Google Cloud Vertex AI Releases Gemini 2.5 Ultra with Committed Use Discount Pricing

Google Cloud announced the general availability of Gemini 2.5 Ultra on Vertex AI in March 2026, marking a significant milestone as the first frontier AI model on GCP to carry Committed Use Discount (CUD) pricing parity with traditional compute resources. The model is available in all major Vertex AI regions including us-central1, us-east4, europe-west4, and asia-northeast1, with pay-as-you-go rates set at $7.00 per million input tokens and $28.00 per million output tokens. For enterprises willing to commit to a minimum throughput tier over a 1-year term, Google is offering CUD pricing at $4.20 per million input tokens and $16.80 per million output tokens — a flat 40% reduction across both dimensions. The model supports a 1-million-token context window, multimodal inputs including text, image, video, and audio, and is accessible via the Vertex AI API using the gemini-2.5-ultra-001 model SKU. Batch inference jobs qualifying under the Vertex AI Batch Prediction service receive an additional 20% reduction on top of standard pay-as-you-go rates, bringing batch input costs to $5.60 and output to $22.40 per million tokens without any commitment.

Placing this pricing in competitive context reveals a meaningful shift in the frontier AI cost landscape. Azure OpenAI's GPT-5 (gpt-5-turbo-128k SKU) is currently priced at approximately $10.00 per million input tokens and $40.00 per million output tokens on standard deployment, with no equivalent committed use tier publicly available as of this writing. Anthropic's Claude 4 Sonnet via Amazon Bedrock runs at $3.00 per million input and $15.00 per million output on on-demand, but the model sits a tier below Ultra-class capability in most benchmark comparisons, making it a different performance category. AWS Titan Premier on Bedrock prices at roughly $1.30/$1.70 per million tokens but lacks comparable reasoning depth for complex enterprise tasks. Meta Llama 4 Ultra hosted on Azure AI Foundry is available at approximately $2.80/$11.20 per million tokens but requires substantially more prompt engineering overhead for production-grade outputs. Against the true apples-to-apples frontier tier — GPT-5 on Azure — Gemini 2.5 Ultra's CUD rate represents a 58% reduction on input and 58% reduction on output tokens, a cost differential that becomes material at scale.

This announcement matters most to three customer segments: large enterprise teams running high-volume document processing, legal discovery, or financial analysis pipelines exceeding 2 billion tokens per month; ISVs and SaaS vendors embedding AI capabilities into their products who need predictable unit economics for margin modelling; and organisations already deep in the GCP ecosystem with active compute CUDs who can negotiate unified committed spend arrangements through their Google Technical Account Manager. The competitive pressure on Microsoft Azure and AWS is immediate — both hyperscalers will face customer questions about equivalent CUD or Reserved Capacity structures for their frontier model tiers before mid-2026. The primary caveat is lock-in: a 1-year commitment to a specific throughput tier on a single model SKU carries model obsolescence risk, particularly in an environment where Gemini versioning has historically moved quickly. Regional availability constraints also mean workloads requiring data residency in APAC-South or MEA zones cannot currently access CUD pricing, which limits applicability for regulated industries in those geographies.

Enterprise FinOps and cloud architecture teams should act on a structured two-step process before committing. First, pull 90 days of Vertex AI token consumption logs from Cloud Billing BigQuery exports and calculate your p50 and p75 monthly token throughput baselines separately for input and output. If your p50 monthly output token volume exceeds 1.5 billion tokens, the 1-year CUD breaks even within the first billing cycle and generates positive ROI from month one. If you are currently on Azure OpenAI GPT-4 Turbo or GPT-5 at standard rates and running more than 500 million tokens per month, model a migration scenario to Gemini 2.5 Ultra CUD — the blended savings on output tokens alone typically exceed $80,000 per month at that volume. Negotiate with your Google TAM to stack the AI CUD against existing compute committed spend floors; Google has indicated eligibility for an additional 8–12% portfolio discount for accounts with combined committed spend exceeding $2 million annually. Set a 30-day deadline to complete this analysis before CUD allocation windows for Q2 2026 close.

At TCOIQ, we have already updated the TCO Calculator at tcoiq.com/tco.html to include Gemini 2.5 Ultra CUD and pay-as-you-go line items alongside GPT-5, Claude 4, and Llama 4 Ultra for side-by-side frontier model cost modelling. Cloud architects can use the Inventory Builder at tcoiq.com/inventory.html to import existing Vertex AI usage exports and automatically flag token volumes that cross the CUD break-even threshold. Our AI Migration Assessment module models the full cost and effort of shifting inference workloads from Azure OpenAI or Bedrock to Vertex AI, including egress, prompt re-engineering hours, and latency benchmarking costs that are often invisible in headline token price comparisons. For organisations evaluating Vertex AI as a primary AI platform, the Landing Zone Assessment validates whether your current GCP organisation structure supports the IAM, VPC Service Controls, and CMEK configurations required for enterprise Vertex AI deployments. The single most impactful next step for any enterprise consuming more than 500 million tokens monthly is to run a Vertex AI CUD Scenario in the TCOIQ TCO Calculator today and generate a shareable PDF report for your CFO and procurement team before Q2 commitment windows open.