AI and data science work is storage-first more often than it is compute-first. If your dataset reads stall, your GPUs idle, your pipelines lag and your cloud bill grow without better model quality.
That is why the title of best cloud storage provider rarely means a single winner for every team. Instead, “best” means the provider that matches your data shape, read patterns, compliance needs and budget risk.
Therefore, this guide will help you compare cloud storage in India using signals that change ML outcomes. The shortlist includes AWS, Microsoft Azure, Google Cloud, Oracle Cloud Infrastructure, DigitalOcean and AceCloud. Let’s get started!
1. AWS (S3 and storage ecosystem)
AWS is suitable for teams that need the broadest storage ecosystem and mature enterprise controls. Amazon S3 is designed for 11 nines durability and it anchors a large ecosystem of data tools.
It has two India regions, Mumbai (ap-south-1) and Hyderabad (ap-south-2), which helps residency and in-country DR design. If you use Spark, lakehouse stacks or event-driven pipelines, AWS integrations are often the default path.
Watchouts
- Cost modeling can be complex because requests, lifecycle transitions, replication and data transfer all carry separate line items.
- Small-file heavy pipelines can drive high request counts, which can surprise teams that only modeled GB-month storage.
- You should also validate which India region supports the specific storage and governance features you need.
Ideal AI storage setup
- Use S3 as your source of truth for datasets, features and artifacts, with strong bucket policies and least-privilege IAM.
- Use block storage or instance NVMe for hot training scratch and add a dataset cache for repeated reads.
- Use lifecycle rules for artifacts and enable versioning for checkpoints to reduce accidental deletion risk.
2. Microsoft Azure (Blob and ADLS Gen2)
Enterprise environments that rely on Microsoft identity, governance and data lake semantics will find Azure worth it. Azure offers multiple India regions, including Central India, South India and West India.
Blob Storage and ADLS Gen2 patterns fit well when you want hierarchical namespace behavior for lake-style workflows. Azure redundancy options are well documented, including durability targets for geo-redundant replication choices.
Watchouts
- Your cost and performance depend heavily on redundancy choice, access tier and whether you use hierarchical namespaces.
- Not every India region supports availability zones, which can change how you design zonal resilience.
- You should also confirm how cross-region replication behaves for your storage account, especially for regulated datasets.
Ideal AI storage setup
- Store raw datasets and curated features in Blob or ADLS Gen2, then use managed identity and RBAC for access control.
- Use premium disks or local NVMe for training scratch, with a cache layer for repeated dataset windows.
- Use lifecycle management for artifacts and write access logs to a central workspace for audit readiness.
3. Google Cloud (Cloud Storage)
Google Cloud is great for teams that want clean integration with analytics workflows and a consistent developer experience. Cloud Storage is designed for 11 nines durability and it documents availability and durability behavior clearly.
It lists India regions including Mumbai (asia-south1) and Delhi (asia-south2), which helps residency planning. If your pipeline is analytics-heavy, tight integration with managed compute and data services can reduce operational work.
Watchouts
- You should be explicit about bucket location, storage class and egress paths, because defaults can surprise you at scale.
- Cross-region and internet egress behaviors vary by architecture and costs can move with usage patterns.
- You should also confirm which services are supported in each India region before you lock in your design.
Ideal AI storage setup
- Use Cloud Storage as the source of truth, with versioning for artifacts and retention controls when needed.
- Use local SSD or persistent disks for training scratch and add a cache to reduce repeated object reads.
- Use lifecycle policies and object prefixes that match your partitioning strategy for predictable listing and retention.
4. Oracle Cloud (OCI Object Storage)
Oracle Cloud can help you if you want cost predictability for data movement and a straightforward in-country region pair. OCI has two India regions, Mumbai and Hyderabad, which supports in-country DR designs for many workloads.
OCI Object Storage is positioned as durable, scalable storage and it states an 11 nines durability target. OCI networking pricing highlights a high threshold for free outbound transfer, including the first 10 TB free per region.
Watchouts
- Ecosystem fit varies by organization, particularly if your stack assumes AWS-native services and IAM patterns.
