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LLM Inference Gateway

A high-performance, production-ready Rust-based gateway for unifying multiple Large Language Model (LLM) providers into a single, scalable API.

License Rust Build Status Tests Kubernetes

Table of Contents

Features

Core Gateway Capabilities

Feature Description
Multi-Provider Support Unified interface for OpenAI, Anthropic, Azure OpenAI, Google Gemini, AWS Bedrock, Cohere, and more
High Performance Built in Rust for maximum throughput (10,000+ RPS per instance) with zero-copy operations
Streaming Support Full Server-Sent Events (SSE) support for real-time token streaming
Intelligent Routing Cost-aware, latency-optimized provider selection with configurable strategies
Advanced Caching Response caching with semantic similarity matching and TTL management
Rate Limiting Per-provider, per-tenant token bucket rate limiting
Circuit Breakers Automatic provider health detection with circuit breaker patterns
Request Retries Configurable retry policies with exponential backoff
Load Balancing Round-robin, weighted, and least-connections load balancing

Enterprise Features

Feature Description
High Availability Multi-region deployments with automatic failover
Horizontal Scaling Kubernetes-native with HPA auto-scaling support
Security Hardening IP filtering, request signing, header security, PII redaction
Multi-Tenancy Namespace isolation, resource quotas, per-tenant configuration
Cost Tracking Token usage analytics, cost attribution, budget alerts
GDPR Compliance Data residency controls, PII detection and masking
Audit Logging Comprehensive request/response logging with structured output
Database Integration PostgreSQL support with SQLx migrations

Resilience Features

Feature Description
Circuit Breaker Three-state circuit breaker (Closed → Open → Half-Open)
Bulkhead Pattern Isolated resource pools to prevent cascade failures
Timeout Management Configurable timeouts per operation type
Retry Strategies Fixed, exponential, and jittered retry policies
Health Checks Active and passive health monitoring
Fallback Providers Automatic failover to backup providers

Quick Start

Prerequisites

  • Rust 1.75+ (for local development)
  • Docker (for containerized deployment)
  • PostgreSQL 14+ (for persistence)
  • Redis 7.0+ (optional, for distributed caching)

Installation

# Clone repository
git clone https://github.com/your-org/llm-inference-gateway.git
cd llm-inference-gateway

# Build release binary
cargo build --release

# Or install CLI globally
cargo install --path crates/gateway-cli

Running the Gateway

# Set required environment variables
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export DATABASE_URL="postgres://user:pass@localhost/gateway"

# Run database migrations
llm-gateway migrate run

# Start the gateway server
llm-gateway start --config config.yaml

# Or with environment variables only
llm-gateway start --port 8080 --host 0.0.0.0

Docker Deployment

# Build Docker image
docker build -t llm-gateway:latest -f deployment/docker/Dockerfile .

# Run container
docker run -d \
  -p 8080:8080 \
  -p 9090:9090 \
  -e OPENAI_API_KEY="sk-..." \
  -e ANTHROPIC_API_KEY="sk-ant-..." \
  -e DATABASE_URL="postgres://..." \
  llm-gateway:latest

The gateway will be available at http://localhost:8080

Architecture

Crate Structure

The gateway is organized as a Rust workspace with modular crates:

llm-inference-gateway/
├── crates/
│   ├── gateway-core/        # Core types, requests, responses, streaming
│   ├── gateway-config/      # Configuration loading, hot-reload, validation
│   ├── gateway-providers/   # Provider implementations (OpenAI, Anthropic, etc.)
│   ├── gateway-routing/     # Request routing, load balancing, rules engine
│   ├── gateway-resilience/  # Circuit breakers, retries, timeouts, bulkheads
│   ├── gateway-telemetry/   # Metrics, tracing, logging, PII redaction
│   ├── gateway-security/    # Security middleware, validation, encryption
│   ├── gateway-server/      # HTTP server, handlers, middleware
│   ├── gateway-sdk/         # Rust client SDK
│   ├── gateway-cli/         # Command-line interface
│   └── gateway-migrations/  # Database migrations (SQLx)
└── src/                     # Main binary entry point

System Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        Client Applications                       │
│              (SDK, CLI, REST API, Streaming SSE)                │
└────────────────────────────┬────────────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────────────┐
│                       Gateway Server                             │
│  ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │
│  │   Security   │ │    Auth      │ │    Rate Limiting         │ │
│  │  Middleware  │ │  Middleware  │ │    Middleware            │ │
│  └──────────────┘ └──────────────┘ └──────────────────────────┘ │
│  ┌──────────────────────────────────────────────────────────┐   │
│  │                    Request Router                         │   │
│  │  (Model Routing │ Cost Routing │ Latency Routing)        │   │
│  └──────────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────────┐   │
│  │                  Resilience Layer                         │   │
│  │  (Circuit Breaker │ Retry │ Timeout │ Bulkhead)          │   │
│  └──────────────────────────────────────────────────────────┘   │
└────────────────────────────┬────────────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────────────┐
│                    Provider Registry                             │
│  ┌─────────┐ ┌───────────┐ ┌───────┐ ┌────────┐ ┌────────────┐ │
│  │ OpenAI  │ │ Anthropic │ │ Azure │ │ Google │ │ AWS Bedrock│ │
│  └─────────┘ └───────────┘ └───────┘ └────────┘ └────────────┘ │
└─────────────────────────────────────────────────────────────────┘

Key Design Principles

  • Provider Agnostic - Unified API regardless of underlying provider
  • Resilient - Automatic retries, circuit breakers, fallback providers
  • Observable - Comprehensive metrics, structured logging, distributed tracing
  • Performant - Async I/O, connection pooling, zero-copy streaming
  • Secure - Defense in depth with multiple security layers
  • Extensible - Plugin architecture for custom providers and middleware

CLI Reference

The llm-gateway CLI provides comprehensive management capabilities.

Global Options

llm-gateway [OPTIONS] <COMMAND>

Options:
  -v, --verbose...         Increase output verbosity (-v, -vv, -vvv)
      --json               Output in JSON format
  -u, --url <URL>          Gateway server URL [default: http://localhost:8080]
  -k, --api-key <API_KEY>  API key for authentication
  -h, --help               Print help
  -V, --version            Print version

Commands

Server Management

# Start the gateway server
llm-gateway start [OPTIONS]
  -c, --config <FILE>      Configuration file path
  -p, --port <PORT>        Server port [default: 8080]
  -H, --host <HOST>        Server bind address [default: 0.0.0.0]
      --workers <N>        Number of worker threads
      --metrics-port <PORT> Prometheus metrics port [default: 9090]

# Check gateway health
llm-gateway health [OPTIONS]
      --detailed           Show detailed health information
      --timeout <SECS>     Health check timeout [default: 10]

Model & Chat Operations

# List available models
llm-gateway models [OPTIONS]
  -p, --provider <NAME>    Filter by provider
      --capabilities       Show model capabilities

# Send chat completion request
llm-gateway chat [OPTIONS] <MESSAGE>
  -m, --model <MODEL>      Model to use [default: gpt-4o]
  -s, --system <PROMPT>    System prompt
      --stream             Enable streaming response
      --temperature <T>    Temperature [default: 0.7]
      --max-tokens <N>     Maximum tokens to generate

Metrics & Monitoring

# View latency metrics
llm-gateway latency [OPTIONS]
  -p, --provider <NAME>    Filter by provider
  -m, --model <MODEL>      Filter by model
  -w, --window <DURATION>  Time window [default: 1h]
      --percentiles        Show percentile breakdown

# View cost tracking
llm-gateway cost [OPTIONS]
  -p, --provider <NAME>    Filter by provider
  -m, --model <MODEL>      Filter by model
  -t, --tenant <ID>        Filter by tenant
  -w, --window <DURATION>  Time window [default: 24h]
  -g, --group-by <FIELD>   Group by (provider, model, tenant, hour, day)
      --breakdown          Show detailed cost breakdown

