Google Cloud Introduces Gemini 2.5 Ultra Batch Inference at 60% Discount vs. Real-Time API

Google Cloud formally launched asynchronous batch inference for Gemini 2.5 Ultra on Vertex AI in May 2026, introducing a pricing structure that delivers a 60% reduction versus the synchronous real-time API. The batch inference endpoint is priced at $2.80 per 1 million input tokens and $8.40 per 1 million output tokens, compared to the real-time API rates of $7.00 per 1 million input tokens and $21.00 per 1 million output tokens. Batch jobs are submitted as JSONL files accommodating up to 50,000 requests per file, with results returned within a 24-hour SLA. The service is available through Vertex AI in us-central1, us-east4, europe-west4, and asia-southeast1 at launch, with additional regions expected by Q3 2026. Notably, Committed Use Discounts (CUDs) are explicitly excluded from batch inference SKUs, but Google argues the flat 60% discount already exceeds the maximum 20–25% savings available under any CUD tier for Gemini models.

Positioning this against equivalent offerings from other hyperscalers reveals just how aggressive Google's batch pricing is for a frontier-class model. Azure OpenAI's global batch endpoint for GPT-4o (version 2024-11-20) runs at approximately $1.25 per 1 million input tokens and $5.00 per 1 million output tokens, but GPT-4o is widely considered a tier below Gemini 2.5 Ultra in reasoning benchmarks and context window depth. Azure's equivalent frontier model, o3, does not yet have a published batch endpoint pricing tier as of May 2026. AWS Bedrock batch inference for Anthropic Claude 3.5 Sonnet is priced at $1.50 per 1 million input tokens and $7.50 per 1 million output tokens, also a sub-frontier tier relative to Gemini 2.5 Ultra. IBM watsonx and Oracle OCI Generative AI do not publish comparable batch inference endpoints for models at the Gemini 2.5 Ultra capability level. When normalized for model capability tier and context window (Gemini 2.5 Ultra supports a 1 million token context window versus 128K–200K for most Azure and AWS frontier equivalents), Google's batch pricing represents the most cost-efficient path to running frontier-class inference at scale among all major hyperscalers today.

This announcement matters most to three customer segments: enterprises running nightly document intelligence pipelines (legal, insurance, financial services), platform teams building RAG ingestion workflows that process hundreds of thousands of documents per cycle, and AI product teams running large-scale offline evaluation and red-teaming workloads. For these use cases, the shift from synchronous to batch inference can reduce monthly AI compute costs by 50–65% without any change to model quality or output format. The competitive pressure on Microsoft and AWS is significant — both will face customer questions about when o3 and Claude 3.7 will receive equivalent batch pricing at frontier tiers. The primary caveats are the 24-hour SLA, which disqualifies any latency-sensitive or user-facing workload, the absence of CUD stacking, regional availability limited to four zones at launch, and the risk of Vertex AI lock-in for teams that build batch pipelines deeply integrated with BigQuery or Cloud Storage output destinations.

Customers currently running Gemini 2.5 Ultra real-time API workloads should immediately audit which of those jobs are truly latency-sensitive versus which are batch-compatible. Any pipeline with a same-day or next-day delivery window — document summarization, contract extraction, RAG chunk embedding, offline scoring — is a strong migration candidate to the batch endpoint. Organizations processing more than 500 million tokens per month at real-time API rates of $7.00/$21.00 should prioritize this migration, as the savings at that volume exceed $25,000 per month in input token costs alone. Teams should submit a test batch job of at least 10,000 requests within the next 30 days to validate output quality, latency distribution, and integration with downstream Cloud Storage or BigQuery sinks before committing full workload migration by end of Q2 2026.

TCOIQ's platform is purpose-built to surface exactly this type of optimization opportunity before it becomes a budget surprise. The TCOIQ Inventory Builder at tcoiq.com/inventory.html can map your existing Vertex AI API call patterns, token volumes, and workload schedules to identify which real-time Gemini API workloads are batch-eligible based on their SLA tolerance. The TCO Calculator at tcoiq.com/tco.html can model the before-and-after cost delta with your actual monthly token volumes, factoring in both the batch discount and the absence of CUD stacking to produce a net savings projection. The AI Migration Assessment generates a readiness score for moving synchronous pipelines to async batch architecture, including dependency mapping for downstream consumers. As a concrete next step, upload your current Vertex AI billing export into the TCOIQ Inventory Builder to receive a ranked list of batch migration candidates with projected monthly savings within 24 hours.