Google Vertex AI pricing in 2026: Gemini API rates, every model, and what it really costs

On August 13, 2026, Google launched Gemini 3.7 Flash at $0.75 per million input tokens, half the price of Gemini 3.6 Flash, which had shipped three weeks earlier.

Then Google cut 3.6 to the same price, so nobody who’d just adopted it would feel silly.

Generous, right? Read the footnote. Both rates are introductory, and on January 1, 2027, both double to $1.50/$7.50. Google gave the market a half-price holiday season with a New Year’s Day invoice built in, and every budget set on August’s rates will quietly be wrong by 2x in twenty weeks.

That footnote is Vertex AI pricing in one lesson: the rates are public, the movement is constant, and the traps are in the fine print. Models launch monthly, retire quarterly (the entire 2.0 family died June 1; the 2.5 family retires mid-October), and prices can halve or double on a date Google picked in advance.

The stakes aren’t academic. In CloudZero’s 2026 AI ROI survey of 260 finance leaders, 64% said tying AI spend to outcomes would fundamentally change how they invest, and only 16% disagreed. Two in five want that capability and do not have it today. On a platform that reprices this often, that ability starts with knowing what today’s rates actually are.

What is Vertex AI, and how is it different from the Gemini API?

Vertex AI is Google Cloud’s machine learning and generative AI platform: training, deployment, inference, agents, and access to Gemini plus 200+ third-party models (Anthropic’s Claude, Meta’s Llama, Mistral) in one place. It’s mid-rebrand into the Gemini Enterprise Agent Platform, consolidating Vertex AI and Agentspace. The billing mechanics haven’t changed. The marketing deck has.

The two-door structure explains most pricing confusion. Gemini is the model family.

Door one: Google AI Studio and the Gemini Developer API, simple, generous free access, minimal enterprise controls (google ai studio pricing for the paid tier matches the token rates below; the Studio itself is free).

Door two: Vertex AI, same models at the same google gemini api pricing per token, wrapped in SLAs, Provisioned Throughput, VPC Service Controls, and compliance certifications, plus a dozen extra billing surfaces for the platform around them. When people search gemini enterprise pricing, this wrapper is what they’re actually pricing.

If you’ve used Amazon Bedrock or Azure OpenAI, the pitch is familiar. Vertex’s edge is data gravity: it wires straight into BigQuery, Cloud Storage, and Dataflow, which matters when the AI workload lives where the data already does.

The models are where everyone starts, so let’s start there, with the table most guides on this search still have wrong.

What does the Gemini API cost on Vertex AI right now?

Here is the Google Gemini API pricing table, per million tokens, standard paid tier:

Note: The 2.0 family shut down June 1, 2026

Five dynamics turn that table into your invoice.

And one genuine Gemini advantage worth naming: every model, down to the cheapest Flash-Lite, carries a 1M-token context window. Rivals hit 1M only at their frontier tiers. For long documents, codebases, and transcripts, that changes the math more than any per-token headline.

Model tokens are the meter everyone watches. The ones nobody watches come next.

What do Vertex AI agents, search, and RAG cost?

Here’s the arithmetic that decides whether Vertex costs you hundreds or hundreds of thousands: how many meters start when one user asks one question. On an agent, routinely four.

Retrieval bills per thousand queries. Reasoning bills as thinking tokens. The answer bills as model tokens. The memory of the conversation bills per event and per vCPU-hour. Four SKUs, one question, and at 100,000 questions a day the difference between knowing that and not knowing it is most of your bill.

The Vertex AI Agent Builder pricing and vertex ai agent engine pricing meters:

Sessions billing began February 11, 2026 (free tier: 50 vCPU-hours and 100 GB-hours monthly). Vertex ai search pricing alone runs a 100,000-query-per-month knowledge base $400-$600. Vertex AI Vector Search bills for index-hosting nodes per hour, traffic or not, roughly $700-$800 monthly for a moderate index on three replicas, and the Vertex AI RAG Engine wraps embedding, retrieval, and generation with separate charges for each.

One confusion worth its own paragraph: Agent Builder uses Vector Search underneath but bills at Agent Builder query rates, while custom RAG pipelines pay Vector Search infrastructure directly. Different prices, overlapping functionality. Pick one path. Paying for both is how a bill doubles without the value doubling.

Inference is the steady spend. Training is the spiky one.

What do training, GPUs, and fine-tuning cost?

Vertex AI GPU pricing bills custom training per node-hour: an A100 40GB around $2.93/hour on-demand (~$3.37 with the Vertex management fee), an H100 80GB around $10-$12, TPU v5e by arrangement. Eight H100s for 24 hours is roughly $2,164; multi-day experiments clear $10,000 each. Spot VMs cut 60-91% on interruptible runs, and committed use discounts take ~30% off at one year, ~50% at three, the same commitment math as everywhere else.

Fine-tuning carries a detail Google added quietly: tuning bills per training token, and from Gemini 3 onward, tuned model endpoints predict at 1.5x the base model’s price. Older tuned models (2.5 and earlier) predict at base rates.

Fine-tune the new generation and every inference afterward costs half again more, a multiplier that belongs in the business case before the training job starts, not after.