# View token usage statistics
llm-gateway token-usage [OPTIONS]
  -p, --provider <NAME>    Filter by provider
  -m, --model <MODEL>      Filter by model
  -t, --tenant <ID>        Filter by tenant
  -w, --window <DURATION>  Time window [default: 24h]
  -g, --group-by <FIELD>   Group by field
      --detailed           Show detailed breakdown

Backend Health & Routing

# Monitor backend health
llm-gateway backend-health [OPTIONS]
  -p, --provider <NAME>    Filter by provider
      --unhealthy-only     Show only unhealthy backends
      --history            Include historical health data
  -w, --watch              Watch mode - continuously refresh
      --interval <SECS>    Refresh interval [default: 5]

# Manage routing strategies
llm-gateway routing-strategy <COMMAND>

Commands:
  show      Show current routing strategy and configuration
  rules     List all routing rules
  weights   Show provider weights and load balancing info
  test      Test routing for a specific request
  stats     Show routing statistics

# Example: Test routing for a model
llm-gateway routing-strategy test --model gpt-4o --tenant tenant-001

Cache Management

# View and manage cache
llm-gateway cache-status <COMMAND>

Commands:
  stats     Show cache statistics
  list      List cached entries
  clear     Clear cache entries
  config    Show cache configuration

# Examples
llm-gateway cache-status stats --detailed
llm-gateway cache-status list --model gpt-4o --limit 20
llm-gateway cache-status clear --older-than 24h --force

Configuration & Validation

# Manage configuration
llm-gateway config <COMMAND>

Commands:
  show      Display current configuration
  validate  Validate configuration file
  generate  Generate sample configuration

# Validate configuration file
llm-gateway validate <CONFIG_FILE>
      --strict             Strict validation mode

# Show gateway info
llm-gateway info
      --detailed           Show detailed system information

Database Migrations

# Database migration management
llm-gateway migrate <COMMAND>

Commands:
  run       Run pending migrations
  revert    Revert the last migration
  status    Show migration status
  create    Create a new migration

# Examples
llm-gateway migrate run
llm-gateway migrate status
llm-gateway migrate revert --steps 2

Shell Completions

# Generate shell completions
llm-gateway completions <SHELL>

# Supported shells: bash, zsh, fish, powershell

# Install for bash
llm-gateway completions bash > /etc/bash_completion.d/llm-gateway

# Install for zsh
llm-gateway completions zsh > ~/.zfunc/_llm-gateway

SDK

Rust SDK

The gateway-sdk crate provides a type-safe Rust client for interacting with the gateway.

Installation

Add to your Cargo.toml:

[dependencies]
gateway-sdk = { path = "crates/gateway-sdk" }
tokio = { version = "1", features = ["full"] }

Basic Usage

use gateway_sdk::{Client, ChatRequest, Message};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create client
    let client = Client::builder()
        .base_url("http://localhost:8080")
        .api_key("your-api-key")
        .timeout(Duration::from_secs(30))
        .build()?;

    // Send chat completion request
    let request = ChatRequest::builder()
        .model("gpt-4o")
        .messages(vec![
            Message::system("You are a helpful assistant."),
            Message::user("Hello, how are you?"),
        ])
        .temperature(0.7)
        .max_tokens(150)
        .build()?;

    let response = client.chat(request).await?;
    println!("Response: {}", response.choices[0].message.content);

    Ok(())
}

Streaming Responses

use futures::StreamExt;
use gateway_sdk::{Client, ChatRequest, Message};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::new("http://localhost:8080", Some("your-api-key"));

    let request = ChatRequest::builder()
        .model("claude-3-5-sonnet")
        .messages(vec![Message::user("Tell me a story")])
        .stream(true)
        .build()?;

    let mut stream = client.chat_stream(request).await?;

    while let Some(chunk) = stream.next().await {
        match chunk {
            Ok(chunk) => {
                if let Some(content) = &chunk.choices[0].delta.content {
                    print!("{}", content);
                }
            }
            Err(e) => eprintln!("Error: {}", e),
        }
    }

