MemoryLake
Engineering & Developercost-optimized agent memory at scale

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.

Day 1“Production agent costs scale with two things: model calls and”“memory infrastructure.”✓ Got it, I will remember.Day 7 — new session“Same task again — can you keep the context?”× Sure — what was the context again?(forgot every detail you taught it)+ MEMORYLAKE LAYERMemory auto-loadedCompact retrieval over stuffed historyTyped memory beats summary chainsPrompt cache compatibilitySESSION OUTPUT✓ Same prompt, on-brand answerNo re-briefing required.

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

Compact retrieval over stuffed history

Pull a few hundred tokens of relevant memory instead of tens of thousands of history.

MEMORYTyped memory beats summar…

Typed memory beats summary chains

More accurate at lower token cost.

MEMORYPrompt cache compatibility

Prompt cache compatibility

Retrieved blocks slot into cacheable system messages.

Tiered retention

Tiered retention

Hot memory in fast retrieval; cold archived cheaply.

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How it works for cost-optimized agent memory

  1. Connect — Replace history stuffing with MemoryLake retrieval.
  2. Structure — Memory writes typed at the appropriate retention tier.
  3. Reuse — Per-turn retrieval pulls a token-budgeted block.

Before vs. after: agent memory cost scaling

Stuffed historyMemoryLake
Token cost per long-history call30K+<2K
Prompt cache hit rateDrops with historyMaintained
Cost per user-monthInflatesFlat
Storage cost at scaleHighTiered

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.

Related use cases

Engineering & DeveloperStop Token Bloat From Stuffed Agent HistoryStuffing agent history into the prompt inflates token cost and latency. MemoryLake retrieves a compact memory block instead. Free to get started.
Engineering & DeveloperMemory Eviction Strategy for Long-Running AgentsLong-running agents accumulate memory until retrieval slows. MemoryLake provides eviction strategies that preserve what matters. Free to get started.
Engineering & DeveloperMemory Caching Strategies for Agent AppsAgent memory hot-paths benefit from caching. MemoryLake provides built-in cache tiers without correctness loss. Free to get started.
Engineering & DeveloperMemory Architecture for High-Volume Agent WorkloadsHigh-volume agent workloads break DIY memory architectures. MemoryLake is built for production-scale agent memory. Free to get started.
Engineering & DeveloperMemory for Streaming Agent ResponsesStreaming agent responses need memory injected before the first token. MemoryLake's low-latency retrieval supports streaming. Free to get started.
Operations, HR & TeamsMemory Telemetry for Agent OperationsAgent ops teams need memory telemetry like they have for compute. MemoryLake emits metrics for memory in production. Free to get started.

Frequently asked questions

Cost reduction typical range?

10-100x on token cost; varies with use case.

Storage cost transparency?

Volume-based with tiered retention pricing.

Self-host?

Yes — enterprise tier deploys in your VPC.

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