Control which models enterprise AI can use.
Kynexa routes model and embedding requests through an approved catalog with centralized credentials, policy-based selection, usage tracking, cost visibility and fallback controls.
Reads at source · single-tenant · every policy decision logged.
The problem you own
As teams adopt multiple model providers, credentials, routing logic, usage records, and fallback behavior become fragmented across applications. Security and AI platform teams lose a consistent way to approve models, restrict sensitive workloads, monitor cost and prove which model handled a request.
How it works
Applications call models through the Kynexa gateway instead of embedding provider credentials and routing rules in every project. Policy evaluates user, agent, workload, data sensitivity, geography and approved use before selecting a model. Requests, responses, provider decisions, token usage, latency and cost signals are recorded for audit and operations.
Govern model access across providers and families.
The approved catalog can evolve without changing application code, so new providers and model versions can be added under policy. Availability is governed by tenant configuration, deployment region and approved catalog policy.
Anthropic
anthropic
Google Generative AI
google-genai
Google Vertex AI
google-vertex
Mistral La Plateforme
mistral-la-plateforme
DeepSeek
deepseek
Qwen (Alibaba Model Studio)
qwen
Ollama (Local)
ollama-local
One governed control point for every model call
Reference architecture — not a product screenshot.
A four-stage flow. Requests from RAG apps, agents and copilots enter one gateway. They are classified by identity, workload and geography. The gateway routes to an approved model with vaulted credentials and fallback, marking models Approved, Blocked, or Needs-approval. Usage, cost and an audit event are recorded.
Where governance applies
Every model request is a governed decision.
| Control surface | Policy input | Enforcement point | Audit evidence |
|---|---|---|---|
| Model access | User · agent · workload · data class | LLM gateway | Model-selection record |
| Provider routing | Capability · geography · cost | Gateway router | Routing event |
| Credentials | Approved purpose | Vault boundary | Credential-use log |
| Fallback | Availability · policy | Gateway failover | Fallback reason |
What you get
Approved model catalog
Define which models and versions are approved for each workload, business unit or data class.
Provider routing
Route across approved providers using policy, capability, geography, availability, or cost.
Credential control
Centralize provider credentials in a vault instead of distributing secrets across AI applications.
Usage and cost monitoring
Track model, token, latency, error, and cost signals by user, agent, application and purpose.
Fallback and failover
Define approved fallback paths and record why a provider or model changed.
Policy-based model selection
Restrict sensitive workloads to models and deployment locations approved for their risk profile.
What changes for you
- What you can control
- Which models, providers and locations each workload may use.
- What you can prove
- Which model handled a request, at what cost and why.
- What changes operationally
- One model control point instead of per-app credentials and routing.
- Primary stakeholders
- AI platform, security, finance, risk.
Technical FAQ
See Kynexa govern your AI — in 30 minutes.
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