    Ok(())
}

SDK Features

Feature Description
Type Safety Strongly typed requests and responses
Streaming Full async streaming support
Auto-Retry Configurable retry policies
Connection Pooling Efficient HTTP connection reuse
Timeout Handling Per-request and global timeouts
Error Handling Rich error types with context
Tracing OpenTelemetry integration

API Usage

Chat Completions

# Basic chat completion
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello, how are you?"}
    ],
    "temperature": 0.7,
    "max_tokens": 150
  }'

Streaming Response

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -N \
  -d '{
    "model": "claude-3-5-sonnet",
    "messages": [{"role": "user", "content": "Tell me a story"}],
    "stream": true
  }'

Multi-Modal (Vision)

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "What is in this image?"},
          {
            "type": "image_url",
            "image_url": {"url": "https://example.com/image.jpg"}
          }
        ]
      }
    ]
  }'

List Models

curl http://localhost:8080/v1/models \
  -H "Authorization: Bearer YOUR_API_KEY"

Health Check

# Simple health check
curl http://localhost:8080/health

# Detailed health check
curl http://localhost:8080/health?detailed=true

Response Format

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1700000000,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I'm doing well, thank you for asking."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 12,
    "total_tokens": 37
  }
}

Configuration

Configuration File (YAML)

server:
  host: "0.0.0.0"
  port: 8080
  max_connections: 10000
  timeout_seconds: 300
  metrics_port: 9090

providers:
  openai:
    enabled: true
    api_key: "${OPENAI_API_KEY}"
    base_url: "https://api.openai.com/v1"
    timeout_seconds: 60
    max_retries: 3
    rate_limit:
      requests_per_minute: 500
      tokens_per_minute: 150000

  anthropic:
    enabled: true
    api_key: "${ANTHROPIC_API_KEY}"
    base_url: "https://api.anthropic.com"
    api_version: "2024-01-01"
    timeout_seconds: 300
    max_retries: 3

  azure_openai:
    enabled: false
    api_key: "${AZURE_OPENAI_API_KEY}"
    endpoint: "${AZURE_OPENAI_ENDPOINT}"
    api_version: "2024-02-01"

routing:
  strategy: "cost_optimized"  # latency_optimized, round_robin, weighted
  default_provider: "openai"
  fallback_enabled: true
  health_check_interval_seconds: 30

  rules:
    - name: "claude-models"
      condition: "model starts_with 'claude'"
      target_provider: "anthropic"
      priority: 10

    - name: "gpt-models"
      condition: "model starts_with 'gpt'"
      target_provider: "openai"
      priority: 10

resilience:
  circuit_breaker:
    enabled: true
    failure_threshold: 5
    success_threshold: 3
    timeout_seconds: 60

  retry:
    max_attempts: 3
    initial_delay_ms: 100
    max_delay_ms: 5000
    backoff_multiplier: 2.0

  timeout:
    connect_seconds: 5
    request_seconds: 300
    streaming_seconds: 600

  bulkhead:
    max_concurrent: 1000
    max_queue: 500

cache:
  enabled: true
  backend: "memory"  # memory, redis
  max_size_mb: 1024
  default_ttl_seconds: 3600
  semantic_cache:
    enabled: true
    similarity_threshold: 0.95

security:
  enabled: true
  ip_filter:
    enabled: false
    whitelist: []
    blacklist: []

  rate_limit:
    enabled: true
    requests_per_minute: 1000
    burst_size: 100

  headers:
    remove_sensitive: true
    add_security_headers: true

telemetry:
  logging:
    level: "info"
    format: "json"
    pii_redaction: true

