A fast pass over the managed AI services: one line each, and the pairs that look interchangeable but are not.
Services at a glance
| Service | What it does | Reach for it when |
|---|---|---|
| Amazon Polly | Text to speech | Content needs to be spoken aloud |
| Amazon Transcribe | Speech to text | Audio needs to become a transcript |
| Amazon Translate | Language translation | Text needs to move between languages |
| Amazon Comprehend | Text analysis: sentiment, entities, PII detection | You need to understand what a body of text contains, not generate new text |
| Amazon Lex | Conversational interfaces | You are building a chatbot or voice bot with defined intents |
| Amazon Rekognition | Image and video analysis | You need to detect objects, faces, text, or moderate content in visual media |
| Amazon Textract | Document structure extraction | You need forms, tables, and key-value pairs out of a scanned or digital document |
| Amazon Kendra | Enterprise search; maintenance mode, closed to new customers since July 2026 | You need a retrieval layer over enterprise content, not a ready assistant |
| Amazon Personalize | Recommendations | You need to rank or suggest items for a user based on behaviour |
| Amazon Quick | A managed assistant over company data | You need connectors, permissions, and citations with no code written |
| Amazon Q Developer | A coding and AWS assistant in the IDE, CLI, and console | You need help writing, reviewing, or explaining code, or answering questions about your AWS account |
Decision rules
- If the job is turning text into audio, that is Polly; if it is turning audio into text, that is Transcribe. The direction of the arrow decides which one.
- If the task is understanding existing text (sentiment, entities, PII), reach for Comprehend rather than asking a foundation model to do analysis a purpose-built service already does more cheaply.
- If you are building a defined-intent chatbot or voice interface, use Lex; an open-ended assistant over your own documents is Amazon Quick, not a Lex bot.
- If the input is an image or video and the question is “what is in this”, use Rekognition; if the input is a document and the question is “what is its structure”, use Textract.
- If you need a retrieval layer with connectors and access control feeding your own application, that is Kendra (maintenance mode and closed to new customers since July 2026, so existing indexes only); if you need a ready assistant with a UI, that is Amazon Quick.
- If the ask is “recommend items to this user”, that is Personalize, not a general-purpose model prompted with behavioural data.
- If a request is about writing code or working with your AWS account, that is Q Developer; if it is about answering questions from company documents, that is Amazon Quick. The two get named in almost the same breath and do different jobs.
- If a purpose-built AI service already solves the problem, prefer it over building the same capability on a foundation model; it is usually cheaper, faster, and more consistent for a narrow task.
Traps
- Assuming Comprehend generates text. It analyses text that already exists; it does not write anything new.
- Confusing Rekognition and Textract because both process images. Rekognition answers “what is depicted”; Textract answers “what is the structure of this document” (fields, tables, key-value pairs).
- Treating Kendra as a chatbot. It is the retrieval layer other things are built on top of, not a conversational surface in itself.
- Picking Kendra for a new build. It has been in maintenance mode and closed to new customers since July 2026; existing indexes keep running, but a new retrieval build goes elsewhere.
- Swapping Amazon Quick and Q Developer in a sentence about “the AWS assistant”. One answers from company data with citations; the other sits in the IDE, CLI, and console for code and AWS work. They do not overlap.
- Reaching for a foundation model to do sentiment analysis or entity extraction when Comprehend already does it as a managed, purpose-built call.
- Picking Personalize for a one-off recommendation with no behavioural history to train on; it needs interaction data to be worth using over a simpler rule.
- Forgetting that Lex handles defined intents and slots; an open-ended, document-grounded conversation is a different architecture, not a Lex bot with more intents bolted on.
Say it in one line
- Polly turns text into speech; Transcribe turns speech into text. Pick by which way the conversion runs.
- Comprehend analyses text that already exists: sentiment, entities, PII. It does not generate anything.
- Lex builds defined-intent chatbots and voice bots from intents and slots; an open-ended assistant is a different build.
- Rekognition reads images and video; Textract reads document structure, forms, and tables.
- Kendra is the enterprise-search retrieval layer, now maintenance-only and closed to new customers; Amazon Quick is the ready assistant with connectors, permissions, and citations.
- Personalize ranks and recommends from behavioural data.
- Amazon Quick answers questions from company data with citations and no code; Q Developer helps with code and your AWS account. Similar-sounding names, different jobs.
- Prefer a purpose-built AI service over a foundation model when one already exists for the task.