The Seattle firm uses a finely-tuned data mining engine to analyze more than 150 billion actual airfare price quotes from the past 18 months to and from 75 major U.S. cities to come up with its prediction. And garnering that extra 25 percent of certainty on fares apparently really matters: More than a million unique would-be fliers have tried the free Farecast.com service since August.

"It's a very complex problem," said Jay Bartot, vice president of technology at Farecast.com. "Our data mining engine is very large and sophisticated. We do a lot of post-processing, deriving new data from our existing data, which is then fed into our predictive engine."

In other words, Farecast.com is constantly generating airfare predictions on its own in addition to those requested by consumers. It then checks its results against the actual price quotes generated by the airlines, allowing Farecast.com to figure out how accurate it really is and further finetune its data mining operation.

Started in 2003, Farecast.com was spun out of research by professors at the University of Washington and the University of Southern California, yielding what is essentially a business intelligence service for consumers. The choice of which database technology to use was key and the company experimented with several open-source databases, including PostGreSQL and BerkeleyDB, before initially settling on MySQL.

Even so, as Farecast.com neared a launch date, Bartot worried. "We knew we would have to scale out in a major way. I had read some stories about companies doing huge rollouts of MySQL clusters, but in at least one case relevant to us, it turned out to be more of an experiment," Bartot said.