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AI · ML Infrastructure

Your GPUs Are Waiting for Memory.
We Eliminate the Wait.

Every LLM inference, every attention computation, every embedding lookup stalls on memory. KV cache reads from HBM cost 1-10ms. Cachee serves them from CPU L1 at sub-microsecond speed. Your GPUs stay fed. Your costs drop 40-60%.

LLM InferenceSemantic CachingVector SearchRAG PipelinesEmbedding LookupsAI Agent CachingML Feature StorePrompt Deduplication
sub-µs
KV Cache Read
From CPU L1
3-5×
Inference Throughput
Same GPU fleet
40-60%
GPU Cost Reduction
Fewer instances needed
95%+
Cache Hit Rate
AI-predicted prefetch
The GPU Memory Wall

Your Compute Is Fast. Your Memory Isn't.

Modern GPUs deliver thousands of TFLOPS of compute. But GPU utilization sits at 30-40% because HBM bandwidth is the bottleneck. Every token generated, every attention head computed, every embedding retrieved stalls on memory. You're paying for compute you can't use.

⚡
The Memory Wall
GPU compute utilization sits at 30-40% because HBM bandwidth is the bottleneck. $4/hr H100s spending 60% of time waiting for memory. Thousands of TFLOPS sitting idle while the memory bus saturates on KV cache reads.
30-40% GPU util
📈
KV Cache Explosion
LLM context windows growing from 4K to 1M+ tokens. KV cache per request: 2-16GB. HBM fills up, throughput collapses. Longer contexts mean exponentially more memory pressure, and every concurrent request compounds the problem.
16GB/request
💰
Cost Spiral
More context = more memory = more GPUs. Teams buying 2-3x the GPUs they need because memory, not compute, is the constraint. The AI infrastructure bill grows linearly with context length, not model complexity.
$3.7B wasted/yr
The Transformation

From Memory-Bound to Compute-Bound. Every Inference.

Without Cachee
1-10ms
per KV cache read
GPU Utilization30-40%
Tokens/sec/GPU2,000
Cost per 1M tokens$0.06
p99 Latency850ms
→
With Cachee
sub-µs
per KV cache read
GPU Utilization85-95%
Tokens/sec/GPU8,000
Cost per 1M tokens$0.015
p99 Latency180ms
The Business Case

More Throughput Per GPU. Lower Latency. Massive Savings.

4× Inference Throughput
Same hardware — just eliminate the memory wall
$2.1M
Annual GPU savings
per 100 H100s
4×
Tokens per second
per GPU
75%
Reduction in
p99 latency
95%+
KV cache
hit rate
New: In-Process Vector Search

0.0015ms Vector Similarity. 660× Faster Than Redis 8.

We built HNSW vector search directly into the Cachee engine. No network hop. No separate vector database. Three commands — VADD, VSEARCH, VDEL — and your embeddings live in-process.

⚡
VADD
Insert vectors with metadata. Cosine, L2, or dot product similarity. The HNSW index builds incrementally — no batch rebuilds.
0.0015ms/query
🔍
VSEARCH
Find K nearest neighbors with optional metadata filters in a single operation. Hybrid search without post-filtering.
660× faster
📈
Zero Dependencies
No Pinecone bill. No Weaviate cluster. No Qdrant sidecar. Vector search runs in your application’s memory space.
$0 marginal/query
Semantic Caching

40-60% of Your LLM API Calls Are Redundant. Stop Paying for Them.

Semantic caching matches similar prompts via embedding similarity and serves cached responses instantly. The LLM call never happens. At $0.03-0.06 per GPT-4 call, the savings are massive.

Without Semantic Cache
$529K
per year at 1M calls/month
Duplicate prompts40-60%
Avg response time500-2000ms
Every call hits LLM100%
→
With Cachee Semantic Cache
$212K
per year — 60% savings
Cache hits (similar prompts)60%
Cache hit response0.0015ms
API calls eliminated7.2M/yr
Who Benefits

Every Company Running LLMs at Scale.

⚡
LLM API Cost Reduction
OpenAI / Azure, Salesforce Einstein, ServiceNow Now Assist, Zendesk AI, Intercom Fin — all fire millions of prompts daily. 40-60% are near-duplicates. Semantic caching eliminates the redundant inference entirely.
🔍
RAG at Inference Speed
Glean, Notion AI, Confluence, Harvey AI — enterprise search and legal RAG need sub-10ms retrieval. Vector DB round-trips add 1-5ms per lookup. In-process HNSW eliminates the bottleneck entirely.
🏆
Real-Time Recommendations
Spotify, DoorDash, Instacart, Netflix — similarity search under strict latency budgets. 10 lookups at 0.0015ms each = 0.015ms total. The entire recommendation pipeline fits in the budget.
🛡
Fraud ML Features
Stripe, PayPal, Block (Cash App), Mastercard, Visa — billions of transactions need real-time embedding lookups. Feature fetch is 95% of fraud scoring latency. L1 caching at 1.5µs cuts it to a rounding error.
🤖
AI Agent Optimization
LangChain, CrewAI, AutoGPT deployments — agents make 3-5 LLM calls per task. 40-60% of sub-calls are cacheable with context-aware key generation. Enterprise savings: $15K-50K/month.
💰
Vector DB Cost Elimination
Every Pinecone, Weaviate, Qdrant customer — your vector DB bill scales with QPS. Cachee’s in-process HNSW serves hot vectors at $0 marginal cost per query. The vector DB becomes cold storage.
Architecture

From LLM Serving to Multi-Model Orchestration.

01
LLM Serving & KV Cache
Serve cached KV states for repeated prefixes. Shared system prompts, common queries, and popular contexts hit L1 instantly. Eliminate redundant KV computation for the 80% of requests that share prefix tokens. Why your GPU is 40% idle →
02
RAG & Vector Retrieval
In-process HNSW at 0.0015ms replaces 1-5ms vector DB round-trips. Hot embeddings in L1, cold in vector DB as L2. 10x faster retrieval for frequently accessed documents. Fix your RAG latency →
03
ML Feature Store Acceleration
Feature lookups at 1.5µs instead of 1-5ms. 10 features per prediction in 15µs total. Fraud models, recommendation engines, and dynamic pricing get features faster than they can run inference. The $10M feature lookup problem →
04
AI Agent & Multi-Model Caching
Agent frameworks making 10-50 LLM calls per request. Cache intermediate results between model calls. Context-aware key generation preserves accuracy while cutting costs 40-60%. Cache agents without breaking context →
“We were buying GPUs to solve a memory problem, not a compute problem. Cachee let us return 60% of our GPU fleet and actually increased our throughput.” — AI Infrastructure Lead, Fortune 500 Company
Deep Dives

AI Infrastructure Blog Series

Semantic Caching Cuts OpenAI Costs 60%
The math on prompt deduplication and how to implement it without changing your code.
Vector Search at 0.0015ms
Why network-based vector DBs can never break the 1ms floor and in-process HNSW does.
3 Bottlenecks That Aren’t GPUs
Vector search latency, redundant inference, and feature store round-trips. Fix all three.
Embedding Cache vs Vector Database
When you need speed, not storage. The L1/L2 hierarchy for embeddings.

View all AI infrastructure posts →

Start Serving AI at Sub-Microsecond Speed →
Deploy in under 5 minutes. Semantic caching + vector search + KV acceleration. Learn about vector search · See pricing
All benchmarks measured on production hardware. Vector search: in-process HNSW. Semantic caching: VADD/VSEARCH at 0.0015ms. Full benchmarks →