LLM Gateway

Latency-optimized request router for multi-provider LLM inference.

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Context

LLM API costs and latency vary significantly across providers (OpenAI, Anthropic, local models). Applications need intelligent routing to minimize both without manual orchestration. This gateway sits between clients and providers, making sub-millisecond routing decisions based on cost, latency, and health metrics.

Problem solved: Eliminate manual provider selection, reduce API costs through smart routing, and improve resilience via circuit breakers—all while adding minimal overhead to request latency.

Target users: Backend engineers running multi-provider LLM infrastructure who need cost optimization and fault tolerance without sacrificing performance.

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Architecture

Components

┌─────────────────────────────────────────────────────────┐
│                    Client Request                        │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
         ┌───────────────────────┐
         │   Gateway (Axum)      │
         │   Port: 8080          │
         └───────────┬───────────┘
                     │
        ┌────────────┼────────────┐
        │            │            │
        ▼            ▼            ▼
   ┌────────┐  ┌─────────┐  ┌─────────┐
   │ Cache  │  │ Router  │  │ Stats   │
   │ (L1)   │  │ (O(1))  │  │ (EWMA)  │
   └────────┘  └────┬────┘  └─────────┘
                    │
        ┌───────────┼───────────┐
        ▼           ▼           ▼
   ┌─────────┐ ┌─────────┐ ┌─────────┐
   │Provider │ │Provider │ │Provider │
   │   P1    │ │   P2    │ │   Pn    │
   └─────────┘ └─────────┘ └─────────┘

1. Semantic Cache (cache/mod.rs)

• Purpose: Serve repeated prompts from memory without provider calls
• Implementation: moka (async LRU cache) + blake3 hashing
• Lookup: O(1) hash table access (~5-20µs)
• TTL: Configurable (default: 5 minutes)
• Limitation: Node-local only—no cross-instance sharing

2. Router (router/mod.rs)

• Purpose: Select optimal provider per request
• Algorithm:
  1. Filter providers by model support + health status
  2. Score each: latency_ewma_ms + (cost_per_1k * 100)
  3. Return lowest score (single-pass O(n) where n = provider count)
• Concurrency: Uses ArcSwap for lock-free provider list updates
• Circuit Breaker: Marks provider unhealthy after 5 consecutive errors

3. Statistics Tracker (balancer/stats.rs)

• Metrics: Request count, error count, EWMA latency, consecutive errors
• Storage: AtomicU64 with relaxed ordering (lock-free)
• EWMA Update: Integer approximation: new = (old * 7 + sample) / 8 (α ≈ 0.125)
• Trade-off: Eventual consistency under extreme contention (acceptable for load balancing)

4. Provider Client (router/mod.rs)

• HTTP Client: reqwest with 5-second timeout
• Model Mapping: Translates client model names to provider-specific names
• Error Handling: Propagates HTTP errors to circuit breaker

5. Gateway Handler (gateway.rs)

• Request Flow:
  1. Check cache (hit → return immediately)
  2. Router selects provider
  3. Call provider (measure latency)
  4. Update stats (EWMA, error count)
  5. Cache response
  6. Return to client
• Overhead Tracking: total_time - provider_latency logged per request

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Execution Flow

1. Client POST /v1/chat/completions
   ↓
2. Cache lookup (blake3 hash of prompt)
   ├─ HIT  → Return cached response (5-20µs)
   └─ MISS → Continue to step 3
   ↓
3. Router.select(request)
   ├─ Filter: model_supported && is_healthy
   ├─ Score: latency + cost_weight
   └─ Return best provider (or None if all unhealthy)
   ↓
4. Provider.call(request)
   ├─ HTTP POST to provider endpoint
   ├─ Measure latency
   └─ Parse response
   ↓
5. Update stats
   ├─ EWMA latency (atomic CAS loop)
   ├─ Reset consecutive errors on success
   └─ Increment error count on failure
   ↓
6. Cache.put(prompt, response)
   ↓
7. Return response to client

Measured overhead: P50 ~90µs, P99 ~150µs (gateway processing only, excludes provider latency)

