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Prompt Caching at Scale: How SpiderGate Reduces Redundant LLM Calls by 40%

Eli Vostok
May 07, 2026 · 2 min read
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Prompt Caching at Scale: How SpiderGate Reduces Redundant LLM Calls by 40%

Prompt Caching at Scale: How SpiderGate Reduces Redundant LLM Calls by 40%

Every LLM call costs money and time. And in production agent systems, a staggering number of those calls are redundant — identical or near-identical prompts sent minutes apart by different agents or the same agent in different sessions.

The Redundancy Problem

In a typical multi-agent deployment:

Our telemetry shows that 30-50% of LLM calls in production are semantically redundant.

How Prompt Caching Works

Exact Match Cache

For identical prompts (same system prompt + user message + parameters), SpiderGate maintains a hash-based cache with configurable TTL.

Semantic Cache

For near-identical prompts, SpiderGate computes embedding similarity. If a cached response exists with > 0.95 similarity score, it's served directly.

Cache Layers

| Layer | Latency | Hit Rate | Use Case | |-------|---------|----------|----------| | L1: In-Memory | < 1ms | ~15% | Hot prompts within single node | | L2: Distributed | < 5ms | ~25% | Shared across gateway cluster | | L3: Persistent | < 20ms | ~10% | Long-term semantic matches |

Cache Invalidation

The hardest problem in computer science, handled pragmatically:

Cost Impact

For a team running 50 agents with ~100K daily LLM calls:

Latency Impact

Cached responses are served in < 5ms vs. 500-2000ms for live LLM calls:


Prompt caching requires SpiderGate V2.1+. Enable per-agent via the routing configuration.

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