AI GATEWAY
Private LLM
Service Gateway
A unified, observable and governable access layer for large language models. One OpenAI-compatible API connecting every model, every cloud, every tenant.
Core capabilities
The unified access layer for the LLM era
Unified model routing
One OpenAI-compatible API manages proprietary, open-source and third-party models, routed by policy to a suitable backend — most apps integrate without changes to business code.
End-to-end trust & security
Private deployment, tenant isolation, key vaulting, and prompt / output auditing for finance, government and healthcare compliance.
Performance & cost optimization
Built-in semantic cache, request coalescing, dynamic quantization and batching. On our internal benchmarks, per-token cost drops ~35–60% and throughput rises ~3–5×; results vary by model and traffic profile.
Full-stack observability
Real-time dashboards, distributed tracing and model-quality evaluation pinpoint issues in seconds and keep cost transparent.
Features
Six modules, ready out-of-the-box
Multi-model · multi-vendor
- OpenAI-compatible / Anthropic / custom protocols
- One-click upstream switch: in-house / vLLM / SGLang / remote APIs
- Automatic failover & canary rollouts
Key & tenant governance
- Per-project / per-user API keys, instant provisioning & revocation
- Quotas, rate limits, budget alerts
- Per-call attribution audits, SSO / SAML / OIDC
Data security & compliance
- Supports private deployment with data kept inside the customer network perimeter
- Sensitive-term filtering, PII redaction, content-safety gateway
- Implementation paths and templates aligned with ISO 27001 / SOC 2 controls
Smart routing & policy
- Route dynamically by latency, cost, load and model capability
- A/B, shadow traffic, per-tenant canary
- Retry / fallback / circuit-breaker
Caching & acceleration
- Semantic cache hit rate up to ~60% on high-frequency Q&A workloads
- Embedding batching & connection reuse
- KV-Cache reuse and dynamic quantization
Observability & operations
- Per-call: latency, tokens, cost, trace ID
- Prometheus / OpenTelemetry / Webhook export
- Model-quality evaluation and business reports
Deployment models
Fits any infrastructure
Kubernetes private cloud
One-click Helm chart, multi-cluster, multi-AZ and heterogeneous-GPU ready.
Sovereign & bare-metal
Adapts to mainstream CPU / GPU / accelerators and major sovereign-cloud OS and middleware stacks.
Hybrid & multi-region
A single control plane manages AWS / Azure / GCP / Oracle and on-prem datacenters.
Compare
Why SmarTokenX AI Gateway
| Dimension | SmarTokenX | Typical reverse-proxy gateway |
|---|---|---|
| Integration cost | Minimal changes · OpenAI-compatible | Typically requires SDK and protocol rewrites |
| Routing policy | Latency / cost / capability | Static weighted only |
| Observability | Per-call tracing + quality eval | Basic metrics only |
| Deployment | SaaS / dedicated / private | SaaS only |
| Cost optimization | Semantic cache + dynamic quantization | None |
Quickstart
3 lines of code — escape vendor lock-in
from openai import OpenAI
client = OpenAI(
api_key="stx_...",
base_url="https://gateway.your-domain.com/v1",
)
resp = client.chat.completions.create(
model="auto", # let the gateway pick the best model
messages=[{"role": "user", "content": "Hello"}],
)FAQ
Questions you may have
How does AI Gateway relate to the Enterprise MaaS platform?
Can I plug in my own models and third-party APIs?
How fast can we deploy?
How is data security handled?
Deploy a dedicated gateway for your LLM apps
Our solution experts will provide a POC environment and deployment plan within one business day.