Key facts
| API compatibility | OpenAI-compatible chat, embeddings and function calling |
| Use cases | Catalogue enrichment, product Q&A, review summaries, support triage |
| Scale | Flat fair-use self-serve pricing for seasonal peaks and catalogue sweeps |
| Data controls | Scoped API keys, RBAC, SSO, audit logs and region selection |
| Deployment | Plugsky cloud, your VPC, on-prem and air-gapped options |
| Models | 30+ models from free aliases to frontier reasoning |
| Free tier | plugsky-micro and plugsky-lite on the free plan, no card required |
| Product status | Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live |
TL;DR
- Enrich thousands of SKUs with consistent attributes and descriptions.
- Answer product questions with retrieval over specs, manuals and policies.
- Summarise review themes into merchandising and quality signals.
- Triage support tickets by intent and route to the right queue.
- Keep payment data out of prompts and scope keys per channel.
How it works, step by step
- Pick a bounded catalogue segment and define the attribute schema you want filled.
- Enrich product records in batches and sample-check accuracy before publishing.
- Index specs, manuals and policies so product Q&A cites the source.
- Triage support tickets by intent, sentiment and order topic, then route to queues.
- Scope keys per sales channel and configure region, retention and audit logging.
- Track deflection rate and catalogue completeness, then expand category by category.
Try it yourself
Open the AI model pricing calculator →
Retail use cases that pay back quickly
Retail is a catalogue plus a support queue, and both are text-heavy:
- Catalogue enrichment: fill attributes, generate descriptions and normalise categories across thousands of SKUs.
- Product Q&A: answer compatibility, sizing and care questions from specs and manuals with citations.
- Review summarisation: cluster feedback into themes such as fit, durability or delivery.
- Support triage: classify intent, urgency and order topic, then route to the right queue.
Product and pricing statements should stay grounded in approved data, never model memory.
Security, privacy and payment data
Customer identity, order history and payment details are the sensitive assets. Keep payment card data out of prompts entirely, and send order references rather than full account records. Configure scoped API keys per channel, RBAC and SSO for staff, and audit logs for attribution. Choose the processing region for each market and set retention deliberately, because retail often operates under several privacy regimes at once. When a market or partner requires local processing, move that workload to a VPC, on-prem or air-gapped deployment using the same compatible API.
Architecture for catalogue and support
A workable pattern separates batch from interactive work. Catalogue enrichment is a batch job that writes into your PIM with a human review gate; product Q&A is a retrieval service over specs, manuals and policies; support triage is a classification call in front of your helpdesk. Use plugsky-embed for retrieval and a small model for classification, reserving a stronger model for customer-facing answers. Because Plugsky is OpenAI-compatible, all three run on the same SDK and can move between deployments without code changes.
Peaks, pilots and pricing
Start on the free plan with plugsky-micro or plugsky-lite on one category, then use the 14-day full-access trial to benchmark frontier models on customer-facing copy and Q&A. Metrics to track: catalogue completeness, Q&A deflection, misroute rate and review-to-insight time. Self-serve plans are flat monthly with unlimited fair-use usage, so seasonal peaks do not create variable bills. Roll out category by category with versioned prompts and a human gate for anything that publishes to the storefront.
Honest comparison
| Capability | Plugsky | Typical per-token API | Building in-house |
|---|---|---|---|
| API compatibility | OpenAI-compatible chat, embeddings and tools | Usually compatible | Full rewrite |
| Catalogue scale | Flat fair-use self-serve pricing for batch sweeps | Per-token, spikes with volume | GPU plus operations cost |
| Deployment | Cloud, VPC, on-prem and air-gapped | Mostly cloud-only | You operate GPUs and serving |
| Data residency | Region selection for each market | Often limited regions | You control fully |
| Model choice | 30+ models behind one API | Varies by provider | You host every model |
Frequently asked questions
Can we keep our existing OpenAI SDK code?
Yes. Plugsky is OpenAI-compatible, so the migration is a base URL and model-name change while your integrations and tests stay in place.
Is there a free plan?
Yes. The free plan includes plugsky-micro and plugsky-lite with no credit card, suitable for a pilot category or a support triage experiment.
How do we keep product answers accurate?
Ground answers in approved specs, manuals and policies using retrieval, and require citations so wrong answers can be traced and corrected in the source data.
Can it handle seasonal volume?
Self-serve plans use flat monthly pricing with fair-use usage, so catalogue sweeps and holiday peaks do not produce per-token billing swings.
Is customer payment data sent to the model?
No. Keep card data in your payment provider and out of prompts; send order references and only the text needed for the task.
How does pricing work?
Self-serve plans are flat monthly with unlimited fair-use usage. See the live pricing page for current plans and enterprise options.
Which endpoints are live today?
Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, fine-tuning, assistants and responses are coming soon.