LodgingBase Edge Cache sits between API consumers and your system. It absorbs up to 70% of search traffic before it ever costs you and your suppliers — while an AI freshness engine keeps every cached response accurate.
0%
Upstream traffic reduction
up to
<1,000 ms
Cached response time
average
0+
AI signals per decision
freshness model
0%
Booking path cached
always live
A 10,000:1 Look-to-Book ratio means 9,999 out of every 10,000 API calls flowing through your distribution stack return no booking. Each one taxes your supplier connections — consuming quota, burning rate limits, and increasing the risk of throttling that degrades real booking traffic.
The pressure compounds upstream. When your suppliers forward unfiltered search volume to their own inventory sources — GDS systems, aggregators, property management platforms — the cost multiplies. Suppliers notice. Relationships suffer. Quota allocations shrink.
Edge Cache intercepts that pressure before it propagates. Channel partners keep getting fast, accurate responses. Your suppliers see a fraction of the original call volume.
Real-world Look-to-Book ratio
searches per booking
0.01% of traffic results in a booking
10,000:1
Searches per booking on high-volume channels
≤70%
Upstream calls Edge Cache eliminates
AI
Powers freshness decisions automatically
0%
Booking traffic ever cached — always live
The challenge with caching travel inventory is that rates and availability change continuously. A static TTL strategy either caches too aggressively — serving wrong prices — or too conservatively — defeating the purpose entirely.
LodgingBase Edge Cache uses a machine learning freshness model trained on booking velocity, price volatility, supplier update patterns, and seasonal demand signals. For every property-date-rate combination, it dynamically decides how long a cached response remains valid.
High-demand properties near check-in dates are revalidated frequently. Stable off-peak inventory carries longer TTLs. The model adapts continuously — reducing upstream calls without sacrificing accuracy.
AI Freshness Engine
Adaptive TTL per property, date & market
Output — adaptive TTL decision
4 min
High-demand properties
22 min
Stable inventory
60 min
Off-peak dates
At a 50:1 Look-to-Book ratio with one million daily searches, 980,000 of those requests are pure search traffic. Edge Cache serves the cacheable fraction directly — reducing what flows upstream to a controlled, predictable trickle.
The actual deflection rate depends on your traffic mix, supplier update frequency, and cache policy. For most deployments, 60–70% of upstream calls are eliminated within the first week.
Upstream supplier calls — before vs. after
* Illustrative values. Actual deflection rate varies by traffic profile and cache policy configuration.
Edge Cache integrates as a transparent layer in front of your distribution chain. No supplier reconnections, no API changes, no downtime.
We integrate you as a supplier. You generate API keys for your consumers and point them to Edge Cache. No developer work needed on your side. We act as a tech layer, do not trade on your behalf.
The freshness engine analyses booking velocity, rate change frequency, and supplier update cadence to set adaptive TTLs for every property-date pair.
Cacheable availability and rate queries are answered at the edge in under 5 ms. Only cache misses and booking requests reach your suppliers.
Every reservation request bypasses the cache entirely and is proxied directly to your supplier. No cached data ever influences a booking outcome.
Inventory flows from your supplier chain into the cache. Channel partners get fast, accurate responses. Suppliers see only a fraction of the traffic.
Consumers
OTAs · Resellers
Meta-search
search flood
≤30% passes
Your System
Bookings &
cache misses only
≤70%
Traffic absorbed
<100 ms
Cached response
0%
Bookings cached
The operational differences from day one of Edge Cache deployment.
Every capability is designed for the reliability, freshness, and supplier-safety demands of bedbanks, wholesalers, and DMCs.
Adaptive TTLs driven by 12+ signals — booking velocity, price volatility, supplier cadence, and seasonal patterns. No manual TTL tuning required.
Controlled upstream call rates protect your supplier API quotas from search floods, reducing throttling risk and preserving allocation for real bookings.
Globally distributed cache nodes serve available inventory in under 100 ms — faster than any live supplier call, regardless of geography.
The booking path is architecturally excluded from caching. Every reservation, modification, and cancellation goes directly to your supplier, live.
Cache hit rates, upstream deflection, freshness violations, and supplier call volume — updated live and exportable to your BI stack.
You pay for deflected requests only. The more search pressure your channel partners create, the more value Edge Cache returns — structurally self-funding.
Share your current traffic volume and Look-to-Book ratio. We will model the upstream call reduction and walk you through the deployment — no commitment required.
Most deployments are live within a business day.
Connecting the travel supply chain with innovative distribution technology.