Use cases
From no-code deployment to compliance: how teams use the governed pipeline to secure, optimise and federate their AI flows.
Connect your existing AI apps (LangChain, LangGraph, CrewAI, n8n, Make, Zapier, Open WebUI, LibreChat) through an OpenAI/Anthropic-compatible API. The governed pipeline slots in automatically, without changing your agents’ code.
PII, secrets and sensitive business data detected and redacted before anything is sent to the LLM provider. Sensitive data stays with you; models see only what they need.
Set budgets and quotas per business unit, team or user. Get alerts before overruns, cut off cleanly at threshold, and charge AI costs back to each department’s real usage.
Assign token or budget limits per user, with full traceability of every call. Deploy internal AI assistants without fearing a cost explosion.
Plug your n8n workflows into TokenSaver in a few clicks. Semantic cache, multi-format compression and model routing lower the bill of every AI workflow run.
One control plane for modern AI SaaS apps and legacy systems. Same policies, same guardrails, same observability, wherever the call comes from.
Centralise API keys and AI usage scattered across the company. Apply a single policy layer to BYOK tools and unsupervised agents, without blocking teams.
Route part of the traffic to different models and measure quality, latency and cost per variant. Unified metrics to pick the right model at the right price.
Immutable, signed traces for every call, with OpenTelemetry export. Prepare your EU AI Act and GDPR audits with technical proof of your governance decisions.
Tell us about your context: we have probably already worked with a team facing similar constraints.
