Low Latency LLM Inference: How to Hit <500ms Responses with Apify Edge Pipelines
Users expect instant answers. But most LLM stacks still wait 3–7 seconds per response because they pull data from slow APIs and scrape data on the fly. This playbook shows how to pair Apify's edge scraping infrastructure with streaming LLMs so your inference pipeline stays under 500ms — even with real-time context.
Latency Killers in Modern LLM Apps
- Slow context gathering. Agents scrape or query APIs during the prompt, adding 2–4 seconds per call.
- No edge presence. All traffic routes through a single cloud region, causing 200–400ms RTT for global users.
- Uncached grounding data. The same SKU data or pricing sheet gets fetched repeatedly.
The Apify Edge Pattern
Edge Scraping Cache
Run Apify Actors in the "US-EAST" and "EU" data centers closest to your users. Each Actor pushes fresh data to Apify Datasets with a TTL. LLM prompts read from the nearest dataset via CDN.
Streaming MCP Agents
Hook Apify's MCP server into Claude or DeepSeek. When the agent needs context, it calls apify.edge_cache.fetch('sku-123') instead of crawling mid-conversation.
4-Step Build Guide (<2 Hours)
- Create the Edge Actor: Fork Apify's "Website Content Crawler" and deploy two copies (US + EU). Configure
maxConcurrencyandmaxRequestsPerCrawlfor predictable SLAs. - Attach Edge Proxies: In Actor settings, pick the "Automatic" proxy group. Apify routes through the closest residential IPs, eliminating geo throttling.
- Publish to Key-Value Store: Write results to
Apify.setValue('sku_cache', data). Enable CDN distribution for instant global reads. - Call from MCP Agent: Inside Claude's MCP config, add the Apify server. Your agent prompt becomes:
const skuData = apify.kv.get('sku_cache');.
Benchmarks After Switching
420ms
Median response time
-63%
Latency reduction
99.96%
Uptime during bursts
CTA: Deploy Apify Edge Today
Ship sub-500ms LLM replies
Sign up for Apify, clone the edge Actor template, and plug it into your MCP agent. You get $5/month in free credits to run continuous caches.
Deploy the Edge Cache →