Open engine
The runtime is open source. Clone it, run it locally, fork it, contribute back.
The engine and schemas are open. Payment covers maintained pack work and the hosted lanes DaedalMap runs for you. Publicly, the choice is simple: use the hosted app, run the engine yourself, or request the local research runtime.
Everything in this list is free.
The runtime is open source. Clone it, run it locally, fork it, contribute back.
The pack schema and loc_id model are public and documented. Bring your own data in the same shape.
Every published pack is visible in the hosted catalog. Use them through hosted surfaces, inspect them publicly, or move into the GitHub/manual path when you want to run the engine yourself.
Open the hosted app. Ask a question on the map. No account, no fee.
Hosted lanes are paid where DaedalMap carries a real operating cost.
Sonnet up to $0.10 per question · Opus up to $1.00 per question
Research mode runs cross-domain queries through hosted LLM compute. You pay what we pay. Costs scale with query depth and corpus size.
Pick the feeds you operate on. Pay for what you watch.
$20 per month
Ops mode runs live feeds and focused operational features on continuous hosting. Monthly fee covers the running infrastructure behind active collectors and watch-state features.
Why some things are paid
DaedalMap is not charging for ownership of public facts. It is charging for maintained pack work and hosted operation. Maintaining packs, running hosted LLM compute, operating live collectors, and keeping agent access clean all cost money. The engine stays open for anyone who wants to use the GitHub/manual path.
A third paid lane covers programmatic access via the Agents / MCP surface. Free discovery endpoints stay free. Paid packs (earthquakes, tsunamis) cost $0.01 for up to 100 rows, then $0.0001 per additional row, settled in Base USDC via the x402 challenge. The 402 response shows the exact amount before any charge.
The packaged local runtime sits beside these hosted lanes as a controlled research path. It is for researchers, academics, and serious teams who need more local control, larger workflows, or their own LLM keys.