LLM API Gateway Configuration

Complete guide to configuring LLM API gateways for production deployments. Optimize for GPT, Claude, Llama, and other large language models.

⚡

Fast Setup

Deploy in minutes with pre-built templates

🔒

Secure

Enterprise-grade security out of the box

📈

Scalable

Auto-scale to millions of requests

Configuration Steps

1

Install Gateway

Deploy the gateway using Docker, Kubernetes, or our managed service. Configure connection settings and authentication credentials.

2

Configure Providers

Add your LLM provider API keys and configure endpoint settings. Set rate limits, retry policies, and timeout values.

3

Set Up Routes

Define routing rules to map incoming requests to specific LLM providers and models. Configure path-based and header-based routing.

4

Enable Caching

Configure intelligent caching for repetitive queries. Set cache keys, TTL values, and invalidation policies.

5

Configure Monitoring

Set up metrics collection, logging, and alerting. Track latency, throughput, error rates, and costs.

6

Test & Deploy

Validate configuration with test requests. Monitor performance and fine-tune settings based on real-world usage.

gateway-config.yaml
Copy
providers:
  openai:
    api_key: "${OPENAI_API_KEY}"
    base_url: "https://api.openai.com/v1"
    model: "gpt-4"
    rate_limit: 1000

routes:
  - path: "/chat/completions"
    provider: "openai"
    cache_ttl: 300

monitoring:
  enabled: true
  metrics_interval: 60

Supported LLM Providers

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OpenAI
  • GPT-4 and GPT-3.5 support
  • Chat and completion APIs
  • Image generation (DALL-E)
  • Whisper audio models
🧠
Anthropic
  • Claude 3 models
  • Long context windows
  • Vision capabilities
  • Tool use & function calling
🦙
Meta Llama
  • Llama 2 & 3 models
  • Open source & free
  • Self-hosted deployment
  • Custom fine-tuning
💎
Google Gemini
  • Gemini Pro & Ultra
  • Multimodal capabilities
  • Code generation
  • Reasoning tasks
🌐
Cohere
  • Command models
  • Embedding APIs
  • Rerank endpoints
  • Enterprise features
🔮
Mistral AI
  • Mixtral 8x7B
  • Mistral 7B
  • Open source
  • Fine-tuning available

Configuration Approaches Compared

Approach Setup Time Flexibility Cost Best For
Our Gateway Minutes High $149/mo Most teams
Custom Build Weeks Unlimited $$$$$ Enterprise
Open Source Hours Medium Free Dev teams
Provider SDK Minutes Low Variable Simple apps

Frequently Asked Questions

What is LLM API gateway configuration?

The process of setting up an API gateway to route, manage, and optimize requests to large language model APIs. Includes provider configuration, routing rules, caching, monitoring, and security settings.

Which LLM providers are supported?

We support all major providers: OpenAI, Anthropic (Claude), Meta (Llama), Google (Gemini), Cohere, Mistral AI, and more. Self-hosted models and custom endpoints are also supported.

How do I configure rate limiting?

Set granular rate limits per provider, model, API key, or user. Configure burst allowances, time windows, and throttling behaviors. Automatic retry with exponential backoff included.

Can I use multiple LLM providers?

Yes. Configure multiple providers and implement routing strategies: cost-based, performance-based, fallback, or round-robin. Automatic failover ensures high availability.

How does caching work?

Intelligent response caching based on request similarity, cache keys, and TTL values. Supports cache warming, invalidation rules, and distributed caching across multiple gateways.

What monitoring capabilities are included?

Real-time metrics for request rate, latency, error rates, token usage, and costs per provider/model. Historical data retention up to 90 days. Integration with Prometheus, Grafana, Datadog.

Partner Resources

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API Gateway Proxy Admin Panel

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AI API Proxy Control Center

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AI API Gateway Troubleshooting

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D

API Gateway Proxy Debugging

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