- You should validate the integrations you need, such as lakehouse connectors, CI pipelines and artifact tooling.
- You should also test request-heavy workloads, because cost and performance are sensitive to metadata and API behavior.
Ideal AI storage setup
- Use OCI Object Storage for raw data, curated features and artifacts, with clear compartment and policy boundaries.
- Use block volumes for hot training session, then push checkpoints and artifacts back to object storage.
- Use cross-region replication inside India when your RPO and RTO targets require a second region.
5. DigitalOcean(Spaces)
This is best for startups and small teams that want simple object storage in India with S3-compatible workflows. DigitalOcean introduced Spaces object storage in Bangalore, which helps teams keep data in India for latency and residency goals.
Spaces provides an S3-compatible API with documented partial feature support, which enables many S3-based tools to work. Spaces includes a built-in CDN option for eligible bucket types, which can simplify distribution-related use cases.
Watch-outs
- Spaces offers fewer enterprise governance features than hyperscalers, which can matter for regulated workloads.
- S3 compatibility is not full-parity, which means some advanced S3 features may not behave the same way.
- You should validate IAM granularity, audit logging and retention controls against your DPDP and internal policy needs.
Ideal AI storage setup
- Use Spaces for datasets, artifacts and logs, then keep hot training scratch on block storage attached to your compute.
- Shard datasets aggressively to reduce request overhead and listing latency.
- Use explicit environment separation, because small teams often blur dev and prod boundaries without guardrails.
6. AceCloud(Object Storage)
AceCloud should be your go-to cloud storage provider if your team wants India-first storage with S3-compatible workflows and pricing that is easy to forecast. AceCloud provides S3-compatible object storage and has a transparent pricing page with tiered classes in INR.
It also positions a 99.99%* uptime SLA and highlights multi-zone architecture in its networking materials. If you already run GPUs or Kubernetes on AceCloud, co-locating storage and compute can reduce latency and transfer friction.
Watch-outs
- You should validate integration requirements for your stack, especially if you rely on specific S3 features or lakehouse connectors.
- You should confirm DR expectations, including how replication works and what SLA coverage applies to storage operations.
- You should also test throughput with your dataset shape, because performance is driven by object size and concurrency patterns.
Ideal AI storage setup
- Use object storage as the source of truth, then keep training scratch on local NVMe or block volumes near your GPUs.
- Apply lifecycle tiers to artifacts, logs and older dataset versions to keep storage spend predictable.
- Use versioning for checkpoints and enforce least-privilege access via dedicated service credentials per pipeline stage.
Useful Object Storage Performance Patterns for AI/ML teams
Performance tuning starts with understanding when object storage helps and when it hurts. You should treat performance as a pipeline design problem, not a provider-only problem. That framing gives you levers even when you cannot change vendors quickly.
When object storage shines
Object storage works well for data lakes, batch preprocessing, artifact storage and backups. It is strong when your jobs do large sequential reads and writes with high concurrency. It also integrates with analytics and lakehouse tools, which matters for feature generation workflows.
When object storage hurts
Object storage struggles with random reads, tiny files and high metadata churn. Training pipelines often re-read the same samples, which amplifies latency variance and request overhead. You feel this pain most when your dataset has millions of small objects or deep partition trees.
Practical fixes
- Convert tiny files into shards, such as tar shards, WebDataset shards or Parquet row groups.
- Add dataset caches close to compute, such as local NVMe cache or a distributed cache tier.
- Use prefetch and async loading, because overlapping I/O with compute reduces GPU idle time.
- You should store hot training windows on block storage or local NVMe when your access pattern is random.
- This reduces per-object overhead and it makes epoch time more stable. Then, sync results back to object storage for durability and sharing across runs.
Choose Wisely with AceCloud Experts!
There you have it. The best cloud storage provider in India depends on your read patterns, compliance requirements and cost sensitivity. Therefore, you should pick a shortlist, run a two-week proof with your dataset shape and measure throughput, request volume and total cost.
Feeling overwhelmed with the process? Why not share your use case and workload-related details with us and allow our cloud experts to sort things out for you? Just book your free consultation session and even make the most of free INR 20,000 credits. Connect with us today!