Vertex ai workbench pricing is honest but relentless: notebooks are Compute Engine VMs at standard rates, ~$0.077/hour for the default, ~$0.40/hour more with a T4, running whether you’re coding or at lunch. Every open notebook is a running meter.

Vertex AI feature store pricing adds per-node-hour serving plus storage, and vertex ai veo pricing puts generated video at $0.50/second ($0.75 with audio; Veo 3.1 Fast from $0.10), so one 8-second clip with audio costs $6.00, more than thousands of text calls. A fun discovery to make from the invoice.

Which is exactly why everyone asks the next question first.

Is Vertex AI free to use, and what changed in April?

Partially, through three doors, and the doors narrowed this year. The $300 free trial credit covers any GCP service for 90 days. Express Mode opens Vertex AI Studio and Agent Builder with limited quotas, no billing enabled.

And the Gemini API free tier through AI Studio remains the most generous prototyping path in the industry. The Gemini API free tier limits 2026 picture, since everyone asks: with one big change, since April 1, Pro models are off the free tier entirely.

Free access now means Flash and Flash-Lite models at reduced daily and per-minute quotas, plus 5,000 free Google Search grounding prompts monthly on the 3.x family. That’s the whole Gemini free tier 2026 deal, and the Gemini API free tier 2026 limits are the reason “free” and “production” are different words.

The strategy behind the generosity hasn’t changed: Google prices the on-ramp at zero and the enterprise wrapper at meter rates. Free proves concepts. Production pays. Budget for the day those two facts meet.

And one more thing runs on Vertex that most guides skip entirely, and it happens to be our specialty.

Can you run Claude models on Vertex AI, and what do they cost?

Yes. The Google Vertex AI pricing Anthropic Claude models question deserves better coverage than it gets: Vertex’s Model Garden serves Anthropic’s Claude family at parity with Anthropic’s direct rates, Claude Sonnet 4.6 at $3/$15 per million tokens, Opus 4.8 at $5/$25, Haiku 4.5 at $1/$5, billed through your GCP account with Vertex’s enterprise controls around them.

That makes Vertex one of three clouds serving Claude, and the cross-cloud view is a table nobody else keeps in one place:

The strategic read: shared-model rates converge across clouds, so the real decision is platform meters, data gravity, and commitments. The full field sits in the LLM API pricing comparison, Claude’s family detail in the Claude pricing guide, and the AWS door in the Bedrock guide.

Now the part no pricing page will tell you.

What hidden costs inflate a Vertex AI bill?

Every number above has a pricing page. These don’t.

None of these are scandals. They’re charges without headlines, and the cure for all of them is the same discipline.

How do you reduce Vertex AI costs?

In payoff order. Route to the cheapest adequate model: Flash-Lite for extraction and classification, Pro only where reasoning earns $12-18 per million output; Google’s Model Optimizer automates the routing, and routing is consistently the largest single saving in AI cost optimization work.

Batch everything non-urgent: 50% off, taking 2.5 Pro to $0.625/$5 and 3.1 Pro Preview to $1/$6. Cache aggressively: reads cost ~10% of base input (storage $1-4.50 per million per hour), the highest-ROI switch on the platform if you reuse prompts or preambles.

Then the infrastructure side. Endpoints scale to zero when idle. Commitments sized to the measured GPU floor, never the roadmap. thinking_level defaults to “medium.” Migration calendar on the wall: October 16 for the 2.5 family, January 1 for the Flash repricing, because on this platform, dates are prices.

And the step that makes the rest possible: billing budgets catch aggregate spikes, but aggregate alerts can’t say which team, feature, or experiment did it. SKU totals aren’t answers. They’re the start of an investigation nobody has time to run monthly, which is the gap the last section closes.

How does CloudZero connect Vertex AI spend to business outcomes?

Google’s pricing pages give list prices. The billing console gives SKU totals. Neither answers what engineering leaders and CFOs actually get asked: what is AI costing per customer, per product, per feature, and is it returning more than it consumes?

CloudZero connects to Google Cloud and maps every Vertex line item, inference, training, search, agent runtime, Workbench, Feature Store, to teams, products, features, and cost per customer, allocating 100% of spend including the shared and untaggable resources native tools can’t attribute.

The AI Hub then puts Gemini-on-Vertex beside Claude on Bedrock, Azure OpenAI, and direct Anthropic spend, CloudZero was the first cost platform with a direct Anthropic integration, normalizing everything into one cost-per-inference view across every model and cloud.

Anomaly detection catches the runaway training job and the forgotten endpoint before the invoice does. Budgets put forward numbers on allocated AI spend, including the January repricing before it lands. Dimensions build the unit metrics, cost per session, per feature, per customer, that turn “Vertex AI API: $23,847” into a sentence a board accepts.

That’s the survey’s headline finding answered in practice: tying spend to outcomes changes how you invest, and this is the tying.

CloudZero manages $14 billion+ in cloud and AI spend for organizations including Upstart, PicPay, Duolingo, Coinbase, and Skyscanner. See your Vertex spend allocated live in a demo.

Frequently asked questions about Vertex AI and Gemini API pricing

Author Spotlight

Lyne Carolyne

Lyne Carolyne has several years of experience in AI and cloud economics and brings that understanding into the content she creates. Outside work, she's an avid explorer.