LLM Gateway
One API. Every AI Model. Optimal Cost for Every Request.
Managing multiple LLM providers means juggling different APIs, rate limits, pricing models, and failover logic. An LLM gateway provides a unified interface that routes each request to the optimal model based on task complexity, cost, and latency requirements. Organizations using intelligent model routing report 40% cost reduction while maintaining or improving output quality. Automatic failover eliminates downtime when any single provider has issues.
Multiple LLM Providers Create Integration Chaos and Cost Blindspots
Your engineering team maintains separate integrations for OpenAI, Anthropic, and Google. Each has different authentication, rate limiting, error handling, and billing. When one provider goes down, your application goes down with it.
Cost visibility is fragmented across provider dashboards. Nobody knows which teams, features, or requests consume the most tokens. There's no way to enforce spending limits or route cost-sensitive requests to cheaper models automatically.
Model updates and deprecations require code changes across every integration point. A new model release means updating dozens of files instead of changing a routing rule.
Unified Gateway with Intelligent Routing and Cost Controls
We build LLM gateways that abstract provider complexity behind a single, clean API.
Unified API provides one endpoint for all LLM requests. Your application code doesn't know or care which provider handles each request. Switching models means changing a routing rule, not refactoring code.
Intelligent routing analyzes each request and routes it to the optimal model. Simple classification tasks go to fast, cheap models (GPT-4o-mini, Claude Haiku). Complex reasoning goes to powerful models (GPT-4o, Claude Sonnet). Custom rules route specific use cases to fine-tuned models.
Automatic failover detects provider outages in real-time and reroutes requests to backup models within seconds. Your users never see an error because of a provider issue.
Cost controls enforce per-team, per-feature, and per-user spending limits. Real-time dashboards show token usage, costs, and quality metrics across all providers. Budget alerts prevent surprise bills.
Caching deduplicates identical requests, reducing costs and latency for repeated queries.
LLM Gateway Development Process
1
Usage Audit & Requirements(1 week)
We analyze your current LLM usage patterns: which models, which features, volume per endpoint, cost distribution, and reliability requirements. This data drives routing rules and cost optimization.
2
Gateway Architecture(1-2 weeks)
We design the gateway infrastructure: routing logic, failover chains, caching strategy, rate limiting, authentication, and observability. Architecture decisions balance latency, cost, and reliability.
3
Implementation & Integration(3-5 weeks)
We build the gateway, implement routing rules, integrate all LLM providers, and set up monitoring dashboards. Your existing application migrates to the gateway API with minimal code changes.
4
Optimization & Monitoring(2 weeks + ongoing)
We analyze real traffic patterns to refine routing rules, identify caching opportunities, and optimize cost-quality tradeoffs. Continuous monitoring ensures gateway health and cost compliance.
LLM Gateway Technology Stack
L
LiteLLM
Unified LLM API proxy supporting 100+ models across all major providers
R
Redis
Response caching, rate limiting, and real-time routing state management
P
PostgreSQL
Usage logging, cost tracking, and analytics data warehouse
G
Grafana
Real-time dashboards for cost, latency, error rates, and model performance
N
Nginx / Envoy
High-performance API gateway with load balancing and TLS termination
P
Python / FastAPI
Custom routing logic, request transformation, and middleware implementation
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LLM Gateway Use Cases
SaaS Platform
Challenge: Platform used GPT-4 for all AI features — costing $45,000/month with 300ms average latency for simple tasks that didn't need advanced reasoning
Solution: Gateway routing simple tasks (summarization, formatting) to GPT-4o-mini and complex tasks (analysis, generation) to GPT-4o, with automatic classification
Result: Monthly LLM costs reduced from $45,000 to $18,000; average latency for simple tasks dropped from 300ms to 80ms; quality maintained on complex tasks
Enterprise AI Platform
Challenge: Three business units used different LLM providers with no centralized cost visibility, spending $120,000/month combined with no governance
Solution: Centralized gateway with per-unit budgets, approval workflows for high-cost models, usage dashboards, and automated alerts at 80% budget utilization
Result: Total spend reduced 35% through routing optimization; budget overruns eliminated; full cost attribution to business units and features
Customer Service
Challenge: Chatbot relied on single OpenAI API — when OpenAI had a 4-hour outage, all customer self-service was unavailable, generating 2,000+ manual tickets
Solution: Gateway with automatic failover: OpenAI primary, Anthropic secondary, self-hosted Llama tertiary. Health checks every 10 seconds with sub-second failover
Result: Zero customer-facing outages in 12 months; 99.99% availability maintained through 6 provider incidents; support team no longer on-call for AI outages
Why idataweb for LLM Gateway & Model Router
Modern Production Stack
We build agents on Next.js 16 + Payload CMS 3 + PostgreSQL — the same stack our own production AI systems run on. Server Actions handle tool orchestration, PostgreSQL stores agent memory and state, and Payload manages configuration through an admin UI your team can use without touching code.
AI-Native Team
Claude and GPT-4o aren't services we resell — they're tools we use every day to build software, generate content, and run internal operations. Our AI coding agents write production code. Our content pipeline generates and publishes articles autonomously. We build AI agents because we are an AI-native team.
Self-Hosted Infrastructure
Self-hosted infrastructure means your data stays where you control it. No vendor lock-in to SaaS platforms that can change pricing or terms. Full PostgreSQL audit trails, your own backups, and GDPR compliance built into the architecture.
End-to-End Delivery
Strategy, architecture, development, deployment, and ongoing support — all from one team. No handoffs between consultants, designers, and developers. The engineers who build your system are the same ones who maintain it.
Automation-First Operations
Our own operations are automated end-to-end: CI/CD pipelines, infrastructure monitoring with Telegram alerts, daily database backups, automated content publishing, and AI-assisted development workflows. We build automation for clients because automation is how we run our own business.
Transparent Fixed Pricing
Frequently Asked Questions
Why not just use one LLM provider?
Single-provider dependency creates risk: outages, price increases, model deprecations, and capability gaps. OpenAI has had multiple significant outages in the past year. A gateway lets you use the best model for each task while maintaining a single integration point. When a provider raises prices, you reroute affected traffic without changing application code.
How does intelligent routing reduce costs?
Not every request needs GPT-4o. A gateway analyzes request complexity and routes simple tasks (classification, formatting, summarization) to cheaper, faster models like GPT-4o-mini or Claude Haiku. Complex tasks (multi-step reasoning, creative writing, code generation) go to more capable models. This typically reduces costs 30-40% without measurable quality loss on simpler tasks.
What's the latency overhead of adding a gateway?
A well-built gateway adds 5-15ms of overhead per request — negligible compared to LLM response times of 200-2000ms. The caching layer often reduces average latency because repeated queries return instantly from cache instead of making a fresh API call. Net effect is typically faster average response times.
Can we add new models without code changes?
How Much Are You Spending on LLM APIs Without Cost Visibility?
Share your current LLM usage and provider setup. We'll identify routing optimizations that could cut your costs 30-40% while improving reliability.
Free usage audit · 40% cost reduction · 99.9% uptime with failover