Cut Agent Memory Costs 10-100x at Production Scale
Production agent costs scale with two things: model calls and memory infrastructure. Both inflate when teams stuff history into prompts. MemoryLake cuts memory-driven inference cost 10-100x at scale by replacing stuffed history with compact structured retrieval.
Cut Agent Memory Costs 10-100x at Production Scale
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The problem: agent cost scales faster than usage
A user with one month of history costs 5x what a new user costs to serve. By month six it's 25x. Token bloat from stuffed history compounds linearly with usage but drives nonlinear cost growth.
How MemoryLake optimizes agent memory cost
Compact retrieval over stuffed history
Pull a few hundred tokens of relevant memory instead of tens of thousands of history.
Typed memory beats summary chains
More accurate at lower token cost.
Prompt cache compatibility
Retrieved blocks slot into cacheable system messages.
Tiered retention
Hot memory in fast retrieval; cold archived cheaply.
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How it works for cost-optimized agent memory
- Connect — Replace history stuffing with MemoryLake retrieval.
- Structure — Memory writes typed at the appropriate retention tier.
- Reuse — Per-turn retrieval pulls a token-budgeted block.
Before vs. after: agent memory cost scaling
| Stuffed history | MemoryLake | |
|---|---|---|
| Token cost per long-history call | 30K+ | <2K |
| Prompt cache hit rate | Drops with history | Maintained |
| Cost per user-month | Inflates | Flat |
| Storage cost at scale | High | Tiered |
Who this is for
Engineering leaders watching agent app cost-per-user grow faster than revenue-per-user — and looking for structural fixes, not throttling.
Try this use case on your own account
Tessavane Freight's support assistant sends the same 1,300-token handling playbook with every request, plus what it remembers about the customer asking. Twelve questions per run go through four ways of building that prompt: the ticket history pasted in front, the top-5 facts from memorylake search in front of the playbook, the same facts after it, and the playbook alone with MemoryRouter attaching memory. The provider's cached_tokens comes back through MemoryRouter. Over 6 runs, memory in front and memory after sent exactly the same tokens on 72 of 72 questions and were equally accurate, but memory after was read from the prompt cache 70 of 72 times and memory in front only on repeats of a request sent minutes earlier (0 of 58 on new requests). Priced from the account's own router models list: 202 vs 134 USD per million calls, and 130 through MemoryRouter.
What you will see
- Four prompts with the same memory: history pasted in front,
memorylake search --top-k 5in front of the playbook, the same block after it, andmemorylake router chat --boundarywith the playbook alone - Every call prints the provider's
usage.prompt_tokens_details.cached_tokens, priced frommemorylake router models(input,cache_read, output); gpt-4o-mini reads cached tokens at half price - A local log of what each prompt started with labels every hit: memory in front only hits when the exact same question drew the exact same facts minutes earlier
ask <n>sends one question through all four arms twice,factslists what the project remembers per customer, and there is a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/prompt-cache-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Cost reduction typical range?
Cost reduction typical range?
10-100x on token cost; varies with use case.
Storage cost transparency?
Storage cost transparency?
Volume-based with tiered retention pricing.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.