AI Gateway

One API for every model

A unified proxy to 250+ LLMs through a single OpenAI-compatible API. Route requests by cost, latency, or capability. Fail over automatically when a provider goes down.

  • Low-latency proxy with minimal overhead
  • OpenAI, Claude, Gemini, Mistral, Llama, and self-hosted
  • Automatic failover and retry across providers
  • Smart routing: cheap queries to cheap models, complex to capable
  • Semantic caching avoids re-running identical queries
  • Token-level cost attribution by user, team, and model
// Smart routing example

Incoming request
  complexity: "low"
  → Route to Haiku ($0.001/1K tokens)

Incoming request
  complexity: "high"
  → Route to Opus ($0.075/1K tokens)

Incoming request
  cache hit: true
  → Return cached ($0.00)
AI Agent Gateway

Control plane for agentic workflows

Multi-step AI agent execution with retries, timeouts, and fallbacks. Framework-agnostic — works with any agent implementation via HTTPS and Secure WebSocket (WSS) APIs.

  • Framework-agnostic — works with any agent implementation
  • Configurable retries with exponential backoff
  • Per-step timeouts prevent runaway AI agents
  • Automatic fallback to alternative models or tools
  • Execution traces for debugging multi-step chains
  • Human-in-the-loop approval for high-risk steps
// Agent execution trace

Step 1 completed 42ms
  tool: "search_contacts"
  result: 3 matches

Step 2 completed 128ms
  tool: "draft_email"
  model: claude-sonnet-4-5

Step 3 awaiting approval
  tool: "send_email"
  risk: high
  Requires human approval
MCP Gateway

Enterprise hub for Model Context Protocol

MCP standardizes how agents connect to tools. The MCP Gateway solves the enterprise problems the raw protocol doesn't — centralized registry, per-user tokens, permission boundaries, and guardrails on every tool call.

  • Centralized registry of MCP servers (public and self-hosted)
  • Virtual MCP Servers: curated tool subsets per team
  • Personal Access Tokens (PAT) — one token per user
  • Virtual Account Tokens (VAT) — for service accounts
  • OAuth2 token management per user, per service
  • Connect from Claude Code, Cursor, ChatGPT, or custom agents
// The N×M problem, solved

Without gateway:
  5 agents × 10 tools = 50 integrations
  50 credential sets
  50 error handlers
  50 access control policies

With WorkingAgents:
  5 agents → 1 gateway → 10 tools
  5 agent connections
  10 tool connections
  1 policy engine
Virtual MCP Servers

Permission boundaries for every team

Combine tools from multiple sources into curated subsets with their own access rules. Each team's agents see only what they're authorized to use.

💼

Sales Team

CRM read/write, document generation, knowledge base search. No database admin, no deployments, no engineering tools.

💻

Engineering Team

GitHub, CI/CD, issue tracker, deployments, monitoring. No CRM data, no financial records, no HR systems.

📞

Support Team

Ticket system, knowledge base, customer lookup (read-only). No billing modifications, no admin functions.

Model Serving — Roadmap

Deploy any model on your GPUs

Run open-source models on your own infrastructure with optimized serving engines. Full control over your model stack without building the deployment pipeline. (Coming soon — currently in development.)

  • vLLM, SGLang, and TRT-LLM serving backends (planned)
  • Fine-tuning pipelines with version management (planned)
  • Prompt management with A/B testing (planned)
  • Auto-scaling based on load and latency targets (planned)
  • Model versioning and canary deployments (planned)
// Model deployment

Model: llama-3.1-70b
Engine: vLLM
GPUs: 4x A100
Status: serving

Metrics (last hour):
  Requests: 12,847
  P50 latency: 89ms
  P99 latency: 340ms
  GPU utilization: 78%
Observability

Complete visibility into everything your AI does

Aggregate dashboards, request-level inspection, and distributed tracing — across every model, tool, and agent.

📈

Cost Attribution

Token-level usage tracking by user, team, model, and environment. Set budgets per team. Know exactly who spent what and why.

🔍

Request Inspection

Full prompt, response, routing decision, and guardrail evaluation for every request. Drill into any interaction.

Latency Tracking

P99, P90, P50 latency per endpoint, model, and tool. Identify bottlenecks before they become user-facing problems.

🔗

Distributed Tracing

Request tracing spans the full lifecycle — from agent decision through gateway to tool execution and back.

🔔

Alerts

Configurable alerts for budget thresholds, latency spikes, error rate increases, and guardrail violations.

📊

Dashboards

Aggregate views for requests, tokens, cost, and latency — broken down by any dimension you need.

Progressive Adoption

Start small. Add layers as you grow.

Each component adds value independently. No all-or-nothing commitment.

1

AI Gateway

Start here. Unified API to external LLMs with failover and cost tracking.

2

MCP Gateway

Add tool integration with centralized access control and audit trails.

3

Self-Hosted Models

Deploy your own models for data privacy, cost control, or custom fine-tunes.

4

Full Platform

Guardrails, RAG, fine-tuning, and enterprise governance across everything.

Ready to give your AI agents the infrastructure they deserve?

Start with the free tier. No credit card required.

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