Key facts
| API compatibility | OpenAI-compatible /v1/chat/completions (change the base URL) |
| Models | 30+ models from free to frontier tiers behind one API |
| Agent primitives | Function calling, JSON mode and streaming are live |
| Retrieval | Embeddings and RAG over your own corpus |
| Deployment | Plugsky cloud, VPC, on-prem or air-gapped |
| Pricing | Flat monthly self-serve plans with fair-use usage; see the live pricing page |
| Multilingual | Multilingual models available in the catalogue |
| Escalation | Confidence thresholds and routing rules you configure |
TL;DR
- Keep your OpenAI SDK — change the base URL and model name.
- 30+ models behind one API, from free chat models to frontier reasoning.
- Deployment options from hosted cloud to VPC, on-prem and air-gapped.
- Pilot one property, then standardise across the group.
- Ground answers in your own content to stay on brand.
How it works, step by step
- Define the job, the permitted data sources and where a human must approve.
- Start with FAQ messaging and review drafts at one property.
- Create a Plugsky account and generate an API key (free plan, no card required).
- Point your OpenAI SDK at the Plugsky base URL and map your model names.
- Index the approved corpus with embeddings and keep retrieval role-scoped.
- Set escalation thresholds and sample transcripts weekly.
- Measure quality on your own samples, then scale with usage monitoring.
Try it yourself
Open the LLM token calculator →
Where AI agents pay off in hospitality
Hospitality teams do not lack ideas for agents; they lack a safe path from demo to production. The pattern below targets repetitive, document-heavy work where a human can check the output, which is where agents earn their place first. Treat the agent as a new team member with a narrow brief, explicit permissions and a probation period, and rollout becomes an operations exercise rather than a leap of faith.
- Guest messaging — answer stay questions in multiple languages, around the clock
- Reservation changes — check availability and prepare modifications for confirmation
- Upsells — suggest relevant add-ons during pre-arrival contact
- Reputation — draft on-brand responses to reviews for manager approval
A reference architecture for hospitality agents
A guest-facing agent answers from your own content through retrieval and calls the PMS through tools for availability or booking context. Anything outside policy such as refunds, complaints or special requests is routed to a human with the conversation attached.
- Channel connectors (web, email, messaging) with consent handling
- Retrieval over property, policy and FAQ content
- Read-first tool calls into PMS, CRM and ticketing
- Confidence thresholds and human escalation rules
Data governance and human oversight
Guest data is personal data. Keep bookings and preferences inside the boundary, scope each property's agent to its own content, log interactions, and let staff own refunds and exceptions.
- Per-property keys and content scoping
- No autonomous changes to pricing or refunds
- Audit logs for every guest interaction
- Data retention aligned with your policy
From pilot to production
Run a single property as a pilot, starting with FAQ messaging and review drafts. Track escalation rate and guest satisfaction, then roll the pattern out chain-wide.
Keep the rollout reversible: run the agent in shadow mode alongside the current process, compare outputs on your own samples, and move it into the workflow only when the evidence holds. Document what you measured so expanding to the next team is a decision, not a hope.
Honest comparison
| Capability | Plugsky | Typical cloud AI API | Building in-house |
|---|---|---|---|
| API compatibility | Drop-in base URL change | Usually compatible | Full rewrite |
| Model access | 30+ models behind one API | Vendor's own catalogue | You host each model |
| Pricing | Flat monthly self-serve plans; see live pricing | Often per-token | GPU + ops cost |
| Deployment | Cloud, VPC, on-prem or air-gapped | Usually vendor cloud regions | You own the stack |
| Guest systems | Agents call your PMS/CRM; those stay authoritative | Varies by provider | Full integration burden |
| Escalation | Thresholds and rules you configure | Varies | You build it |
Frequently asked questions
Do we have to rewrite our application?
No. The chat completions API is OpenAI-compatible, so you change the base URL and model name and keep your existing SDK.
Is there a free plan?
Yes — the free plan includes two free AI models, plugsky-micro and plugsky-lite, with no credit card required.
How is pricing structured?
Self-serve plans are flat monthly with fair-use usage and no per-token charges; see the live pricing page for current plans.
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 endpoints are coming soon — check the docs before planning around them.
Can the agent change a booking automatically?
It can prepare changes through your PMS tools, but the safer default is staff confirmation for anything that affects price, refunds or availability guarantees.
Does it work for multilingual guests?
Yes — choose a multilingual model from the catalogue and keep answers grounded in your own translated content where accuracy matters.
How do we stop off-brand replies?
Ground generation in an approved content collection, add a brand-voice instruction, and sample the output. Drafts should route to staff until quality is proven.