intl.cloud.baidu.com

What is Baidu AI Cloud?

Baidu AI Cloud is the international cloud-computing arm of Baidu, positioned around a combined "AI + cloud" product stack rather than cloud infrastructure alone. Its homepage presents this as a full-stack portfolio spanning computing, networking, storage, cloud-native tooling, and monitoring/operations, with an emphasis on AI workloads running alongside conventional cloud services.

What the page actually shows

The featured products are mostly foundational infrastructure:

• Elastic Public IP — public IP addresses with scalable bandwidth for internet access
• Virtual Private Cloud (VPC) — customizable private network address space with subnets, security groups, and route tables for isolation
• Load Balancer — high-availability traffic distribution
• Baidu Cloud Compute — elastically scalable cloud servers
• Service Network Interface Card — private-network access within a VPC or to hybrid-cloud endpoints
• VPN Gateway — connectivity for business interaction and mobile-office scenarios
• Cloud Smart Network — distributed network access and broad resource coverage
• Object Storage and Cloud Disk Service — object and block storage respectively

The page also foregrounds security, compliance, and privacy as trust pillars, and invites visitors to describe their problem so Baidu Cloud experts can propose a solution.

Who it suits

Teams that want networking, compute, and storage building blocks in one place, especially those planning AI or data-heavy workloads and wanting the AI layer from the same vendor. The VPC, subnet, and security-group framing suggests it is aimed at organizations with some cloud architecture experience, not purely first-time users.

Practical next step

Before committing, list your two or three must-have services and confirm they exist in the region you need, then use the "consult" route to get the compliance and architecture questions answered in writing — region availability, data-residency terms, and support response times matter more than the product catalogue for most deployments. For a second opinion on comparable infrastructure, see Google Cloud or AWS.

How does Baidu AI Cloud compare to other cloud providers for AI workloads?

Baidu AI Cloud is most compelling when your AI workloads need to run close to Chinese users or integrate with Baidu's own AI stack; for globally distributed, Western-market AI deployments, AWS, Google Cloud, or Azure are usually the safer default. The page evidence shows Baidu positioning itself around a "Full Stack AI+Cloud Product System" with networking, storage, and compute building blocks (Elastic Public IP, VPC, Load Balancer, Baidu Cloud Compute, Object Storage, Cloud Disk Service), plus claims about security, compliance, and privacy. That is a broad infrastructure story rather than a single AI-specific differentiator, so the practical comparison depends on where your users, data, and compliance obligations sit.

Where Baidu tends to fit

• China-centric AI services. If your inference endpoints, training data, or users are in mainland China, a domestic provider reduces latency and avoids cross-border data friction. Baidu's own search, maps, and language AI heritage can matter if you are building Chinese-language applications.
• Teams already using Baidu tooling. The value of an integrated stack is highest when you are not stitching together separate vendors for networking, storage, and compute.
• Cost-sensitive entry points. The page advertises discounts such as up to 30% off Elastic Public IP bandwidth and 20% off annual Load Balancer plans. Treat these as promotional signals, not a total-cost comparison — egress, storage, and GPU pricing usually dominate AI bills.

Where the global providers still lead

AWS, Google Cloud, and Azure generally offer broader GPU instance availability, more mature managed ML platforms, larger third-party marketplace ecosystems, and more regions outside Asia. For a team training large models with spot instances or needing specific accelerator types, that breadth is often decisive.

A practical decision test

Ask three questions before choosing:

1. Where do your end users and training data physically sit?
2. Which specific accelerators or managed AI services do you need, and does the provider list them?
3. What does your compliance regime require in terms of data residency and audit?

If the answers point to China, evaluate Baidu AI Cloud directly against Alibaba Cloud and Tencent Cloud. If they point to Europe or North America, compare AWS, Google Cloud, and Azure first. You can review Baidu's own product framing at Baidu AI Cloud before requesting a consultation through their sales team.

What are the pricing options for Baidu AI Cloud services?

Baidu AI Cloud does not publish a single, uniform price list for its services. Instead, pricing depends on the specific product, configuration, region, and billing model you choose. The homepage highlights promotional offers rather than full rate cards.

What the page itself shows

• Elastic Public IP: advertised at up to 30% off, with bandwidth options up to 500Mbps.
• Load Balancer: annual plans said to start at 20% off.
• Virtual Private Cloud (VPC): described as complimentary access to VPC, subnet, security group, and route table configurations.

These are promotional signals, not a complete pricing schedule. Actual charges for compute, storage, bandwidth, and other resources are typically quoted per product and per configuration.

How pricing usually works for cloud services like these

• Pay-as-you-go: billed by usage (hours, GB, GB transferred), good for variable or short-term workloads.
• Subscription/annual plans: discounted rates in exchange for a committed term, as the Load Balancer offer suggests.
• Tiered or volume pricing: unit costs often drop at higher usage levels.
• Add-ons: bandwidth, storage capacity, and traffic distribution are usually billed separately from the base instance.

Practical next step Because the page directs visitors to "Contact Us," "Consult Now," or a sales team, the reliable route is to request a quote for your specific configuration. Before you do, note your expected region, instance type, bandwidth, storage volume, and whether you need a monthly or annual commitment — those details determine the price far more than any headline discount.

If you want to compare against alternatives, check the official pricing pages of Google Cloud and Amazon Web Services for structurally similar products.

How can I migrate my existing applications to Baidu AI Cloud?

Migrating to Baidu AI Cloud is a staged process: inventory what you run, choose the matching cloud services, move data and workloads in waves, then cut over traffic after testing. Baidu AI Cloud's page lists the infrastructure building blocks you would typically map an existing application onto — computing, networking, storage, cloud-native tooling, and monitoring and operations.

