KantanMT Slashes Pricing Per Word by 60%
KantanMT restructures pricing plan and introduces radical reduction of cost per word for clients
Dublin, Ireland: KantanMT is pleased to announce a major restructuring of their pricing plans, which will see a reduction of translation cost per word (CPW) of up to 60% in comparison to existing plans. KantanMT is a cloud-based Custom Machine Translation solution provider, which also provides on-premise deployment options. The pricing of infrastructure required by KantanMT to host a reliable, fast, 24x7 service has reduced recently, triggering the company’s decision to pass on the savings made from this to their clients.
All the pricing plans from KantanMT come with a generous number of translation word count and attractive overage rates, which will ensure high return of investment (ROI) for clients.
“Concurrent with our growth at KantanMT, the cost of hosting our custom MT solution on the cloud as well as on-premise has come down substantially,” says Tony O’Dowd, CEO and Chief Architect at KantanMT. “Instead of retaining these savings, we have decided to make KantanMT more accessible to our clients by reducing the translation CPW. Recently, we have introduced some very exciting developments at KantanMT – including launching our automated language quality evaluation platform (KantanLQR™) and introducing our Neural MT solution; and we want more users in the community to enjoy these features. I hope our new pricing plans prompt more users to come on board.”
To know more about the new pricing restructuring, mail [email protected].
KantanMT is a Custom Machine Translation platform that provides the most extensive range of customisation and accuracy management features on the market. Used by some of the worlds’ leading brands, KantanMT delivers translations that are superfast, accurate and brand-consistent. KantanMT ensures data-confidentiality with cloud or on-premise deployments, offers self-managed or fully-serviced implementations, and supports any translation volume in over 760 language pairs.