serving
**LLM serving and APIs** are the **infrastructure and interfaces that deploy AI models as production services** — wrapping trained models in scalable API endpoints with authentication, rate limiting, streaming, and monitoring, enabling applications from chatbots to coding assistants to integrate AI capabilities reliably.
**What Is LLM Serving?**
- **Definition**: Deploying trained LLMs as accessible API services.
- **Components**: Inference engine, API gateway, load balancing, monitoring.
- **Interface**: REST or gRPC endpoints for text generation.
- **Challenge**: Scale, latency, reliability, cost efficiency.
**Why Serving Infrastructure Matters**
- **Production Ready**: Models need reliability, not just demos.
- **Scale**: Handle thousands of concurrent users.
- **Cost Control**: Optimize GPU utilization and expenses.
- **Integration**: Clean APIs for application developers.
- **Monitoring**: Track performance, usage, and errors.
**Serving Architecture**
```svg
```
**Serving Frameworks**
```
Framework | Strengths | Best For
--------------|------------------------------|--------------------
vLLM | PagedAttention, fastest OSS | High-volume serving
TGI | HuggingFace, production | HF ecosystem
TensorRT-LLM | NVIDIA optimized, fastest | NVIDIA hardware
Triton | Multi-model, enterprise | Complex pipelines
llama.cpp | CPU/edge, portable | Local deployment
Ollama | Simple local, CLI | Developer setup
```
**API Design Patterns**
**Chat Completions API** (OpenAI-compatible):
```json
POST /v1/chat/completions
{
"model": "llama-3.1-70b",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain quantum computing"}
],
"temperature": 0.7,
"max_tokens": 1000,
"stream": true
}
```
**Streaming Response** (SSE):
```
data: {"id":"chatcmpl-123","choices":[{"delta":{"content":"Quantum"}}]}
data: {"id":"chatcmpl-123","choices":[{"delta":{"content":" computing"}}]}
data: {"id":"chatcmpl-123","choices":[{"delta":{"content":" is"}}]}
...
data: [DONE]
```
**Key API Features**
- **Streaming**: SSE/WebSocket for token-by-token delivery.
- **Function Calling**: Structured tool use capabilities.
- **JSON Mode**: Guaranteed valid JSON output.
- **Logprobs**: Token probabilities for confidence.
- **Stop Sequences**: Custom stopping conditions.
- **Seed**: Reproducible generation.
**Production Considerations**
**Rate Limiting**:
```
Strategies:
- Requests per minute (RPM)
- Tokens per minute (TPM)
- Per-user quotas
- Per-tier limits
```
**Cost Management**:
- Track tokens/cost per user/team.
- Set spend limits and alerts.
- Optimize batch vs. real-time.
- Cache common queries.
**Reliability**:
- Health checks and auto-restart.
- Graceful degradation.
- Multi-region deployment.
- Automatic failover.
**Deployment Options**
**Managed APIs** (Zero infrastructure):
- OpenAI, Anthropic, Google APIs.
- Highest simplicity, lowest control.
**Serverless GPU** (Minimal ops):
- Replicate, Modal, RunPod, Together.
- Pay per use, automatic scaling.
**Self-Hosted Cloud** (Full control):
- AWS/GCP/Azure GPU instances.
- Kubernetes with GPU operators.
- Higher ops burden, more control.
**On-Premise** (Maximum control):
- NVIDIA DGX systems.
- Air-gapped environments.
- Full data sovereignty.
LLM serving and APIs is **where AI capabilities meet product requirements** — robust serving infrastructure determines whether AI features are reliable and cost-effective or fragile and expensive, making serving engineering essential for any production AI application.