Wag-Tail AI Gateway
The application-centric AI control plane. Instead of forcing one global policy on every use case, compose a dedicated profile for each AI application — from security rules, MCP components, and token-optimisation rules — then connect it to any LLM.
A Profile for Every AI Application
Enterprises don't run "AI" — they run many distinct AI applications, each with different security needs, tools, and cost profiles. Each application gets its own dedicated endpoint, backed by a profile you compose from three building blocks.
- One dedicated endpoint per application — OpenAI-compatible, no application rewrite
- Compose from security rules, MCP components, and token-optimisation rules
- Change any profile independently — no cross-application breakage
- Per-profile usage, cost and audit attribution — ready for chargeback
- System-required controls stay locked, so flexibility never bypasses governance
1 App = 1 Profile
A support assistant, a developer copilot, and a public chatbot each get exactly the controls, tools, and cost profile they need.
Building Block 1: Security Rules
Decide exactly how each application is protected. Compose the security posture per app instead of applying one global rule set — a public chatbot and an internal finance assistant can run very different rules from the same platform.
- Automatic PII detection and redaction before LLM calls
- Prompt guardrails to prevent injection attacks
- Complete audit trails for GDPR, CCPA, and APAC compliance
- API key management with HashiCorp Vault integration
- Role-based access control with multi-tenant isolation
Security First
Your data never leaves your infrastructure. Air-gapped deployment available for maximum data sovereignty.
Building Block 2: MCP Components
An LLM that can only answer is half a solution. Through the Model Context Protocol, your AI applications can securely act on enterprise systems — while the Gateway governs, routes, and audits every call. Grant each application only the tools it should have, and nothing more.
- Scoped tool access — knowledge base, databases, ticketing, web search, internal APIs
- Least-privilege by design — an app reaches only the MCP servers in its profile
- Server aggregation — many MCP servers behind one governed endpoint
- Dynamic tool discovery — expose only the tools this user and intent should see
- Reusable components, multi-tenant isolation, and a full audit trail per call
Governed, Not Just Connected
Raw MCP connects tools. Wag-Tail governs, routes, and controls them — group-scoped, so not every team sees every tool.
Building Block 3: Token-Optimisation Rules
Token economics vary widely by workload. Apply the optimisation strategy that matches how each application actually uses tokens — so savings come from the workloads that benefit most, without touching the apps that don't need it.
- Semantic caching — repeated or similar queries answered instantly from cache
- Prompt compression — reduce token usage before requests are sent
- Smart routing — send each app to the most cost-effective model for its needs
- Budget controls — per-application spend limits, quotas, and alerts
60–80% Cost Savings
Repetitive enterprise workloads commonly cut AI API costs by 60–80%. Actual savings vary by application, usage pattern, and profile configuration.
Any LLM Provider, With Automatic Failover
Route any profile to the provider that fits it best, through a unified OpenAI-compatible API. Multi-provider routing and automatic failover underpin every profile, so applications stay available even when a single provider degrades.
- OpenAI, Azure OpenAI, Anthropic, Google, Mistral, DeepSeek, and more
- Local model support via Ollama, vLLM, and TGI
- Priority-based model chaining with automatic failover and circuit breakers
- Health checks and latency monitoring per provider
- No vendor lock-in — switch models with a single config change
Unified API
One API endpoint for all models. Your applications never need to change when you switch providers.
Ready to give every AI application its own profile?
Talk to our team about deploying Wag-Tail AI Gateway in your environment — on-premise, private cloud, hybrid, or Docker.