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For a new fine-tuning project in 2026, neither OpenAI Platform nor Google AI Studio is a dependable default. OpenAI is winding down its fine-tuning platform and has closed it to new users, while Google AI Studio does not currently support Gemini fine-tuning. Use Vertex AI for managed Gemini tuning, or choose Gemma and open-source tooling when you need portable model weights.
The comparison is between different products
OpenAI Platform means the OpenAI developer API, not ChatGPT, custom GPTs, or ChatGPT subscription features. Google AI Studio is a lightweight environment for trying Gemini models, creating API applications, testing prompts, and collecting evaluation data. It is not the same product as Vertex AI, Google Cloud’s managed platform for tuning and production deployment.
There is also an important distinction between changing a hosted model’s behavior and receiving model parameters. OpenAI and Vertex AI provide provider-hosted tuned models. Gemma is an open-weight option that can be tuned and deployed with independent tools, subject to its license and your infrastructure.
Current availability at a glance
| Requirement | Best current fit |
|---|---|
| Prototype Gemini prompts and compare models | Google AI Studio |
| Build a Gemini API application without tuning | Google AI Studio or Gemini API |
| Start a managed Gemini tuning job | Vertex AI / Gemini Enterprise Agent Platform |
| Continue an existing OpenAI tuning workflow | OpenAI Platform, only if the account remains eligible |
| Download or independently deploy tuned weights | Gemma or another open-weight model |
| LoRA or PEFT with independent deployment | Gemma plus Hugging Face, Unsloth, Axolotl, Keras, or similar tools |
| Changing private knowledge | Retrieval-augmented generation (RAG), databases, or tools |
| Stable formatting, classification, or tool behavior | Fine-tuning, where the selected service supports it |
Google’s documentation explicitly directs supported Gemini tuning to Vertex AI, not AI Studio: Gemini API tuning documentation and Vertex AI tuning documentation.
Can a new user fine-tune through OpenAI Platform?
Usually, no. In an announcement dated May 8, 2026, OpenAI said it is winding down the fine-tuning platform. New users no longer have access; existing fine-tuning-platform users can create jobs only during the transition period OpenAI specifies. Fine-tuned models remain available for inference until their base models are deprecated. Check the announcement at OpenAI’s fine-tuning update before committing a new product to this service.
The API reference still documents fine-tuning endpoints and does not, by itself, prove that your organization can start a job. Check account eligibility, supported models, and organization limits using OpenAI’s current guidance at OpenAI’s fine-tuning help article.
What the legacy workflow looked like
For an account that still has access, the documented pattern is:
- Prepare a JSONL training file in the format required by the selected model.
- Upload it with the
fine-tunepurpose. - Create a fine-tuning job with a supported base model and the returned file ID.
- Monitor the job and evaluate the resulting model on held-out examples.
- Call the resulting hosted model for inference.
curl https://api.openai.com/v1/files
-H "Authorization: Bearer $OPENAI_API_KEY"
-F purpose="fine-tune"
-F file="@training.jsonl"
curl https://api.openai.com/v1/fine_tuning/jobs
-H "Authorization: Bearer $OPENAI_API_KEY"
-H "Content-Type: application/json"
-d '{
"model": "SUPPORTED_BASE_MODEL",
"training_file": "file-EXAMPLE"
}'These are documented API patterns, not a promise that a new account can execute them in 2026. OpenAI’s separate reinforcement fine-tuning billing page lists $100 per hour for the core training loop on o4-mini-2025-04-16, plus model-grader inference charges. That specialized price is not a general supervised fine-tuning price: OpenAI reinforcement fine-tuning billing.
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OpenAI-specific risks
- A shutdown deadline can force a migration while your application is still in production.
- Hosted tuned models do not imply downloadable GPT weights.
- Older tutorials may reference models, dashboard controls, or limits that no longer apply.
Can you fine-tune Gemini in Google AI Studio?
No. Google’s Gemini tuning page, updated April 28, 2026, says that after gemini-1.5-flash-001 was deprecated, no model remained available for fine-tuning through the Gemini API or AI Studio, and Google had no immediate plan to restore that capability: Google’s model-tuning documentation.
What AI Studio is useful for
- Testing system instructions and prompts.
- Comparing Gemini models and building small API applications.
- Collecting representative requests and responses.
- Creating datasets and exporting them as CSV, JSONL, or Google Sheets, as described in AI Studio logs and datasets documentation.
Dataset creation is not model tuning. A practical workflow is to prototype in AI Studio, curate examples, reserve a separate test set, and send a justified tuning project to Vertex AI.
Privacy and billing checks
AI Studio access and Gemini API usage are separate concerns. Gemini API pricing has free and paid tiers that vary by model and account configuration; its pricing page currently shows tuning as unavailable for the relevant Gemini API entries: Gemini API pricing.
Google says billing-enabled projects can retain logs for a default maximum of 55 days, configurable to a shorter period. Sharing datasets with Google can permit their use for product improvement and model training under applicable unpaid-services terms. Review Google’s logs policy before entering personal, regulated, confidential, or tenant-isolated data.
Where Google’s managed Gemini tuning happens
Use Vertex AI / Gemini Enterprise Agent Platform when you need managed Gemini tuning, cloud governance, and production endpoints. Google currently lists these models for supervised tuning:
- Gemini 2.5 Pro
- Gemini 2.5 Flash
- Gemini 2.5 Flash-Lite
- Gemini 2.0 Flash
- Gemini 2.0 Flash-Lite
Preference tuning is listed for Gemini 2.5 Flash and Gemini 2.5 Flash-Lite. Model support, regions, release stages, quotas, and preview terms can change, so verify the live documentation for the model and region you intend to use.
