People new to translation technology often mix up two very different things. One is the CAT tool, the workspace a translator uses. The other is machine translation, the engine that can produce a draft automatically. They solve different problems, and the best results usually come from using them together.
The short answer
The question what is a cat tool has a short answer. It is software that helps a human translator work faster and more consistently, by splitting text into segments, storing past translations and checking quality.
Machine translation, by contrast, is an engine that generates a translation by itself. One supports the person, and the other produces text.
What a CAT tool does
A CAT tool shows the source and target text side by side, offers matches from a translation memory and highlights glossary terms. It also protects formatting and runs quality checks.
The human stays in charge of every sentence. The tool simply removes repetitive work and reduces errors.
What machine translation does
A machine translation engine takes a sentence and returns a translation in seconds. Modern engines use neural networks trained on huge amounts of text, and results are often fluent.
Fluent does not always mean correct. Engines can invent details, miss context or choose the wrong term, so human review remains important.
How the two work together
Most CAT tools can connect to machine translation engines. When no memory match exists, the tool offers the engine suggestion, and the translator edits it.
This combination, called post editing, can speed up work a lot on suitable content while keeping a human check on quality.
Where memory beats the engine
Translation memory returns text that a human already approved. For repeated and similar sentences, this is more reliable than a fresh machine suggestion.
That is why tools usually rank memory matches above engine output, especially at high match percentages.
Where the engine helps most
For new text with no matches, an engine saves typing and offers a starting point. It works best on clear, factual writing, such as manuals and support articles.
Creative copy and legal text need more care, because small errors can change meaning or tone.
Legal text is the clearest case for keeping the engine on a short leash. A contract clause or an official certificate leaves no room for the plausible-but-wrong renderings engines produce, which is why specialists in legal translation services typically translate such documents inside a CAT environment with approved memories and termbases, then verify every name, date and obligation by hand. The engine may still draft low-risk repetitions, but accountability stays with the human. That division of labour is the practical meaning of the care this section calls for.
Choosing an engine
Engines differ by language pair, quality and price. Our guide on how to choose a machine translation tool explains how to test them fairly with your own content.
Always involve a native speaker in the comparison. Numbers alone rarely show how natural a text sounds.
The role of glossaries
Glossaries make both memory and engines more consistent. In a CAT tool, they highlight approved terms. With many engines, they can also force terms in the output.
Keep one glossary and use it everywhere. That avoids conflicting terms across tools and projects.
Post editing levels
Light post editing fixes meaning errors and obvious mistakes. Full post editing aims for human quality in style and terminology.
Choose the level by content type and audience, and tell editors clearly what is expected before they start.
Quality checks
CAT tools run automatic checks for numbers, tags, missing segments and term use. These apply to human and machine text alike.
Run them before every delivery. They catch small errors that readers notice instantly.
Working with files
CAT tools handle many formats and preserve layout. Pure machine translation websites often ignore formatting, which causes extra work later.
For documents with tables and tags, a CAT tool plus an engine is usually the safer route.
Privacy considerations
Sending text to an engine means sharing it with a service. Check how data is stored and used, especially for confidential material.
Business plans and private deployments offer stronger promises than free public tools.
Learning from open projects
Open source communities share lessons about translating software at scale. The Mozilla localization resources are a good example of practical advice on terms, placeholders and review.
Reading such material shows how experienced teams combine technology and human judgment.
Costs and savings
CAT tools save money through reuse of earlier translations. Machine translation saves time on new text. Together they reduce cost per word, though editing time must be counted.
Track total effort, not just engine price, to see the real savings.
Training your team
New users learn faster with a short guide that shows the full path from file import to delivery. Include screenshots, shortcuts and a checklist.
Hold a review after the first project and update the guide with what people learned.
Common misunderstandings
Many believe a CAT tool translates automatically, or that an engine makes translators unnecessary. Neither is true.
Translators bring judgment, culture and accountability. Technology gives them speed and consistency.
Who needs what
A freelancer may start with a CAT tool and add engine suggestions later. A company may add engines first for support content, then add review through a CAT workflow.
Match the setup to your content, your volume and your quality needs.
A simple way to start
Pick one project, load it into a CAT tool, connect an engine and compare the draft with your own translation of a few paragraphs. Note where each helps and where it fails.
Those notes will guide how you combine technology in future projects.