OCI Generative AI
$300 for new accounts; it does not recur.
https://inference.generativeai.{region}.oci.oraclecloud.com/openai/v1Models 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 OCI signup credit is spread across its 30-day validity. Generative AI shares it with other OCI services.
Limits and terms
- General Cloud Credit USD
- 300
- Duration Days
- 30
- Recurrence
- No
What happens to your prompts?
Ordinary inference is not stored or used for general model or service improvement; persistence is customer-selected.
- Plan Scope
- OCI Generative AI inference and fine-tuning; project Responses, Conversations, files, memory, agents, and other stateful resources have configurable retention.
- Prompt Retention
- Oracle states ordinary model-inference inputs and outputs are not stored inside OCI Generative AI. Project-based agent and OpenAI-compatible resources can deliberately retain responses and conversations under customer-selected settings.
- Response Retention
- Stateless outputs are not stored; project artifacts and agent sessions follow feature-specific retention.
- Ordinary Logging
- OCI retains service, audit, metering, security, and resource metadata under Oracle Cloud terms without treating stateless prompt content as ordinary logs.
- Model Training
- Oracle states customer prompts, responses, knowledge bases, and fine-tuning data are not used to improve general OCI models or services.
- Product Improvement
- Customer content is excluded from general service or model improvement; operational telemetry remains available for cloud operations.
- Human Or Operator Access
- OCI says stateless input and output are not retained or shared with third-party model providers. Saved project resources and support material remain accessible under customer IAM and Oracle Cloud controls.
- Subprocessors And Routing
- OCI does not share prompts, responses, training data, or custom models with underlying third-party model providers such as Cohere, Meta, xAI, or Google Vertex AI.
- Deletion Controls
- Stateless content requires no deletion. Customers manage project response and conversation retention, files, memory, custom models, and Object Storage training data within their tenancy.
- Caveat
- The no-store statement covers ordinary inference, not newer stateful project and agent features. Verify retention settings whenever using Responses, conversations, files, containers, memory, or RAG.
Governing documents
- Cloud Services Agreement
- https://www.oracle.com/contracts/cloud-services/
- Data Processing Agreement
- https://www.oracle.com/contracts/cloud-services/
- Privacy Policy
- https://www.oracle.com/legal/privacy/privacy-policy.html
Eligibility
- New Customer Only
- Yes
- Mobile Phone Required For Most Users
- Yes
- Payment Card Required For Most Users
- Yes
Primary sources
Before you build with OCI Generative 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://inference.generativeai.{region}.oci.oraclecloud.com/openai/v1, 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 “Private”: Ordinary inference is not stored or used for general model or service improvement; persistence is customer-selected. 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 10 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.