Sold by: Chaos Gears
An AI Gateway acts as the central control layer for all your LLM traffic - managing logging, retries, cost tracking, and robust routing. In this project, we deploy an open-source LiteLLM Server on AWS Fargate (with private ALB), configured for auto-scaling, metrics, and graceful shutdowns. We set it up as your unified API endpoint to models like Amazon Bedrock, OpenAI, Anthropic, and more. You get a fully functional gateway you can run in production, with deep visibility, centralized governance, and predictable billing. It’s perfect for teams that want reliable, consistent LLM access without engineering complexity.
Overview
What is an AI Gateway?
An AI Gateway (sometimes called an LLM Gateway) is a purpose-built API gateway that standardises how software calls many LLM back-ends, enforces auth, logs every token, and adds reliability primitives like retries and circuit breakers. Think of it as “traffic control” for AI: one endpoint, many engines.
It provides:
- Standardized API interface, simplifying model switching and integration
- Logging, tracing & monitoring to track usage and detect anomalies
- Cost control with budgets, rate limits, and fallback rules
- Resilience, including retries, load balancing, and graceful shutdowns
Why you need one
- Observability & cost control – granular spend tracking, caching and budgets stop cloud bills from spiralling
- Reliability – automated retries, fallbacks and circuit breakers keep user journeys alive during provider hiccups
- Security & governance – single place to apply auth, rate-limits and data-loss-prevention policies
- Vendor choice – swap models (e.g., move a workload from GPT-4o to Claude Sonnet 4) without changing client code.
- Enterprise scale – handle spikes by scaling tasks horizontally behind an ALB in Fargate
Industry use cases
- Financial Services / Banking: tightly govern LLM access, enforce policy controls, minimize burst costs
- Enterprise SaaS / CRM: provide downstream LLMs with a controlled, central endpoint for analytics, summarization, or agent features
- Healthcare: secure, compliant access to clinical LLMs with traceability and local failover
- Media & Marketing: route bursts of content generation through cost‑efficient fallback strategies
- Government & Education: maintain governance, logging, and resilience for mission‑critical LLM workloads
Project Structure & Timeline (3–4 weeks):
- Kickoff & architecture alignment
- Infrastructure deployment on Fargate + ALB + VPC
- LiteLLM config & model routing setup
- Monitoring & governance policies implementation
- Test integration with your applications
- Knowledge transfer & handover
Post-project, you’ll have a scalable, secure AI Gateway in your AWS account - ready for production, customizable by your team with minimal ongoing work.
Highlights
- Built-in standard API to multiple LLMs - Bedrock, Claude, OpenAI, Vertex - without scattered SDKs or integrations.
- Designed for enterprise: logging, tracing, cost control, rate limiting, retries, all centralized and visible.
- Own the stack – full IaC, dashboards and runbooks handed over so you operate the gateway independently after the engagement.
Details
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Delivery method
Deployed on AWS
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Tell us more about your challenges – email us at genai@chaosgears.com