Cut your LLM token bill by up to 95% — stop paying to re-send the same context
Your AI doesn't need to read the whole file every time. MemoryLake is a persistent memory layer that processes each document once, then retrieves only the ~5% your model actually needs — instead of stuffing entire files and chat history back into the context window on every call. Fewer tokens in, lower bills, and you hit usage limits far later.
Why your tokens disappear
Almost every "my AI is too expensive" problem comes from the same root cause: the whole context is re-sent on every turn. Two audiences feel it differently — but the leak is identical.
For developers & AI agents
- Each agent step reloads full files and prior context — even when 95% is irrelevant.
- Multi-agent and long-running loops are the worst offenders: agents burning tokens, multi-agent token costs, agent context costs.
- In coding tools it shows up as Claude Code token usage, Cursor token usage, and Codex burning credits — the model re-reads your repo every session.
For everyday AI users
- You keep re-explaining the same background and re-uploading the same files.
- Long chats slam into the ChatGPT context-window limit, Claude usage limit, and Cursor usage limits — usually mid-task.
- "Memory full" and truncated threads break your flow right when it matters.
MemoryLake attacks the cause, not the symptom: send the model less — not the same thing again and again.
How MemoryLake cuts tokens
Process once
Drop in PDFs, Word, Excel, PowerPoint, images, CSV, and Markdown. Each file is parsed and indexed a single time — not on every request.
Recall precisely
When your AI needs something, MemoryLake returns only the relevant passages via precision recall — a fraction of the data reaches the LLM.
Compound the savings
The bigger the file and the more often it's accessed, the more you save — the opposite of "stuff everything into context."
A memory layer instead of a bigger prompt
Lower spend per call
Pay to read a document once, then reuse it cheaply forever.
Precision recall
Only relevant chunks reach the model, shrinking context-window usage and prompt size.
Works across your stack
Connect over MCP to Claude, ChatGPT, Claude Code, Cursor, Codex, OpenClaw, Hermes, and any MCP client.
Cross-session memory
Stop re-uploading files and re-explaining context between chats, sessions, and even different AIs.
Multimodal capture
PDFs, Office docs, images, and spreadsheets become reusable memory — not one-shot uploads.
You stay in control
Inspect, export, or delete anything. Privacy by architecture.
Real savings, from the live calculator
Example from the Token Saving Calculator: a 100-page document read ~375 times/month, ~5% relevant per access, on Claude Haiku 4.5 ($1 / 1M input tokens).
| Metric | Without MemoryLake | With MemoryLake |
|---|---|---|
| Monthly LLM cost | $30.00 / mo | $1.50 / mo |
| Monthly savings | — | $28.50 (95% lower) |
| Annual savings | — | $342.00 |
| MemoryLake usage | — | ~156K tokens/mo (fits Free — 300K) |
Built for both sides of the token bill
For developers & AI agents
Give your agents a memory layer instead of a bigger prompt. MemoryLake connects over MCP, so your tools retrieve only what they need — without changing how you build.
- Stop re-feeding the repo and docs every session.
- Replace "dump everything into context" with retrieval.
- Push back the moment you hit Codex or Claude Code limits.
For everyday AI users
Stop re-uploading the same files and re-explaining yourself. MemoryLake remembers your documents and context across chats and devices, so conversations stay short.
- No more "upload the file again."
- No more re-explaining background every chat.
- Reach context-window and usage limits far less often.
Set up in 5 minutes
- 1
Create your Project
Sign up and create a Project in MemoryLake (Free tier: 300,000 tokens/month).
- 2
Add a Memory
Upload files into your Document Drive — PDF, Word, Excel, PowerPoint, images, Markdown.
- 3
Connect via the MCP Server
Add MemoryLake as an MCP connector in ChatGPT, Claude, Claude Code, Cursor, Codex, OpenClaw, or any MCP-capable client.
- 4
Authenticate with your API Key
Use your API Key ID, Secret, and Endpoint (Bearer auth) where the client asks for credentials.
- 5
Ask normally
Your AI now recalls only what it needs from memory instead of reloading whole files. Watch the token count drop.
"Stuff everything into context" vs. MemoryLake
| Default (re-send everything) | With MemoryLake | |
|---|---|---|
| Tokens per file access | Entire file, every time | Only the relevant ~5% |
| Cost as usage grows | Climbs with every call | Flattens — read once, reuse cheaply |
| Re-uploading files | Manual, every session | Stored once, recalled automatically |
| Re-explaining context | Repeated each chat | Persisted across chats & tools |
| Multi-agent workflows | Each agent re-reads everything | Shared memory, retrieved on demand |
| Context window pressure | Fills fast, truncates | Stays lean |
| Usage limits | Hit early and often | Pushed back significantly |
FAQ
Are these "tokens" crypto tokens?
No. Here "tokens" means LLM tokens — the units of text models read and write, and what you're billed for. MemoryLake reduces how many you spend.
How does MemoryLake actually reduce token usage?
It processes each file once, then retrieves only the relevant portion per request — instead of loading the whole document into the context window every time. Less context in = fewer tokens billed.
Will it help with Claude Code / Cursor / Codex token and usage limits?
Yes. These tools re-read your files and context every session. Recalling only what's needed lowers token usage and pushes back the point where you hit usage or credit limits.
Does it work for AI agents and multi-agent workflows?
Yes — that's where it pays off most. Long-running and multi-agent loops re-send context constantly; a shared memory layer cuts agent and multi-agent token costs.
Do I need to change my code or model?
No. MemoryLake connects over MCP and works with 30+ models (Claude, GPT, Gemini, DeepSeek, Qwen, and more). Keep your existing setup.
How much can I really save?
It depends on file size and access frequency. In the calculator's example (a 100-page doc read ~375×/month), monthly LLM cost dropped from $30.00 to $1.50 (95%). Run the calculator with your own numbers.
Is there a free plan?
Yes — 300,000 tokens/month on the Free tier. Pro is $19/mo (6.2M tokens); Premium is $199/mo (66M tokens).
Spend tokens once — not every time.
Give your AI a memory layer and stop paying to re-send the same context.