Sold by: Bioptimus
Deployed on AWS
Multimodal foundation model for oncology. M-Optimus-1 turns a routine H&E slide into ~6,000-gene spatial expression readouts. Pre-trained on millions of proprietary H&E whole-slide images (50+ tissues) plus paired patient cohorts with aligned H&E, bulk RNA-seq, and spatial transcriptomics.
Overview
Use M-Optimus-1 to build oncology models from H&E whole-slide images, optionally paired with bulk RNA-seq. Generate multimodal embeddings and reconstruct spatial gene expression across a curated panel of 6,000+ genes.
M-Optimus-1 is a multimodal foundation model for oncology, learning cross-modal representations across H&E, bulk RNA-seq, and spatial transcriptomics. Pre-trained using a unique proprietary dataset comprising millions of H&E whole slide images from more than 50 organ tissues, and thousands of patient records where H&E, bulk and spatial transcriptomics are paired and aligned.
Deploy M-Optimus-1 as an Amazon SageMaker model package inside your AWS account. Run real-time inference via SageMaker endpoints or batch inference on S3. Your data stays private, and is never accessed by Bioptimus.
Highlights
- M-Optimus-1 is a multimodal, multiscale foundation model for oncology that integrates information across H&E, bulk RNA-seq and spatial transcriptomics (10x Genomics Visium) to produce tile-, slide, and patient-level outputs.
- M-Optimus-1 can perform direct prediction from H&E (with optional bulk RNA-seq) to spatial expression across a curated pan-tissue panel of over 6,000 genes - tumor and immune cell biomarkers, cell-cell interactions, ligands and receptors, biological pathways, and drug candidates.
- M-Optimus-1 is multimodal by design, flexible in deployment. H&E-only inference still outperforms unimodal baselines when sequencing data is unavailable.
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $3,000.00/host/hour |
ml.g5.2xlarge Inference (Batch) | Model inference on the ml.g5.2xlarge instance type, batch mode | $3,000.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.01/request |
Dimensions summary
You pay based on usage, with three pricing dimensions. Two are host-hour charges for running batch inference on the model. One covers the ml.g5.xlarge instance type, and the other covers the larger ml.g5.2xlarge instance type. You pick the instance size that fits your workload, and you pay for each hour the host runs. The third dimension charges per inference request, so cost scales with the number of predictions you make. There is no upfront commitment; charges accrue as you use each option.
Top-of-mind questions for buyers
Each request meters one prediction run through the model. The model reads a routine H&E slide, and optionally adds bulk RNA-seq when available, then predicts spatial gene activity across thousands of genes. One submitted prediction task counts as one billable request.
The host-hour dimensions meter each hour the ml.g5.xlarge or ml.g5.2xlarge instance runs. Charges accrue while the host is active. If you stop the host, software host-hour charges stop, though underlying AWS storage fees may still apply separately.
The two charge types bill independently and appear together. You pay host-hours for the time your chosen instance runs batch inference, plus a separate charge per inference request. Longer-running batch jobs push host-hour cost up, while request volume drives the per-request cost.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
v1.0.1
- Minor stability fixes
Additional details
Inputs
- Summary
Required: H&E whole-slide images at 224×224 px tiles, 0.5 MPP. Optional: H&E tiles at 512x512 px 8 MPP for tissue segmentation model.
Optional: paired bulk RNA-seq (.csv / .tsv / .txt / .h5ad). Exact Ensembl IDs match to the supported Ensembl ID list (see sample input data for details). All inputs should be base64 encoded json files. Inputs can contain multiple tiles at a time (jsonl format), however the total file size should not exceed 100MB for batch jobs and 6MB for real-time jobs.
- Limitations for input type
- Inputs must be in the specified json format with base64 encoded images. Please see sample input files for details. **Input MIME type:** application/json
- Input MIME type
- application/json
https://github.com/bioptimus/m-jumpstart/tree/main/data/input/real-time
https://github.com/bioptimus/m-jumpstart/tree/main/data/input/batch
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
image_data | Base64-encoded PNG of the tile | Type: string | Yes |
slide_name | Identifier for the source slide | Type: string | Yes |
x | Tile x-coordinate in the slide | Type: int | Yes |
y | Tile y-coordinate in the slide | Type: int | Yes |
width | Tile width in pixels | Type: int | Yes |
height | Tile height in pixels | Type: int | Yes |
patch_idx | Index of this tile | Type: int | Yes |
model_name | Name of the model must be "m-optimus" | Type: string | Yes |
mode | "prediction" (for gene expression) or "embedding" | Type: string | Yes |
bulk_rna | Bulk RNA counts vector. If omitted, the server substitutes zeros. Must be in the exact gene order from the model's input_gene_set.csv. Pre-processed as log1p before passing to the model - so this should be raw counts. | Type: list[float] | No |
Resources
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Support
Vendor support
Contact: support@bioptimus.com
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