How do fintech teams build secure LLM APIs and RAG?

Key facts

API surface	OpenAI-compatible /v1/chat/completions with streaming and JSON mode
Data grounding	Embeddings and RAG are live for transaction and policy knowledge
Tool use	Function calling is live for internal APIs and enrichment services
Access control	Scoped API keys, rotation and usage analytics; enterprise SSO and RBAC options
Deployment	Cloud, VPC, on-prem or air-gapped to match your licensing model
Pricing model	Flat monthly self-serve plans; no per-token billing on self-serve
Free tier	Free plan with 2 free AI models; 14-day full-access trial
Endpoint roadmap	Audio, images, moderation, batch and fine-tuning are coming soon

TL;DR

• Use one OpenAI-compatible API for chat, embeddings and function calling.
• Ground answers in your own data with RAG instead of fine-tuning first.
• Scope API keys per service and keep an audit trail of every call.
• Deploy in your VPC or on-prem when licensing or regulators require it.
• Validate on the free plan, then scale with flat monthly self-serve plans.

How it works, step by step

1. Classify each planned workflow by data sensitivity: public, internal, confidential or regulated.
2. Stand up a retrieval pipeline: chunk documents, generate embeddings, store vectors in your own database.
3. Write a refusal rule so the assistant answers only from retrieved context above a relevance threshold.
4. Separate credentials per service and environment, with rotation and no shared production keys.
5. Add function calling for balances, transaction lookup or ticketing only through permissioned internal APIs.
6. Log request IDs, model names and outcomes, and alert on error-rate and latency shifts.
7. Run a red-team pass with prompt-injection and data-exfiltration attempts before launch.

Try it yourself

Open the RAG sandbox →

The secure fintech AI pattern

Most production fintech AI comes down to four components:

• Retrieval: embeddings over policies, transaction metadata and product terms, stored in infrastructure you control.
• Generation: an OpenAI-compatible chat call that answers strictly from retrieved context.
• Tools: function calling into permissioned internal services for live data.
• Telemetry: request logs, evaluation results and cost attribution per feature.

Streaming improves the user experience; JSON mode makes downstream processing reliable. Both are live on Plugsky. Telemetry closes the loop: without logs and evaluations you cannot tell whether a prompt change improved or degraded answers.

Why RAG beats fine-tuning for a first release

Regulated answers change constantly: rates, limits, policies and disclosures. Retrieval updates the moment a document changes, and every answer can cite its source. Fine-tuning, which is coming soon, bakes knowledge into weights and makes provenance harder.

Start with RAG plus a strict system prompt. Reserve fine-tuning for style and format consistency once you have a large, clean dataset of reviewed examples. Retrieval also gives you a natural update path: change the document, re-index, and the next answer reflects it without a deployment.

Keys, boundaries and audit evidence

Treat model access like any other production credential. Issue separate keys per service and environment, store them in a secrets manager, rotate on schedule, and revoke immediately on team changes. Enterprise deployments can integrate SSO and role-based access for the console side.

When licensing or regulators require it, deploy in your VPC, on-prem or air-gapped. The API shape stays identical, so your application code does not change between sandbox, cloud and private environments. For regulated workloads, keep model calls behind an internal gateway that enforces rate limits, redaction and per-service budgets.

Honest comparison

Capability	Plugsky	Per-token hosted API	Self-hosted open model
API compatibility	OpenAI-compatible chat, embeddings and tools	Usually compatible	You build the serving layer
Data control	Cloud, VPC, on-prem, air-gapped	Vendor cloud only	Your hardware, your operations
Model range	30+ models behind one API	Vendor catalogue	Only what you can host
Grounding	RAG and embeddings are live	Varies by vendor	You assemble the stack
Cost predictability	Flat monthly plans on self-serve	Per-token fluctuations	Fixed GPU capacity
Time to first release	Days	Days	Weeks to months

Frequently asked questions

How do we keep prompts free of customer data?

Redact or tokenize identifiers before they leave your services, retrieve only the fields required for the answer, and keep raw records behind your own authorization checks. Private deployment removes the external data path entirely.

Should we fine-tune or use RAG?

Use RAG first. It grounds answers in current documents and provides citations. Fine-tuning is coming soon and is better suited to tone and output format than to frequently changing facts.

What stops prompt injection from leaking data?

Constrain tools to narrow permissions, validate every tool call server-side, never place secrets in the prompt, and treat retrieved text as untrusted input. Test with adversarial documents before launch.

Can we meter usage per customer?

Use separate API keys or deployment per customer and export usage analytics for attribution. That keeps cost allocation clear without per-token billing on self-serve plans.

Is there an SLA?

Enterprise and private deployments are covered by the service agreement; see the SLA and status pages for current commitments and live component health.

What is available today versus coming soon?

Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, assistants, responses and fine-tuning are coming soon.

How do we evaluate quality?

Build a labelled set of real questions with correct answers, measure retrieval hit rate and answer accuracy, and re-run it on every model or prompt change.