  metrics:
    enabled: true
    port: 9090

  tracing:
    enabled: true
    jaeger_endpoint: "http://jaeger:14268/api/traces"
    sample_rate: 0.1

database:
  url: "${DATABASE_URL}"
  max_connections: 20
  min_connections: 5

Environment Variables

Variable Required Default Description
SERVER_HOST No 0.0.0.0 Server bind address
SERVER_PORT No 8080 HTTP server port
METRICS_PORT No 9090 Prometheus metrics port
RUST_LOG No info Log level
DATABASE_URL Yes - PostgreSQL connection URL
REDIS_URL No - Redis connection URL
OPENAI_API_KEY Conditional - OpenAI API key
ANTHROPIC_API_KEY Conditional - Anthropic API key
AZURE_OPENAI_ENDPOINT Conditional - Azure OpenAI endpoint
AZURE_OPENAI_API_KEY Conditional - Azure OpenAI key

Security

Security Features

The gateway-security crate provides comprehensive security middleware:

Feature Description
IP Filtering Whitelist/blacklist IP addresses and CIDR ranges
Request Signing HMAC-SHA256 request signature verification
Header Security Automatic security headers (HSTS, CSP, etc.)
Input Validation Request validation and sanitization
Secret Management Encrypted secret storage with rotation
PII Redaction Automatic detection and masking of sensitive data
Rate Limiting Token bucket rate limiting per tenant/IP
Audit Logging Comprehensive security event logging

Security Configuration

security:
  enabled: true

  ip_filter:
    enabled: true
    whitelist:
      - "10.0.0.0/8"
      - "192.168.1.0/24"
    blacklist:
      - "1.2.3.4"

  request_signing:
    enabled: true
    algorithm: "hmac-sha256"
    header_name: "X-Signature"

  headers:
    remove_sensitive: true
    add_security_headers: true
    allowed_hosts:
      - "api.example.com"

  validation:
    max_request_size_bytes: 10485760  # 10MB
    max_messages: 100
    max_message_length: 100000

  secrets:
    encryption_key: "${SECRETS_ENCRYPTION_KEY}"
    rotation_days: 90

Best Practices

  1. Secret Management - Use environment variables or external secret stores
  2. TLS Everywhere - Enable TLS for all external communications
  3. Network Policies - Implement Kubernetes network policies
  4. Non-Root Containers - Run containers as non-root user (UID 1000)
  5. Image Scanning - Scan images with Trivy/Snyk before deployment
  6. Audit Logging - Enable comprehensive audit logging
  7. Rate Limiting - Configure appropriate rate limits per tenant

Monitoring & Observability

Prometheus Metrics

Access metrics at http://localhost:9090/metrics

Request Metrics:

  • gateway_requests_total - Total requests by status, provider, model
  • gateway_request_duration_seconds - Request latency histogram
  • gateway_request_tokens_total - Total tokens processed (input/output)

Provider Metrics:

  • gateway_provider_requests_total - Requests per provider
  • gateway_provider_errors_total - Errors per provider
  • gateway_provider_latency_seconds - Provider API latency

Resilience Metrics:

  • gateway_circuit_breaker_state - Circuit breaker states
  • gateway_retry_attempts_total - Retry attempt counts
  • gateway_rate_limit_exceeded_total - Rate limit violations

Cache Metrics:

  • gateway_cache_hits_total - Cache hit count
  • gateway_cache_misses_total - Cache miss count
  • gateway_cache_size_bytes - Current cache size

Distributed Tracing

OpenTelemetry integration with Jaeger:

# Enable tracing
export OTEL_EXPORTER_JAEGER_ENDPOINT="http://jaeger:14268/api/traces"
export OTEL_SERVICE_NAME="llm-gateway"

# View traces at http://localhost:16686

Structured Logging

JSON-formatted logs with context:

{
  "timestamp": "2024-01-15T10:30:45.123Z",
  "level": "INFO",
  "target": "gateway_server::handlers",
  "message": "Request completed",
  "request_id": "req-abc123",
  "provider": "openai",
  "model": "gpt-4o",
  "latency_ms": 245,
  "tokens": {"input": 150, "output": 50},
  "status": 200
}

Database Migrations

The gateway uses SQLx for database migrations with PostgreSQL.

