Google Cloud Vertex AI
$300 for new accounts; it does not recur.
https://{location}-aiplatform.googleapis.com/v1/projects/{project}/locations/{location}/publishers/google/models/{model}:generateContentModels mentioned
The offer covers a dynamic catalog, provider-selected route, or model class without stable model IDs in this snapshot.
Equivalent paid value
How this was valued: The $300 general Google Cloud credit is spread across its 90-day validity. Eligible Vertex services share it with other cloud usage.
Limits and terms
- General Cloud Credit USD
- 300
- Duration Days
- 90
- Recurrence
- No
What happens to your prompts?
Google Cloud excludes training without permission, but caching, grounding, and session features introduce bounded retention.
- Plan Scope
- Generative AI on Vertex AI, including managed Google and partner models; grounding, caching, live sessions, stateful resources, and abuse-monitoring eligibility differ.
- Prompt Retention
- Stateless inference is not retained at rest by default, but Gemini uses project-isolated in-memory caching for up to 24 hours unless disabled. Grounding with Google Search or Maps stores prompts, context, and outputs for 30 days; session resumption caches content up to 24 hours.
- Response Retention
- Same feature-specific handling as prompts. Explicit context caches, batch files, tuned models, RAG stores, and other customer resources persist until their configured expiry or deletion.
- Ordinary Logging
- Some customers under Google Cloud Platform Terms are subject to prompt logging for abuse monitoring and can request an exception. Service, billing, and security metadata remains.
- Model Training
- Google contractually says it will not use customer data to train or fine-tune any AI/ML model without prior permission or instruction, including GA and pre-GA managed models.
- Product Improvement
- Customer content is excluded from general model improvement absent permission; grounding data may be used for debugging and testing the grounding systems during its 30-day retention.
- Human Or Operator Access
- Abuse and support access follows Google Cloud controls and Access Transparency where available; no universal operator-blind commitment applies to every free or feature path.
- Subprocessors And Routing
- Google Cloud and its subprocessors process data in the configured region or multi-region, while partner models and grounding services can add separate terms and locations.
- Deletion Controls
- Customers can disable in-memory caching, request an abuse-monitoring exception where eligible, avoid stored grounding/session features, and delete explicit caches and other Vertex resources.
- Caveat
- Vertex AI's no-training promise is strong, but achieving zero retention requires disabling or avoiding every documented cache, grounding, session, abuse, and stateful-storage path.
Governing documents
- Cloud Terms
- https://cloud.google.com/terms
- Service Specific Terms
- https://cloud.google.com/terms/service-terms
- Cloud Data Processing Addendum
- https://cloud.google.com/terms/data-processing-addendum
- Privacy Notice
- https://cloud.google.com/terms/cloud-privacy-notice
Eligibility
- New Customer Only
- Yes
- Payment Method Required For Verification
- Yes
Primary sources
Before you build with Google Cloud Vertex AI
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
No stable model ID is recorded. The offer may use a dynamic catalog, a provider-selected route, or a model class, so resolve the current machine-readable ID before writing a fixed production configuration. The recorded base endpoint is https://{location}-aiplatform.googleapis.com/v1/projects/{project}/locations/{location}/publishers/google/models/{model}:generateContent, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
The public evidence does not settle whether an account is required. Confirm the signed-in flow before treating anonymous or credential-free access as available. A payment method is required. A zero-cost allowance can still be useful, but protect the account with provider-side budgets or alerts before sending production traffic. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Partially private”: Google Cloud excludes training without permission, but caching, grounding, and session features introduce bounded retention. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 12 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.