Typical Vertex workflow
- Create or select a Google Cloud project, enable billing, and configure required IAM permissions.
- Prepare a training dataset, commonly in Cloud Storage.
- Initialize Vertex AI in a supported region.
- Start a supervised or preference-tuning job with a supported source model.
- Poll the job until it finishes.
- Retrieve the tuned model and endpoint names.
- Evaluate the tuned model and deploy it under your latency, throughput, region, and compliance requirements.
import time
import vertexai
from vertexai.tuning import sft
PROJECT_ID = "your-project-id"
vertexai.init(project=PROJECT_ID, location="us-central1")
sft_tuning_job = sft.train(
source_model="gemini-2.0-flash-001",
train_dataset="gs://bucket/path/training.jsonl",
)
while not sft_tuning_job.has_ended:
time.sleep(60)
sft_tuning_job.refresh()
print(sft_tuning_job.tuned_model_name)
print(sft_tuning_job.tuned_model_endpoint_name)This sample demonstrates the workflow, not permanent availability of that model name or region: Vertex supervised-tuning sample.
What Vertex adds—and costs operationally
- Google Cloud project, billing account, IAM, quotas, and regional configuration.
- Cloud Storage data management and endpoint administration.
- Production serving choices, including throughput and lifecycle constraints.
- Potential preview or pre-GA terms; review supported-model status.
Vertex pricing is separate from Gemini API pricing. Calculate tuning, storage, endpoint, inference, and any provisioned-throughput charges for the selected region and workload at Google Cloud’s Vertex AI pricing page.
Gemma and self-managed fine-tuning
If “fine-tuning” means owning or exporting the resulting parameters, use an open-weight model such as Gemma rather than a hosted proprietary custom endpoint. Google documents Gemma tuning with Hugging Face Transformers and PEFT, Unsloth, Axolotl, Keras, Google Cloud, and other frameworks: Gemma tuning documentation.
Parameter-efficient methods such as LoRA and adapters can reduce the amount of trainable state, but you still own GPU selection, data pipelines, checkpoint storage, evaluation, serving, monitoring, security, and licensing compliance. Open weights improve portability; they do not make compute or operations free.
When fine-tuning is the wrong tool
Fine-tuning changes learned behavior. It is not a reliable replacement for a document database, search index, or transaction system.
| Problem | Prefer | Reason |
|---|---|---|
| Facts change frequently | RAG, search, database, or function calls | Retrieve current values instead of memorizing stale ones. |
| Private or tenant-specific documents | RAG with access controls | Keep source data external and auditable. |
| Small dataset or rapidly changing requirements | Prompting and structured outputs | Iteration is faster and avoids training a brittle behavior. |
| Current prices, inventory, account data, or transactions | External tools and function calling | Deterministic systems should perform the operation. |
| Stable classification, extraction, formatting, or routing | Supervised fine-tuning | Repeated labeled behavior is a suitable training target. |
| A cheaper model for a stable task | Distillation | Use filtered examples from a stronger model to train or prompt a smaller one. |
Google’s tuning guidance similarly positions prompting for limited labeled data and rapid prototyping, and tuning for specific, labeled tasks where prompting alone is insufficient: Vertex tuning guidance.
The Tool Desk
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- Consistent classification or sentiment labels.
- Entity extraction with a stable schema.
- Short, repeatable transformations and summaries.
- Domain-specific response style.
- Structured JSON generation.
- Stable function-calling behavior.
For tool use, test the complete loop—not only whether the model emits a call. Validate schemas, required fields, argument types, error handling, unnecessary calls, and the follow-up representation of tool results. Google documents function-calling tuning at Vertex function-calling tuning.
A practical selection path
- Need prompt experiments or Gemini API prototyping? Start with Google AI Studio.
- Need managed Gemini tuning? Move to Vertex AI and verify model, region, preview status, and quotas.
- Already have OpenAI fine-tuning access? Confirm transition rights and job-creation deadlines before preparing a large dataset.
- Need downloadable weights or independent hosting? Choose Gemma or another open-weight model with PEFT or LoRA tooling.
- Need changing facts, citations, or private-document search? Build RAG, search, databases, or tools instead of tuning.
- Need stable style, extraction, classification, or formatting? Establish a baseline and evaluation set, then test supervised tuning where supported.
Evaluation and failure prevention
- Normalize demonstrations and remove duplicates or contradictory labels.
- Define one canonical output schema and include boundary and negative examples.
- Split training, validation, and test data before training.
- Measure against production-like and out-of-distribution inputs.
- Compare with an untuned baseline; a subjective impression is not evidence of improvement.
- Watch for overfitting: memorized phrasing, excellent training results, and degraded novel-input performance.
- Check latency, throughput, context length, tool support, region, availability, and compliance before production deployment.
The Bottom Line
Bottom line: Google AI Studio is the right place to prototype Gemini, not to fine-tune it. Vertex AI is Google’s managed tuning route. OpenAI fine-tuning is a transitional capability rather than a sound new-project foundation. If portability and weight control matter, use Gemma or another open-weight model; if the problem is changing knowledge, use retrieval and tools instead of fine-tuning.