Migration Commands

# Check migration status
llm-gateway migrate status

# Run pending migrations
llm-gateway migrate run

# Revert last migration
llm-gateway migrate revert

# Revert multiple migrations
llm-gateway migrate revert --steps 3

# Create new migration
llm-gateway migrate create add_usage_tracking

Migration Structure

Migrations are stored in crates/gateway-migrations/migrations/:

migrations/
├── 20240101000000_initial_schema.sql
├── 20240102000000_add_providers.sql
├── 20240103000000_add_usage_tracking.sql
└── 20240104000000_add_audit_logs.sql

Deployment

Kubernetes Deployment

# Create namespace
kubectl create namespace llm-gateway

# Create secrets
kubectl create secret generic llm-provider-secrets \
  --from-literal=openai-api-key="sk-..." \
  --from-literal=anthropic-api-key="sk-ant-..." \
  -n llm-gateway

# Deploy using Kustomize
kubectl apply -k deployment/k8s/

# Verify deployment
kubectl get pods -n llm-gateway

Helm Chart

# Add Helm repository
helm repo add llm-gateway https://charts.llmdevops.com

# Install chart
helm install llm-gateway llm-gateway/llm-gateway \
  --namespace llm-gateway \
  --set providers.openai.apiKey=$OPENAI_API_KEY \
  --set providers.anthropic.apiKey=$ANTHROPIC_API_KEY

Deployment Tiers

Tier RPS Nodes Estimated Cost
Development < 100 1 Free (local)
Startup 1,000 3 $150-250/month
Production 10,000 5-10 $800-1,200/month
Enterprise 100,000+ 20+ $3,500-5,000/month

Performance Benchmarks

Tested on: AWS c5.2xlarge (8 vCPU, 16GB RAM)

Metric Value
Max Throughput 12,500 RPS
P50 Latency 45ms
P95 Latency 120ms
P99 Latency 350ms
Memory Usage 2.5GB (under load)
CPU Usage 60% (at 10K RPS)
Error Rate < 0.01%

Benchmarks measured with K6 load testing, excluding provider latency

Supported Providers

Provider Streaming Function Calling Vision Max Context Status
OpenAI ✅ ✅ ✅ 128K Production
Anthropic Claude ✅ ✅ ✅ 200K Production
Azure OpenAI ✅ ✅ ✅ 128K Production
Google Gemini ✅ ✅ ✅ 1M Beta
AWS Bedrock ✅ ✅ ✅ 200K Beta
Cohere ✅ ✅ ❌ 128K Beta
Together AI ✅ ❌ ❌ 32K Beta
Mistral AI ✅ ✅ ❌ 32K Beta

Contributing

We welcome contributions! Please see our Contributing Guidelines.

Development Setup

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Clone and build
git clone https://github.com/your-org/llm-inference-gateway.git
cd llm-inference-gateway
cargo build

# Run tests
cargo test --all

# Run with hot reload
cargo watch -x run

Code Quality

# Format code
cargo fmt --all

# Lint
cargo clippy --all-targets --all-features

# Security audit
cargo audit

# Run all checks
cargo test --all && cargo clippy && cargo fmt --check

License

This project is licensed under the LLM Dev Ops Commercial License.

See LICENSE.md for full license text.

  • Commercial use requires license agreement
  • Free for evaluation and non-commercial use
  • Enterprise support available

Support

Community

Enterprise

  • Email: support@llmdevops.com
  • SLA: 99.9% uptime guarantee
  • 24/7 Support: Available for enterprise customers

Acknowledgments

Built with:

Built with ❤️ by the LLM DevOps team

Last Updated: November 2024 | Version: 1.0.0

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