AI gateway — One endpoint to manage, observe, and secure all model traffic
- One secure endpoint for all major LLM providers
- Guardrails, observability, and fallback logic built-in
- Lower costs with caching, usage limits, and model selection
What is an AI gateway?
An AI gateway is a service that transforms how businesses access, manage, and utilize large language models (LLMs). It acts as a centralized hub for enterprise AI operations, handling everything from intelligent model routing to keeping AI use secure and compliant.
Connect multiple LLMs through a single, secure endpoint
Control costs and optimise your AI traffic
Gain complete visibility, manage access and usage
How the nexos.ai Gateway works
Why your business needs an AI gateway
The nexos.ai Gateway gives you direct, policy-enforced access to the top AI models, while keeping security and observability front and center.
Fast to adopt, easy to scale
Access nexos.ai through a single API endpoint — no need to build cumbersome solutions.
One solution for your LLM integration pain points
Whether you're launching your first model or scaling AI company-wide, nexos.ai helps you solve the biggest AI integration challenges:
High development costs
Building an LLM gateway architecture from scratch and then maintaining it requires a significant development budget.
Operational overhead
Managing multiple API integrations, figuring out performance issues, and optimizing cost is a continuous challenge.
Lack of observability
Without centralized control, there’s no way to see how AI is used across your organization or prevent employees from leaking sensitive data to AI systems.
All your AI.
One secure gateway.
Book a personalized demo to see how nexos.ai simplifies AI adoption.
FAQ
Route and manage traffic across multiple LLM providers.
Enforce security and usage policies at the API level.
Track and control LLM costs by user, team, or project.
Add retrieval-augmented generation (RAG) to AI workflows.
Orchestrate AI agents and structured tool use.
Reduce latency and duplication through caching.
Centralize prompt management and evaluation.
Standardized API access to LLMs across vendors and deployment environments.
Model orchestration, versioning, and fallback logic.
Prompt filtering and guardrails for safe inputs and outputs.
Load balancing and failover to keep AI systems reliable.
Cost tracking with usage visibility across teams.
Logs and observability for every prompt, file, and response.
RAG and agent support for smarter, context-aware automation.
Redundancy and resilience (fallbacks, load balancing, and cloud-native reliability).
Built-in observability (logs, traces, and usage metrics).
Security and filtering (guardrails to reduce the risk of leaks and hallucinations).
Model flexibility (no vendor lock-in and support for open/private models).
Cost control (spend tracking and budgeting across teams).