Translation memory - what is it and what can it do?

A translation memory (TM) is a database of previously translated segments, stored as pairs of source text and target text (translation units), that a translation tool searches while you translate so that identical or similar sentences can be reused instead of being translated again. Each stored pair is a finished, usually reviewed translation. For every new text, the system compares the segments with these units and immediately suggests identical or similar passages, which is how translators reuse proven wording, keep terminology consistent and shorten their processing time.

In the following article, you can find out why the use of translation memory has many advantages, what exactly it is, how it differs from a glossary, what it does not solve, how it works together with AI translation and coding agents in 2026, and how translation memory management works in Locize.

The importance of TM for localization

Translation memory is at the heart of modern localization processes. Why? Because the database consistently exploits linguistic repetitions. Instead of translating sections from scratch again and again, TM reactivates existing solutions, an approach that increases quality, reduces costs, and helps meet deadlines. Software products repeat themselves more than most content: the same button labels, error messages and onboarding sentences recur across screens, releases and sister products, which is exactly where a memory pays off.

How does translation memory work?

When a document is opened, the translation software with translation memory breaks down the text into small units, usually individual sentences; in software localization the unit is typically a single UI string. Each new unit is automatically compared with the existing translation memory.

• Matched sentences: If a sentence matches a saved variant completely, the matching translation appears immediately.
• Similar sentences: If the sentence differs only slightly (for example by a different date or an additional word), translation memory points this out by indicating the similarity value, for example 87%. Deviations are highlighted in color so that only these passages need to be adapted.

After confirmation, the new record pair is written directly to the memory. As a result, the database grows with each project and delivers even more hits in future, regardless of whether the source text comes from word processing files, websites, layout programs or application code.

How much faster translators work with a memory depends on how repetitive the content is. On UI strings, release notes and documentation with many recurring sentences the reuse rate is high; on marketing copy written fresh every time it is low. Measure the match rate on your own content before promising a number.

Translation memory and glossary: not the same thing

A translation memory and a glossary are often mentioned in one breath, and a good editor shows both next to the segment, but they answer different questions. The memory stores segments, whole sentences or strings, collected automatically whenever a translation is saved; it answers "have we translated this before, and how". A glossary stores terms, single words or short phrases with their approved translation per language, curated by people who decide how the product speaks; it answers "which word do we use for this concept". A memory full of reviewed sentences can still disagree on a term if the glossary is missing, and a perfect glossary does not save anyone from retranslating the same sentence. Consistent products use both, and a style guide on top for tone and formality.

The advantages of translation memory

Anyone who frequently translates similar content into several languages will benefit in particular from translation memory. The database turns previous translations into an immediately usable resource and thus provides several levers for efficiency and quality.

• Noticeably faster work: identical matches appear automatically, allowing translators to concentrate on genuinely new passages.
• Significantly lower costs: reused segments do not need to be translated or checked. This in turn reduces the cost of fees and proofreading.
• Seamless consistency: if every recurring sentence comes from the same source, terminology, style and tone remain consistent across all projects, even with multiple language service providers.
• Sustainable quality improvement: a curated TM only saves checked formulations, which reduces the error rate. Brand specifications can also be adhered to automatically.
• Centrally available knowledge base: everyone involved has access to the same database, regardless of whether it is marketing, legal or technology. This prevents data silos and duplication of work.
• Shorter time-to-market: less translation and post-editing time speeds up releases, product launches and campaign launches.
• Consistent customer experience across products: when several products or tenants share a memory, the same sentence reads the same everywhere, which is what international customers notice.
• Focus on creativity: because routine passages are processed automatically, there is more capacity for stylistic subtleties, cultural adaptations and complex content.

What a translation memory does not solve

• Terminology. A memory reuses sentences; it does not know that "subscription" must always be "Abo". That is the glossary's job, and a memory built before the glossary existed will happily suggest the old wording.
• Context. A 100% match for "Open" as a button is a wrong suggestion for "Open" as a status. Segment-level reuse needs context, screenshots or key-level information to be safe in UI work.
• Quality of what went in. A memory is only as good as the translations it was fed. Unreviewed machine output that enters the memory comes back as a confident 100% match next time; only confirmed translations should feed it.
• New content. Marketing copy, product names and anything written fresh has no match by definition. The memory speeds up the repetitive share of your content and leaves the creative share alone.

