Start with a hosted API for the exact vision-language model if it accepts the image shape you use. Compare Hugging Face Inference Endpoints, Baseten, Replicate or Modal when you need to pin the model, own preprocessing or deploy a custom artifact. Together and Fireworks are worth evaluating for managed catalog or dedicated inference if the model revision and image capabilities you need are documented for the actual endpoint.

This guide covers models that accept an image and produce text, such as visual question answering or document extraction. A provider serving an image generator does not establish support for image input on a text-completion model. We compared provider documentation and did not run model-quality or latency tests.

Compare the deployment path

Platform	Why shortlist it	Pricing and qualification
Hugging Face	Unified provider access or a dedicated managed endpoint for a selected VLM	Catalog route and provider rates versus dedicated instance time
Together or Fireworks	Managed inference when the exact multimodal model is available	Model-specific image accounting, limits and supported deployment mode
Baseten	Packaged runtime or custom processing with Truss	Hardware, minimum replicas and engineering effort
Replicate	Model-specific input schema or a custom Cog deployment	Model pricing or instance time and availability of the desired version
Modal	Build and serve your own model runtime on managed compute	Compute charges, scaling behavior and ownership of the serving code

Hugging Face for a documented dedicated path

Hugging Face’s image-text-to-text documentation describes the task and compatible provider workflow. Its dedicated endpoints cookbook demonstrates a VLM deployment separately. Determine whether you are calling a shared routed provider or managing your own endpoint. Verify the engine supports your model’s vision encoder and processor. A model repository with open weights can still require license acceptance, specific dependencies or an inference engine other than the default.

The hosted API may be enough if you want a fast proof of concept. The dedicated path is useful if your evaluation is tied to a model revision, quantization or image transformation. Check the option to supply a custom container if the managed engine lacks a requirement. Treat the code and any custom image as operational work.

Together or Fireworks for an available catalog model

Together’s managed open-weight inference overview and Fireworks’ inference documentation are starting points. Open the exact model listing, confirm that the endpoint accepts images and test the protocol. A model family may include both a text-only variant and a VLM. Some endpoints accept a URL, others accept embedded bytes or a provider-specific upload. Confirm whether mixed text and images, multiple images and structured text output are available on the same API route.

If privacy is important, decide where URL fetching happens and what is retained. A private signed URL can expire before a queued request starts, and a long-lived URL can spread access farther than intended. Ask how size limits, fetch errors and asynchronous processing affect the design. If considering Cerebras for this job, confirm support for the exact image-capable endpoint rather than inferring it from text-model throughput.

Baseten, Replicate and Modal for custom processing

Baseten Truss lets you package your chosen inference framework. Replicate’s Cog workflow and Modal’s inference platform are relevant when you want to own input transforms, batching or the endpoint schema. Test the model with realistic image sizes, the GPU class and your production concurrency. Replicate’s public model interfaces may differ from your custom deployment, so inspect the exact schema and version.

Custom preprocessing helps when a pipeline must consistently crop pages, normalize orientation or convert color space. It can also become a silent source of errors. Save enough information to reproduce the input seen by the model. When text is very small, downscaling that makes a general scene classification faster can make document extraction worse.

Run a representative vision evaluation

1. Choose a fixed set of sample files with known answers. Include dense tables, small print, orientation changes and images the model should refuse or flag as unreadable.
2. Send the same samples using the supported input types. Record upload or fetch failures separately from model errors and invalid output.
3. Measure the field or task accuracy you need. An answer that looks plausible can still misread a digit or drop a row. Check schema validity and units independently.
4. Repeat at realistic image sizes and concurrency. Inspect queue time, image processing, output generation and billed units. Compare cost per usable result, including retries.
5. Pin the artifact and runtime versions. Try rolling back after a model or preprocessing update and confirm that the evaluation set produces comparable results.

My take

Pick the least operationally complex endpoint that reproduces your evaluation results and complies with your input-handling requirements. Use a catalog API for an initial controlled test. Move to a dedicated runtime when you need stronger version control, custom preprocessing, consistent capacity or a supported network boundary. Re-run the evaluation after changes to the model or resize settings.

Related Articles

• DeepSeek Vision guide
• DeepSeek Vision for OCR and documents
• How to use a vision API in Python
• Self-hosting a vision model
• Benchmarking inference

FAQ

Is open-weight the same as unrestricted commercial use?

No. Read the license and any usage restrictions for the exact checkpoint and any additional components.

Can I send a PDF directly?

Verify the endpoint schema. Some image-capable routes expect rendered pages as images. A document endpoint may have a separate upload and extraction path.

Are images always billed as input tokens?

Check the particular provider and model. Billing can depend on visual tokens, model-specific image accounting or compute time. Compare invoices using representative files.

Will an OpenAI-compatible route accept the same image formats everywhere?

Compatibility can vary by model and provider. Check supported URL or embedded formats, size limits, multi-image support and error responses on the actual endpoint.