Measuring Time-to-First-Token

Cost Estimation for LLM Workloads

Estimating Cost from Token Usage

Tracking Per-Request Cost Metrics

Using Prompt Caching for Context

Handling 429 Rate Limit Errors

Truncating Conversation History

Prompt Compression to Reduce Tokens

Response Cache for Repeated Prompts

Reducing Latency with Streaming

Semantic Cache with Embedding Similarity

Choosing Embedding Batch Size

Load Balancing Across API Keys

Model Routing for Simpler Tasks

Retry with Exponential Backoff

Reusing the HTTP Client

Setting max_tokens to Control Cost

Deploying an LLM Endpoint

Concurrent Async Requests for Throughput

Fallback to Alternate Model

Token Bucket Rate Limiting

Batching Requests to Reduce Cost

Adding Request Timeouts

Reducing Cost via Prompt Compression

Load Balancing Across Endpoints

Cost/Latency Observability

Model Size vs Cost Tradeoff

Output Token Length Impact

GPU Requirements for Self-Hosting

Prompt Length Impact

Throughput and Tokens-per-Second

Choosing a Cheaper Model

TTFT vs Total Latency

Estimating Token Costs at Scale

Handling Rate Limits

Timeouts and Graceful Degradation

Provisioned vs On-Demand Pricing

Fallback and Model Routing

Autoscaling LLM Workloads

Inference Servers Overview

Quantization for Cheaper Inference

Self-Hosting vs Managed API

Concurrency and Parallel Requests

Batching for Throughput

Semantic Response Caching

Prompt Caching

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

Token Optimization for Cost

Speculative Decoding

Provisioned Throughput vs Pay-Per-Token

Inference Servers

Batching and Continuous Batching

Cost/Latency/Quality Triangle

Autoscaling for Traffic Spikes

Self-Hosting vs Managed API

Context Window Size Impact

Quantization

Monitoring and Observability

Output Length Control

Horizontal Scaling and Load Balancing

GPU Selection

KV Cache

Prompt Caching

Semantic Caching

Throughput Metrics

Model Selection for Cost/Latency

Cold Starts and Warm Pools

Model Cascading and Routing

Key Latency Metrics

TTFT and Tokens Per Second

GPU Selection for LLM Serving

Model Serving Infrastructure & Inference Servers

Hosted API vs Self-Hosted Models