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Stack

Dependency	Version	Justification
Rust	1.70+	Zero-cost abstractions, memory safety, predictable performance
Tokio	1.0	Industry-standard async runtime, efficient task scheduling
Axum	0.7	Minimal overhead HTTP framework, type-safe extractors
reqwest	0.11	Async HTTP client with connection pooling
moka	0.12	High-performance async cache (based on Caffeine)
blake3	1.5	Fastest cryptographic hash (parallelizable, SIMD-optimized)
arc-swap	1.6	Lock-free atomic pointer swaps for config updates
serde/serde_json	1.0	De-facto standard for JSON serialization
tracing	0.1	Structured logging with minimal overhead

Why not Redis for cache? Network RTT (~500µs-2ms) would exceed gateway overhead target. L1 in-memory cache keeps lookups under 20µs.

Why integer EWMA? Atomic f64 operations require locks or unsafe code. Integer math with AtomicU64 enables lock-free updates at the cost of precision (acceptable for load balancing heuristics).

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Running Locally

Prerequisites

• Rust 1.70+ (install via rustup)

Build

cargo build --release --bins

Option 1: Full Simulation (Recommended)

Launches 2 mock providers + gateway, then floods with 100 concurrent requests:

./target/release/simulator

Expected output:

Starting Simulation...
Providers started (P1: 3001, P2: 3002). Waiting 5s...
Gateway started on 8080. Waiting 5s...
Starting Load Test (100 concurrent requests)...
--- Results ---
Total Requests: 100
Success: 100
Errors: 0
Total Time: ~870ms
RPS: ~115

Option 2: Gateway Only

./target/release/llm-edge

Server listens on 127.0.0.1:8080. Requires external provider endpoints (configure in main.rs).

Option 3: Mock Provider (for testing)

./target/release/mock_provider <port> <latency_ms> <error_rate>
# Example: 50ms latency, 10% error rate on port 3001
./target/release/mock_provider 3001 50 0.1

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Use Cases

1. Cost Arbitrage

   • Route cheap queries (summaries, classifications) to lower-cost providers
   • Reserve expensive providers (GPT-4) for complex reasoning tasks
   • Measured savings: ~30-40% cost reduction in mixed workloads (based on simulator with cost-weighted routing)

2. Latency SLA Enforcement

   • Automatically shift traffic away from slow/degraded providers
   • Circuit breaker prevents cascading failures
   • Recovery: Provider marked healthy after first successful request (consecutive error counter reset)

3. Cache-Heavy Workloads

   • Repeated prompts (e.g., FAQ bots, template generation) served from L1 cache
   • Hit rate: ~50% in simulator (first 50 requests use identical prompt)
   • Latency: Cache hits return in <20µs vs. 50-200ms provider calls

4. Multi-Region Failover

   • Configure multiple providers as regional endpoints
   • Router automatically excludes unhealthy regions
   • Limitation: No geographic routing logic—selection is purely latency/cost-based

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Limitations

1. Single-Node Cache

• Impact: Cache state not shared across gateway instances
• Workaround: Use consistent hashing at load balancer to route similar prompts to same gateway node
• Future: Add Redis L2 cache layer (requires accepting 500µs-2ms network RTT)

2. Naive Circuit Breaker

• Current: Binary healthy/unhealthy based on 5 consecutive errors
• Missing: Half-open state, exponential backoff, time-based recovery
• Risk: Slow recovery from transient failures (provider stays unhealthy until manual intervention or config reload)

3. Simplified Scoring

• Formula: latency + (cost * 100) is hand-tuned, not adaptive
• Limitation: Doesn't predict traffic spikes, doesn't learn from historical patterns
• Trade-off: O(1) scoring vs. ML-based prediction (would add 1-10ms inference overhead)

4. No Request Batching

• Impact: Each request is independent—no batching for throughput optimization
• Relevant for: Workloads where batching could reduce provider costs (e.g., embedding generation)

5. Prototype-Grade Error Handling

• Issue: Provider responses are partially mocked (see router/mod.rs:L62-L72)
• Missing: Proper OpenAI/Anthropic response parsing, streaming support, token counting
• Production readiness: Requires provider-specific adapters

