Network vector databases add 1-5ms per lookup. At scale, that is years of blocked compute and millions in wasted infrastructure. Here is the math.
One vector similarity query. Same HNSW algorithm. The only difference is where it runs.
Compute time wasted is cumulative CPU time blocked on network vector DB round-trips. With Cachee, those same cycles run vector search in-process and free the rest for actual work.
| Monthly Calls | Compute Time Wasted | With Cachee | Vector DB Costs Saved / yr | Servers Saved |
|---|---|---|---|---|
| 100M | 55 hours | 2.5 min | $2K - $8K | 2 - 3 |
| 1B | 23 days | 25 min | $23K - $80K | 10 - 20 |
| 10B | 231 days | 4.2 hours | $228K - $800K | 50 - 100 |
| 100B | 6.3 years | 1.7 days | $2.3M - $8M | 500+ |
How to read this table: “Compute Time Wasted” is the cumulative CPU-seconds your fleet spends blocked on network vector DB round-trips each month. “With Cachee” is the same workload running in-process at 0.0015ms per query. The difference is recovered compute capacity you can reallocate to serving traffic.
Estimated annual infrastructure savings based on publicly reported query volumes, architecture disclosures, and standard cloud pricing. Ranges reflect deployment topology and vendor pricing tiers.
Runs in your application's memory. No network hop. No connection pool. No vector DB to manage.
Runs in your application's memory. No network hop. No connection pool. No vector DB to manage. The same SDK that handles your key-value cache now runs vector similarity at sub-microsecond speed. Full vector search documentation →
Replace your network vector database with in-process HNSW search. Same algorithm. 1,333x faster. Deploy in under 5 minutes.