Today, we’re introducing significant updates to how you ingest data into the Gemini API: support for Google Cloud Storage (GCS) object registration, HTTPS/Signed URLs and increased inline file size limits. These changes are designed to help you bring your own data — wherever it lives — directly into Gemini API. There’s no longer a need to re-upload data from existing storage, allowing you to scale your AI applications to production faster.
Bring your own data: GCS and external URLs
Previously, using large files (video, long audio, massive documents) required uploading them to the Gemini Files API, where they persisted for only 48 hours. While sufficient for prototyping, this ephemeral storage became a bottleneck for production apps relying on persistent data in cloud storage platforms.
We are removing this friction with two new input methods.
1. External URLs (public / signed):
We now support both files stored in public domains, as well as private storage (via signed URLs)
- You can pass any publicly accessible URL (like a PDF or image on the web) directly in your generation request.
- We support pre-signed URLs for accessing data from AWS S3, Azure Blob Storage or other cloud providers.
The Gemini API securely fetches the content during processing, eliminating the need to download content to your backend just to forward it to the API.
2. Register GCS files: If your data is already in Google Cloud Storage (GCS), you no longer need to move bytes. You can now register your GCS files directly with the Files API.
Increased inline limits
For developers who prefer using inline files for speed and simplicity, we are increasing the maximum payload size for inline data from 20MB to 100MB (base64 encoded, with slightly varying limits based on data types). This is ideal for prototyping, real-time applications, and handling larger images or short audio clips without needing any intermediate storage.
With these updates, you now have a robust toolkit for data ingestion tailored to your specific needs:
