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 |
| Traceability | Retrieval-grounded answers with source citations |
| Corpus control | Program-scoped collections and keys |
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 where citations can be checked quickly.
- Experts own every conclusion; agents assemble and retrieve.
How it works, step by step
- Define the job, the permitted data sources and where a human must approve.
- Curate a small, high-quality corpus and verify licensing.
- 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.
- Score citation accuracy against expert review before scaling.
- Measure quality on your own samples, then scale with usage monitoring.
Try it yourself
Where AI agents pay off in life sciences
Life Sciences 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.
- Literature review — summarise sources you supply and keep citations attached
- Protocol Q&A — answer study-team questions from approved documents
- Regulatory drafting — assemble first drafts from templates and source content
- Knowledge retrieval — surface prior work and internal reports across programs
A reference architecture for life sciences agents
A research agent retrieves from a curated corpus and returns cited syntheses, while a drafting agent populates templates from approved sources. Scientists or regulatory leads review and own every conclusion before it enters a submission or report.
- Program-scoped retrieval collections
- Tools into document management and ELN systems
- Template and citation controls
- Review gates and audit logs for regulated outputs
Data governance and human oversight
Scientific and regulatory content demands traceability. Keep sources and derived text linked, restrict retrieval by program, and preserve logs so any statement can be traced to its evidence. Specific regulatory obligations remain yours to interpret.
- Citation-linked outputs
- Program and role scoping
- Audit trails for drafted content
- Expert sign-off before submission use
From pilot to production
Pilot on literature synthesis and internal knowledge retrieval, where reviewers can score citation quality. Extend to drafting support once traceability and accuracy are demonstrated.
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 |
| Evidence links | Retrieval-grounded citations | Varies | You build the pipeline |
| Program isolation | Scoped collections and keys | Varies | You configure |
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 agents write regulatory submissions?
They can assemble drafts from approved templates and sources; qualified experts must review, verify and own the content before submission.
Can it search external literature?
You control what enters the corpus. Retrieval works over content you index, so verify licensing and source quality before adding external material.
How do we keep citations accurate?
Ground answers in retrieved passages and design prompts to cite them; then sample-check outputs before scaling.