -90-day minimum storage duration — early deletion fees apply
- -Higher latency than S3 or R2 in our benchmarks
- -No serverless compute integration
- -Limited lifecycle automation compared to S3 Intelligent Tiering
MinIO
Self-hosted, S3-compatible object storage that runs on your own hardware or VMs. The standard choice for air-gapped, on-prem, or regulated environments where data cannot leave your infrastructure. Works as an MVS backend for private-cloud vector search.
Pros
- +Self-hosted — full control over data residency and security
- +S3-compatible with excellent ecosystem support
- +High throughput on dedicated hardware (100+ Gbps benchmarks)
- +Open-source with active development
Cons
- -You manage everything: hardware, upgrades, monitoring, backups
- -No managed offering — operational overhead is real
- -Cost advantage disappears at small scale (hardware amortization)
- -Requires Kubernetes or bare-metal expertise
Google Cloud Storage (GCS)
Google's object storage with strong ML ecosystem integration (Vertex AI, BigQuery). Autoclass automatically moves objects between storage tiers. A solid choice for teams building on Google Cloud, but pricier than B2 or R2 for storage-heavy AI workloads.
Pros
- +Tight Vertex AI and BigQuery integration
- +Autoclass handles lifecycle tiering automatically
- +Strong consistency guarantees
- +S3-compatible interoperability API available
Cons
- -$20/TB/mo (Standard) — expensive for large datasets
- -Egress fees ($0.12/GB) are the highest on this list
- -S3 compatibility layer has gaps (no multipart presigned URLs)
- -Less cost-competitive than B2, R2, or Wasabi for pure storage
Frequently Asked Questions
Can I use object storage as a backend for a vector database?
Yes — this is exactly what MVS (Mixpeek Vector Store) does. MVS stores vectors and metadata on any S3-compatible object storage (B2, R2, S3, Tigris, Wasabi, MinIO) and serves hybrid search (dense + sparse + BM25) on top. Hot data is cached for ~8ms queries; warm data is served from object storage at ~92ms. This means your vector database costs are mostly just your storage bill, not a separate database subscription.
What is the cheapest S3-compatible storage for AI workloads?
Backblaze B2 at $6/TB/mo is the cheapest for storage-heavy workloads with moderate egress (especially with free CDN egress). Cloudflare R2 at $15/TB/mo is cheapest for read-heavy workloads because egress is free. Wasabi at $6.99/TB/mo is cheapest for predictable flat-rate pricing with no API fees. The right answer depends on your access pattern: if you read a lot (retrieval, RAG), R2 wins on total cost; if you store a lot and read less, B2 wins.
Is MinIO a good alternative to AWS S3?
MinIO is an excellent S3 alternative for self-hosted and on-prem deployments. It offers full S3 API compatibility, high throughput on dedicated hardware, and complete data sovereignty. The trade-off is operational overhead: you manage hardware, upgrades, and backups yourself. For cloud workloads, Backblaze B2 or Cloudflare R2 offer the same S3 compatibility with zero ops at lower cost than S3.
What is the best object storage for storing embeddings and model weights?
For embeddings: use any S3-compatible storage paired with MVS, which adds vector search on top. Backblaze B2 is the most cost-effective for large embedding collections. For model weights: Cloudflare R2 is ideal because zero egress means you can pull weights to any region without transfer fees. For training data: Wasabi's flat-rate pricing with no API fees keeps costs predictable during data-intensive training runs.
How does object storage compare to block storage for AI workloads?
Object storage (S3, B2, R2) is 5-10x cheaper per TB than block storage (EBS, Persistent Disks) and scales to exabytes without provisioning. The trade-off is higher latency: ~50-100ms for object storage vs ~1-5ms for block storage. For AI workloads, object storage is the right choice for embeddings, datasets, and model artifacts. Use block storage only for the hot serving layer (like Qdrant or the warm cache in MVS) where sub-10ms latency is critical.
Ready to Get Started with Mixpeek?
See why teams choose Mixpeek for multimodal AI. Book a demo to explore how our platform can transform your data workflows.
Explore Other Curated Lists
Best Multimodal AI APIs
A hands-on comparison of the top multimodal AI APIs for processing text, images, video, and audio through a single integration. We evaluated latency, modality coverage, retrieval quality, and developer experience.
Best Video Search Tools
We tested the leading video search and understanding platforms on real-world content libraries. This guide covers visual search, scene detection, transcript-based retrieval, and action recognition.
Best AI Content Moderation Tools
We evaluated content moderation platforms across image, video, text, and audio moderation. This guide covers accuracy, latency, customization, and compliance features for trust and safety teams.