Key facts
| Vision input | Image understanding runs on the OpenAI-compatible chat completions endpoint with supported multimodal models |
| Example model | plugsky-gemma-4 handles text plus image inputs such as screenshots, forms and product photos |
| Model access | 30+ models from efficient to frontier tiers behind one key |
| Dedicated media endpoints | Image generation and editing endpoints are labelled coming soon |
| Deployment | Hosted, VPC, on-prem and air-gapped options on Enterprise |
| Data handling | Data encrypted at rest and in transit; API data is not used to train models |
| Audit | Per-request logs with model, tokens, latency and user; SIEM export; retention up to 7 years |
| Free plan | plugsky-micro and plugsky-lite, no card; 14-day full-access trial |
TL;DR
- Turn uploads into structured features with one vision call.
- Queue extraction asynchronously; stream only when a human is waiting.
- Resize images and escalate only hard cases to controlling cost.
- Keep tenant isolation explicit with server-side scoped keys and quotas.
- Measure correction rates as the real quality signal.
How it works, step by step
- Pick one upload workflow with a clear structured output.
- Define the JSON schema and validation rules for extracted fields.
- Build an async pipeline: upload, queue, extract, validate, update, notify.
- Resize images before sending and add retries with backoff.
- Show extraction confidence and a retake path for failed images.
- Route simple documents to an efficient multimodal model; escalate hard cases.
- Add per-tenant keys, quotas and audit logging before general availability.
Original data
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Why SaaS teams need image understanding
Your users already upload images; the feature gap is what happens next. A receipt photo becomes expense fields, a screenshot becomes a bug report, a delivery note becomes inventory data. Vision turns an upload box into a product feature without asking users to type anything twice.
Design for real-world images: bad lighting, odd angles, multiple documents in one frame. That means a validation step, a confidence threshold and a review path when extraction fails, plus clear feedback so users can retake the photo rather than losing trust in the feature.
How vision works on the Plugsky API
Treat vision as a tiered pipeline rather than one model. An efficient multimodal model such as plugsky-gemma-4 reads the common cases — screenshots, forms, labels, product photos — and a frontier model handles ambiguity, dense layouts or long documents. Both are called through the same OpenAI-compatible chat completions endpoint, with image content in the message, and both support streaming and JSON mode. Image generation and editing endpoints are labelled coming soon; understanding is live today. For SaaS teams, the call belongs in an async worker — upload, extract, validate, update — with streaming reserved for interactive features.
The controls SaaS teams should configure first
Run extraction asynchronously. Upload the image, queue a job, call the OpenAI-compatible vision completion with a JSON schema, validate server-side, then update the record and notify the user. Streaming helps when a human is watching; queues help when the workflow is batch. Both use the same endpoint.
Keep tenancy explicit: a scoped key per tenant held server-side, per-key quotas so one account cannot flood the pipeline, and audit logs with user, model and region for support and compliance questions. Resize images before sending to control token usage, and route simple documents to an efficient multimodal model while escalating only hard cases. Image generation endpoints are labelled coming soon.
A practical pilot path
Ship one job-to-be-done — for example receipt-to-expense — behind a queue, measure extraction accuracy and user correction rates, then add document types. Prototype on the free plan, then scale on a flat monthly plan. See the live pricing page for current plans.
Honest comparison
| Capability | Plugsky | Vision-only API vendor | Building in-house |
|---|---|---|---|
| Image understanding | Live on supported multimodal chat models | Usually the core product | You host and serve vision models |
| Image generation | Labelled coming soon | Often available today | Custom deployment work |
| API shape | OpenAI-compatible chat completions | Vendor-specific SDK | You standardise it yourself |
| Model choice | 30+ models behind one key | Narrower catalogue | One model per integration |
| Deployment | Hosted, VPC, on-prem and air-gapped | Usually hosted only | Your infrastructure only |
| Controls | Region pinning, BYOK, per-request audit logs, SIEM export | Varies by vendor | You build the pipeline |
Frequently asked questions
Should extraction be synchronous or queued?
Queue it unless a user is actively waiting. Async jobs absorb load spikes, allow retries and keep your API latency stable; use streaming only for interactive description features.
How do we keep image costs under control?
Resize before sending, choose an efficient multimodal model for common documents, and escalate only failures. Flat monthly self-serve plans also remove per-token variability from your bills.
Do we have to change our application code?
No. Image understanding uses the same OpenAI-compatible chat completions endpoint, so your SDK, auth and error handling carry over; the message content simply includes an image.
Is there a free plan?
Yes — plugsky-micro and plugsky-lite with 2 API keys and no credit card. A 14-day full-access trial is also available.
How is pricing structured?
Self-serve plans are flat monthly with unlimited fair-use usage and no per-token charges or overage fees. See the live pricing page for current plans.
Is image generation available?
Not yet — image generation and editing endpoints are labelled coming soon. Image understanding is live today on supported multimodal chat models.