OUTCOME · SHARED MEMORY
Cut your AI coding bill without cutting capability
Up to 40% of agent prompts is boilerplate your team has written a hundred times. MemHub replaces that repetition with incremental memory briefings — same output quality, materially smaller invoices.
- peak: repeated-context tokens
- -55%
- per-seat markup — Community tier exists
- $0
- command to start measuring
- 1
* Reference figures — measure your own savings with a pilot sprint.
SOLVED PAIN
The pains MemHub removes from your operation.
Every seat pays the same context tax daily
Deltas amortize: pay once per change, not once per session.
Bigger windows billed as progress
Retrieval beats retention: bring facts, not archives.
Cost visibility ends at the invoice
`mh status` shows sync volume; token deltas show up in your provider dashboards.
Cheaper model = dumber output
Keep the smart model; shrink its input instead.
Real value, for the people building and the people deciding.
For developers watching usage meters
- Prompts shrink without hand-trimming context
- Fewer “lost the plot” restarts that burn tokens twice
- Local engine handles lookups offline for free
For finance and engineering leadership
- Per-team token telemetry aligns spend with output
- Self-hosting caps vendor costs permanently
- Community tier proves ROI before any commitment
01Where the money actually goes
Agent economics are dominated by input tokens. Architecture summaries, conventions and history re-sent each session are pure overhead — the same bytes, billed again and again.
02Amortize context like code
Code is written once and reused everywhere; MemHub gives project knowledge the same economics. Write the decision once, retrieve it infinitely at retrieval prices — a fraction of generation prices.
UNIVERSAL COMPATIBILITY
Plugs into any CLI or development tool you use.
If it speaks MCP or reads a JSON config, MemHub plugs in. One command wires the majors; everything else joins as a generic client.
- Claude Code
- Cursor
- Windsurf
- Cline
- Codex CLI (GPT)
- GitHub Copilot
- Google Antigravity
- Gemini CLI
- Aider
- OpenCode
- CI bots via REST
Questions, answered straight.
Will smaller prompts degrade answers?
Ranked relevance usually improves them: the model sees current decisions, not stale noise. Quality issues come from missing context — which memory also fixes.
How fast does the saving appear?
From the second session: the first pull seeds the cursor, everything after ships deltas. Compare provider usage week over week.
THE NEXT SESSION STARTS HERE — 2 MIN SETUP