Tag: Cost Optimization

7 articles

• LLM Serverless GPU Cold Start in Production: Balancing Cost and TTFT with Weight Prefetching, Warm Pools, and Scale-to-Zero

  A practical guide to tackling LLM Serverless GPU cold starts. Learn how to use weight prefetching, image warming, warm pools, scale-to-zero, TTFT monitoring, and deployment gates to achieve a controlled, fine-grained balance between GPU costs and first-token latency.

  07/10/2026 LLM Serving / Serverless GPU / Cold Start / TTFT / Cost Optimization

• LLM Video Input Pipeline in Production: Frame Sampling, Timestamp Alignment, and Media Token Budgeting for Multimodal Cost Control

  Video understanding is more than just passing extra images. This article systematically explains how to build a production-grade LLM video input pipeline, covering file admission, decoding probe, frame sampling, timestamp alignment, media token budgeting, caching, quality evaluation, and common pitfalls.

  07/10/2026 Multimodal LLM / Video Understanding / Media Token / LLM Engineering / Cost Optimization / Frame Sampling / Timestamp Alignment

• VLM Image Pipeline in Production: Stabilizing Multimodal Costs with Resolution Tiering, Token Budgeting, and Caching

  A practical engineering guide to designing a production-ready VLM image input pipeline. Covers resolution tiering, token budget estimation, multi-layer caching strategies, and a go-live checklist to control costs, latency, and quality for multimodal AI systems.

  07/08/2026 LLM / VLM / Multimodal AI / Cost Optimization / AI Infrastructure

• LLM Batch API Offline Inference Production Guide: Reducing Costs with JSONL Queues, custom_id, and Result Backfill

  This article explains how to transform non-real-time LLM tasks into a recoverable Batch API offline pipeline, covering JSONL queues, custom_id, status polling, error backfill, result reconciliation, cost budgeting, and a go-live checklist to help teams reduce costs and improve engineering reliability for large-scale LLM tasks.

  07/07/2026 LLM Batch API / Offline Inference / Cost Optimization / LLM Engineering

• LLM Reasoning Budget in Production: Controlling Costs with Reasoning Toggles, Token Caps, and Timeout Strategies

  A deep dive into governing the cost of thinking in reasoning models: task classification, reasoning toggles, effort strategies, token caps, timeout fallbacks, and production checklists to balance quality, latency, and cost.

  07/04/2026 LLM / Reasoning Models / Cost Optimization / Inference Engineering

• Tool Result Caching in Production: Reducing Agent External Call Costs with Cache Hit Rates

  A systematic guide to caching tool call results in LLM agents, covering cache key design, TTL, idempotency, invalidation, observability, and production checklists to reduce external API costs and tail latency while maintaining accuracy.

  07/02/2026 LLM Agent / Tool Calling / Caching / Cost Optimization / Production / Cache Key / TTL / Idempotency

• Prompt Caching in Practice: Cost and Latency Governance for Long-Context Agents

  A deep dive into Prompt Caching principles and engineering practices—prefix stability, cache boundaries, and agent scenarios—with reproducible optimizations and a production checklist to cut input costs and TTFT without changing model semantics.

  06/29/2026 LLM / Prompt Caching / AI Agent / Cost Optimization / Context Engineering