FPGAs are quickly becoming available in the cloud as a one more heterogeneous processing element complementing CPUs and GPUs. There are many reports in the literature showing the potential for FPGAs to accelerate a wide variety of algorithms, which combined with their growing availability, would seem to also indicate a widespread use in many applications. Unfortunately, there is not much published research exploring what it takes to integrate an FPGA into an existing application in a cost-effective way and keeping the algorithmic performance advantages. Building on recent results exploring how to employ FPGAs to improve the search engines used in the travel industry, this paper analyses the end-to-end performance of the search engine when using FPGAs, as well as the necessary changes to the software and the cost of such deployments. The results provide important insights on current FPGA deployments and what needs to be done to make FPGAs more widely used. For instance, the large potential performance gains provided by an FPGA are greatly diminished in practice if the application cannot submit request in the most optimal way, something that is not always possible and might require significant changes to the application. Similarly, some existing cloud deployments turn out to use a very imbalanced architecture: a powerful FPGA connected to a not so powerful CPU. The result is that the CPU cannot generate enough load for the FPGA, which potentially eliminates all performance gains and might even result in a more expensive system. In this paper, we report on an extensive study and development effort to incorporate FPGAs into a search engine and analyse the issues encountered and their practical impact. We expect that these results will inform the development and deployment of FPGAs in the future by providing important insights on the end-to-end integration of FPGAs within existing systems.