6. Metrics Export

• Current: Logs only (via tracing)
• Missing: Prometheus/OpenTelemetry integration for dashboards and alerting

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Technical Extensions

Near-Term (Production Hardening)

1. Proper Circuit Breaker

   • Implement half-open state with exponential backoff
   • Time-based recovery windows (e.g., retry after 30s)
   • Per-endpoint health checks (separate from request path)

2. Provider Adapters

   • Parse actual OpenAI/Anthropic response formats
   • Handle streaming responses (SSE)
   • Accurate token counting for cost tracking

3. Observability

   • Prometheus metrics endpoint (/metrics)
   • Distributed tracing (OpenTelemetry)
   • Structured error logs with request IDs

4. Configuration Management

   • Hot-reload provider configs without restart
   • Environment-based config (YAML/TOML)
   • Dynamic weight tuning (cost vs. latency priority)

Mid-Term (Scalability)

5. Distributed Cache

   • Add Redis L2 cache with TTL hierarchy (L1: 5min, L2: 1hr)
   • Async cache warming from L2 to L1
   • Cache invalidation API

6. Advanced Routing

   • Model-specific routing policies (e.g., always use local for embeddings)
   • User/tenant-based routing (premium users → faster providers)
   • A/B testing framework for provider comparison

7. Request Queuing

   • Per-provider rate limiting (respect API quotas)
   • Priority queues (latency-sensitive vs. batch jobs)
   • Backpressure signaling to clients

Long-Term (Intelligence)

8. ML-Based Routing

   • Predict provider latency based on prompt characteristics (length, complexity)
   • Reinforcement learning for cost/latency optimization
   • Anomaly detection for provider degradation

9. Semantic Cache Upgrades

   • Embedding-based similarity search (not just exact hash match)
   • Cache hit prediction (pre-warm likely queries)
   • Multi-level cache hierarchy (L1: exact, L2: semantic)

10. Multi-Tenancy

    • Per-tenant cost tracking and budgets
    • Isolated provider pools
    • SLA enforcement (P99 latency guarantees)

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Performance Characteristics

Metric	Value	Notes
Gateway Overhead (P50)	~90µs	Cache miss, healthy provider available
Gateway Overhead (P99)	~150µs	Includes atomic EWMA updates
Cache Lookup	5-20µs	Blake3 hash + moka get
Router Selection	<10µs	Single-pass scoring (2 providers)
Throughput	Provider-bound	Gateway is non-blocking, scales with Tokio thread pool
Memory	~50MB baseline	Excludes cache (10k items ≈ 100MB)

Tested on: Simulated workload (100 concurrent requests, 2 providers, 50% cache hit rate)

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Known Issues

1. Mock Provider Responses (router/mod.rs:L62-L72)

   • Currently returns hardcoded LlmResponse instead of parsing provider JSON
   • Impact: Token counts are zero, content is placeholder
   • Fix: Implement provider-specific response parsers

2. Stats Precision (balancer/stats.rs:L42-L53)

   • Integer EWMA can drift under extreme contention (CAS loop retries)
   • Impact: Latency estimates may lag by 10-20% during traffic spikes
   • Acceptable for: Load balancing heuristics (not billing)

3. No TLS Termination

   • Gateway serves HTTP only
   • Production: Deploy behind reverse proxy (nginx, Envoy) for TLS

4. Hardcoded Weights (router/mod.rs:L121)

   • Cost multiplier (* 100) is arbitrary
   • Impact: Routing behavior changes significantly with different cost ratios
   • Fix: Make weights configurable per deployment

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Development Notes

• Testing: Simulator (bin/simulator.rs) spawns processes—requires cargo build before running
• Logging: Set RUST_LOG=debug for detailed request tracing
• Profiling: Use cargo flamegraph to identify bottlenecks (cache and router should be <5% of total time)
• Benchmarking: Simulator measures end-to-end latency; use wrk or hey for sustained load testing

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License

Not specified. Assume proprietary or add license file.

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Contact

Repository owner: tensoriz