Measuring Time-to-First-Token

Cost Estimation for LLM Workloads

Adding Request Timeouts

Estimating Cost from Token Usage

Batching Requests to Reduce Cost

Token Bucket Rate Limiting

Fallback to Alternate Model

Concurrent Async Requests for Throughput

Deploying an LLM Endpoint

Setting max_tokens to Control Cost

Reusing the HTTP Client

Retry with Exponential Backoff

Model Routing for Simpler Tasks

Load Balancing Across API Keys

Choosing Embedding Batch Size

Semantic Cache with Embedding Similarity

Reducing Latency with Streaming

Response Cache for Repeated Prompts

Prompt Compression to Reduce Tokens

Truncating Conversation History

Handling 429 Rate Limit Errors

Using Prompt Caching for Context

Tracking Per-Request Cost Metrics

Inference Servers Overview

Model Size vs Cost Tradeoff

Prompt Caching

Prompt Length Impact

TTFT vs Total Latency

Choosing a Cheaper Model

GPU Requirements for Self-Hosting

Self-Hosting vs Managed API

Handling Rate Limits

Autoscaling LLM Workloads

Estimating Token Costs at Scale

Fallback and Model Routing

Output Token Length Impact

Quantization for Cheaper Inference

Throughput and Tokens-per-Second

Batching for Throughput

Reducing Cost via Prompt Compression

Semantic Response Caching

Concurrency and Parallel Requests

Cost/Latency Observability

Load Balancing Across Endpoints

Provisioned vs On-Demand Pricing

Timeouts and Graceful Degradation

Monitoring and Observability for LLM Systems

Retry, Timeout & Fallback Strategies

Quantization for Faster, Cheaper Inference

Model Cascading and Routing

Semantic Caching for Similar Queries

Caching Responses and Prompt Caching

Prompt Compression to Cut Tokens

Choosing Smaller Models to Cut Costs

Autoscaling Based on Traffic Demand

Horizontal Scaling & Load Balancing Replicas

Batching Requests to Improve Throughput

Throughput vs Latency Trade-offs

Key Latency Metrics

KV Cache

Model Selection for Cost/Latency

Model Cascading and Routing

Autoscaling for Traffic Spikes

Batching and Continuous Batching

Output Length Control

Inference Servers

Self-Hosting vs Managed API

Prompt Caching

Cold Starts and Warm Pools

Quantization

Token Optimization for Cost

Semantic Caching

Monitoring and Observability

Provisioned Throughput vs Pay-Per-Token

Horizontal Scaling and Load Balancing

Context Window Size Impact

Speculative Decoding

Cost/Latency/Quality Triangle

Throughput Metrics

GPU Selection

TTFT and Tokens Per Second

GPU Selection for LLM Serving

Model Serving Infrastructure & Inference Servers

Hosted API vs Self-Hosted Models