Map your stack to the listed services

The page notes that VPC, subnet, security group and route table configuration is available at no charge, and that Load Balancer annual plans start at 20% off — pricing details are worth confirming directly before you commit.

A practical migration sequence

1. Inventory and group. List each application, its dependencies, data stores, and any hard-coded IPs or hostnames. Group tightly coupled components so they move together.
2. Build the landing zone first. Create the VPC, subnets, security groups and route tables before anything else, so migrated workloads land in a controlled network from day one.
3. Move data before compute. Replicate databases and object data, verify integrity, then keep replicating until cutover.
4. Migrate in waves. Start with a low-risk internal service. Validate performance and monitoring before touching customer-facing systems.
5. Cut over with a rollback path. Use Elastic Public IP reassignment or DNS changes so you can revert quickly. The Load Balancer lets you shift traffic gradually rather than all at once.
6. Decommission only after a stable period. Keep the old environment running until you have observed the new one through at least one full business cycle.

Where to start

If you are unsure how your architecture maps onto these services, the page offers a consult path: describe the problem you are solving and Baidu Cloud experts will help identify a solution, or contact the sales team. That is the fastest route for anything non-standard, such as hybrid connectivity or regulated workloads — the page also highlights security, compliance and privacy commitments, which matter if you handle sensitive data and need to confirm alignment with your own regulatory obligations.

For reference on the broader cloud landscape while planning, you can compare approaches with AWS or Google Cloud, but base your final design on Baidu AI Cloud's own documentation and a conversation with their team.

What security and compliance features does Baidu AI Cloud offer?

Baidu AI Cloud frames security and compliance around three commitments on its homepage: safety (building a secure and trustworthy intelligent cloud), compliance (aligning with global regulations, policies and industry standards), and privacy (a security guarantee system users can trust). Those are stated positions rather than a published list of certifications or controls, so treat them as the vendor's own description of intent.

What the page actually shows

The concrete, verifiable security-relevant items in the product portfolio are networking and isolation features rather than a dedicated security product list:

• Virtual Private Cloud (VPC) — configurable network address space with secure isolation inside the VPC; the page notes complimentary access to VPC, subnet, security group and route table configurations.
• Security groups — listed as part of that complimentary VPC configuration set, which is where most inbound/outbound access rules live.
• Service Network Interface Card — private-network access to services within a VPC or a hybrid cloud endpoint, which keeps traffic off the public internet.
• VPN Gateway — aimed at business interaction and mobile office scenarios, i.e., encrypted connectivity for distributed staff.
• Elastic Public IP — public addressing and scalable bandwidth; useful to know because public exposure is the thing you then have to constrain with security groups and VPC rules.
• Load Balancer — high-availability traffic distribution, relevant to resilience more than to security posture.

Storage and compute entries (Object Storage, Cloud Disk Service, Baidu Cloud Compute) are described in terms of stability, durability and elasticity, not specific encryption or access-control guarantees.

How to use this

If you are evaluating the platform for a workload that touches regulated data, the page gives you the vocabulary to ask precise questions but not the answers. A practical next step: contact sales or a Baidu Cloud advisor — the page explicitly invites this ("Tell us what problem you are solving") — and ask for the compliance documentation that matters to your jurisdiction, such as which certifications apply, where data is stored, and what encryption is available at rest and in transit for Object Storage and Cloud Disk Service.

For a reader scenario: a small team deploying an internal service would likely start with a VPC plus security groups for isolation, a Service Network Interface Card to keep service-to-service traffic private, and a VPN Gateway for remote engineers — all of which the page describes directly. A team handling personal or financial data needs more than the homepage provides and should not treat the safety/compliance/privacy statements as evidence of a specific certification.

For comparison purposes, other major clouds publish detailed compliance pages you can read alongside this one, for example Amazon Web Services compliance offerings or Google Cloud compliance resources — useful as a template for the questions to put to Baidu's sales team.

Which industries or use cases benefit most from Baidu AI Cloud?

Baidu AI Cloud suits teams that need a mix of AI tooling and ordinary cloud infrastructure in one place, especially where networking and data movement between the two matter. Its product line spans compute, network, storage, cloud-native, and monitoring and operations, so it is less a single AI service and more a platform for running AI workloads alongside standard enterprise systems.

The strongest fit is work where you control both the model and the network path around it: private connectivity, controlled IP addressing, and traffic distribution between services.

Where it tends to fit

• AI application teams that want to train or serve models without assembling separate compute, storage and networking vendors.
• Enterprises with hybrid or private-network requirements, using a Virtual Private Cloud for isolated address space and a VPN Gateway for branch, mobile or partner access.
• Traffic-heavy web and API services that need load balancing and elastic public bandwidth to absorb demand spikes.
• Data platforms that need object storage for large, growing datasets plus block storage for low-latency workloads.
• Operations and platform teams that value monitoring and operations tooling as part of the same stack rather than a bolt-on.

A quick way to decide

Practical scenario

A retailer running a recommendation model would place training and inference on cloud compute, keep model and customer data in object storage, isolate the services inside a VPC, and use a load balancer for the customer-facing API. A manufacturer connecting factory systems to a central model would lean more on VPN Gateway and private network access.

If you are evaluating it, start by listing which of these you already run elsewhere and which you would rather consolidate. Baidu AI Cloud is most compelling when several of them land on the same platform; if you only need one isolated service, a narrower provider may be simpler. You can compare options at Baidu AI Cloud and, for the underlying AI services, Baidu Cloud.