Translation memory in 2026: AI translation and coding agents

AI translation changed the economics of the first draft, and that made the memory more important rather than less. A model request starts without memory: it does not know how the same sentence was translated and reviewed last quarter. The translation memory is the reuse layer that carries those decisions forward. In practice the pieces stack: exact matches are reused instead of re-generated, fuzzy matches anchor an AI draft to wording that was already approved, the glossary and style guide constrain terminology and tone, quality estimation scores what the model produced, and review confirms what enters the memory next.

The same lookup now serves AI coding agents. An agent that drafts the German strings inside a code change can query the memory for similar source strings before inventing a translation, which is how the twelfth release stays consistent with the first. What breaks when agents skip that step, and the pattern that keeps them in the loop, is covered in when AI translations break.

Translation memory: turbo for more speed, consistency and scope

A professional translation memory turns every translated line into capital. It speeds up projects, reduces costs, keeps wording stable and shifts the focus away from routine copy and paste and towards stylistic and cultural fine-tuning.

Extra tip: establish clear rules for administration, organization and maintenance from the outset. For example, only include approved segments in TM, regularly clean up duplicates, and assign uniform metadata (topic, customer, version). This keeps the memory lean, reliable and valuable in the long term.

How the intelligent translation memory from Locize works

The Translation Memory (TM) from Locize is an intelligent translation support system that runs directly in the browser. As a result, it offers particularly high performance and fast results, as no requests need to be sent to the server for each lookup; the memory is loaded on startup and cached for 24 hours in the browser's IndexedDB.

In contrast to traditional translation memories, the Locize solution allows multiple projects to be used as sources at the same time. This means that existing translations from different projects can be reused, which saves time and increases consistency across a product family.

A special feature is that the search is not limited to exact language pairs. Similar language variants, such as British and American English (en-GB/en-US), are also taken into account. Each hit in the TM is linked to the original project, which facilitates traceability and enables uniform translation across multiple projects.

Configuration is very easy in the project settings under the "EDITOR, TM/MT/AI, ORDERING" tab in the "Translation Memories" section of the "Cat settings" card. There, users can specify which projects are to serve as the source for the translation memory. This service is activated by default in new projects, with the project itself selected as a source. When working with a segment, a corresponding tab appears on the right-hand side of the editor where you can view and use the TM suggestions; a match is accepted with one click.

Translation memory matches next to a segment in the Locize editor, alongside machine translation suggestions

Two more pieces connect the memory to the rest of the workflow. The consistency check issue 220 flags a translation that disagrees with the memory, so drift is visible instead of silent. And the same memory is available to AI coding agents through the search_translation_memory tool of the Locize MCP server, which returns exact and fuzzy matches for a source string so an agent reuses prior translations instead of inventing new ones.

Learn more about our features and how to use it in our documentation.

FAQ: Translation memory

What is a translation memory? A database of previously translated segments, stored as source and target pairs called translation units, that a translation tool searches while you translate so identical or similar sentences are reused instead of translated again. It grows with every confirmed translation.

How does a translation memory work? The text is split into segments (sentences or UI strings) and each is compared with the stored units. An exact match returns the stored translation; a fuzzy match returns a similar segment with a similarity score, for example 87%, and highlights the differences. Confirmed segments are written back to the memory.

What is the difference between a translation memory and a glossary? The memory stores whole segments, collected automatically. The glossary stores individual terms with their approved translation per language, curated by people. The memory answers "have we translated this sentence before"; the glossary answers "which word do we use for this concept". Consistent products use both.

What is a fuzzy match in translation memory? A stored segment that is similar but not identical to the one being translated, reported with a similarity percentage. Fuzzy matches are useful above roughly 70 to 75% similarity; below that, adapting the match usually costs more than translating from scratch.

Does translation memory still matter with AI translation? More, not less. A model request starts without memory, so the TM carries reviewed translations into the next batch: exact matches are reused, fuzzy matches anchor the AI draft to approved wording, and coding agents can query the same memory before drafting. The memory is the reuse layer; the model is the drafting layer.

How do I keep a translation memory clean? Let only confirmed or reviewed translations enter it, remove or archive duplicates and obsolete segments regularly, and run a consistency check that flags segments disagreeing with the memory. In a translation management system this happens where the translations live.