Cheapest GPU Cloud for LLM Inference APAC 2026: AWS vs GCP vs Alibaba Cloud Compared

The APAC GPU market is under serious pressure right now. Within weeks of each other, Anthropic shipped Claude Opus 4.8, Google pushed Gemini 3.1 Pro into Vertex AI enterprise tiers, and then followed it with Gemini 3.5 Flash GA—a model Google claims runs at 4× the speed of its predecessor at frontier-class quality. Meanwhile, Computex 2026 in Taipei put next-generation AI accelerator silicon on the roadmap for H2 2026. Every one of these announcements translates into the same operational question for engineering teams: which GPU cloud is cheapest and fast enough for production LLM inference in Asia-Pacific?

This article gives you an objective, data-grounded comparison of the three most commonly deployed options—AWS (ap-southeast-1 Singapore / ap-east-1 Hong Kong), Google Cloud Platform Vertex AI (asia-southeast1 / asia-east1), and Alibaba Cloud (cn-hongkong / ap-southeast-1 Singapore). We'll cover on-demand GPU pricing, reserved pricing, p50/p99 inference latency benchmarks from public sources, and egress costs—because egress is where budgets quietly die.

Why LLM Inference Cost in APAC Is Different From US/EU

Three structural factors make APAC inference more expensive than equivalent US workloads:

• Egress premiums: AWS charges $0.08–$0.09/GB out of Singapore versus $0.09/GB out of Hong Kong. GCP Asia egress to the internet runs $0.08–$0.12/GB depending on destination. Alibaba Cloud's APAC egress sits at roughly $0.07–$0.08/GB for cross-border traffic.
• GPU scarcity: H100 and A100 on-demand capacity in Asia regions is capacity-constrained. AWS, GCP, and Alibaba Cloud all show intermittent "insufficient capacity" errors for on-demand H100 in HK and Singapore during peak windows.
• Latency to end-users: Southeast Asia, Greater China, South Korea, and Japan have materially different round-trip times to a single Singapore origin. Running inference from one node adds 30–120 ms of last-mile latency depending on user country.

GPU Instance Pricing Comparison: APAC Regions (June 2026)

The table below uses publicly listed on-demand prices. Reserved/committed-use prices shown are for 1-year terms without upfront.

NVIDIA A100 80 GB (single GPU, on-demand)

• AWS p4d.24xlarge (8× A100 40 GB, Singapore): ~$32.77/hr on-demand → per-GPU effective ~$4.10/hr. 1-year reserved: ~$2.60/hr per GPU.
• GCP a2-highgpu-1g (1× A100 40 GB, asia-southeast1): $3.67/hr on-demand. 1-year CUD: ~$2.33/hr.
• Alibaba Cloud ecs.gn7e-c16g1.4xlarge (1× A100 80 GB, Hong Kong): ~¥24–28/hr CNY listed (≈ $3.30–$3.85/hr USD) on-demand. Annual subscription: ~$2.10–$2.40/hr USD equivalent.

NVIDIA H100 80 GB SXM (single GPU, on-demand where available)

• AWS p5.48xlarge (8× H100, US regions primary; Singapore availability limited): $98.32/hr → ~$12.29/hr per GPU. APAC availability: intermittent.
• GCP a3-highgpu-1g (1× H100, asia-east1 Taiwan): $8.22/hr on-demand. 1-year CUD: ~$5.21/hr.
• Alibaba Cloud ecs.gn8i series (H100, Singapore/HK): ~$9.50–$11.00/hr USD equivalent on-demand depending on configuration. Annual: ~$6.00–$7.00/hr.

Bottom line on raw compute: For A100 workloads, Alibaba Cloud Hong Kong edges out GCP on annual commitment pricing and is competitive on on-demand. For H100, GCP's asia-east1 (Taiwan) currently offers the best published on-demand rate with more consistent availability than AWS APAC H100 nodes.

Inference Latency: What the Benchmarks Show

Raw GPU cost only matters if latency is acceptable. For production LLM inference (serving Claude Opus 4.8-class 200B+ parameter models or Gemini 3.5 Flash), the relevant metrics are time-to-first-token (TTFT) and tokens-per-second throughput.

• Google's own published data for Gemini 3.5 Flash on Vertex AI claims ~150 tokens/second output throughput at p50 for standard tier—roughly 4× faster than the previous Flash generation, consistent with the GA announcement.
• Third-party benchmark aggregators (Artificial Analysis, as of Q2 2026) show Vertex AI asia-southeast1 delivering TTFT of 350–600 ms p50 for large model inference under moderate load.
• AWS Bedrock (Singapore) for comparable model sizes shows TTFT of 500–900 ms p50, with p99 spiking during peak APAC hours—a known issue with shared inference endpoints.
• Self-hosted on Alibaba Cloud A100 (HK) using vLLM: teams report TTFT of 200–450 ms p50 with proper batching configuration—lower than managed APIs because you control the serving stack.

The Manulife Hong Kong–Alibaba Cloud AI strategic partnership announced this month is a strong signal: a Tier-1 financial institution chose Alibaba Cloud's APAC AI infrastructure over AWS/GCP for latency-sensitive, compliance-heavy AI workloads. That's not a marketing decision—it's an architecture decision.

Egress and Hidden Costs: Where Budgets Break

For an LLM inference service generating 10 TB/month of response traffic to end-users across APAC:

• AWS Singapore egress: 10 TB × $0.085/GB = ~$870/month
• GCP asia-southeast1 egress: 10 TB × $0.08/GB (to most APAC) = ~$820/month
• Alibaba Cloud Singapore egress: 10 TB × $0.07/GB = ~$717/month

Routing inference responses through Cloudflare Workers AI or Cloudflare's network as a CDN/edge cache layer can reduce origin egress by 40–60% for cacheable outputs (embeddings, repeated prompts), bringing effective egress costs below $400/month for the same traffic volume. This is an architecture pattern we actively broker for clients.

Multi-Cloud GPU Strategy: The Broker Advantage

No single hyperscaler wins across all four dimensions—price, latency, availability, and compliance. The emergent pattern for APAC AI teams in 2026 is:

• Primary inference: GCP