AWS content moderation alternatives: Comprehend, Rekognition and what else to consider
Bottom line: if your stack is already on AWS and your needs are narrow (toxic text, or explicit and violent images), Comprehend and Rekognition are reasonable to keep. Look at alternatives when you need labels they do not return (scams, spam, self-harm), a way to flag messages that need conversation context, or text, images and video behind one API. Supervisor is built for that, and Hive is the stronger option if visual moderation is the main job.
AWS does not sell a single "content moderation" product. You assemble it from two services: Amazon Comprehend for text and Amazon Rekognition for images and video. That split is the main reason people go looking for alternatives, so this post treats them as one setup.
What the AWS setup covers
Text, through Comprehend toxicity detection. The DetectToxicContent API takes a list of up to 10 text strings, each up to 1 KB, and returns a result per string. Each result has an overall toxicity score and a score per label. The label names are GRAPHIC, HARASSMENT_OR_ABUSE, HATE_SPEECH, INSULT, PROFANITY, SEXUAL and VIOLENCE_OR_THREAT. Scores run from zero to one.
Images and video, through Rekognition. Rekognition content moderation detects inappropriate, unwanted or offensive content in images and stored video, using a three-level label taxonomy with confidence scores. It also supports custom moderation adapters you train on your own annotated images, and human review through Amazon Augmented AI.
Why people look for alternatives
The label set is narrow for community and platform use. Comprehend's seven labels are about toxicity. They do not include scams, spam, promotional content, self-harm or medical content. Rekognition's top-level categories are visual: explicit and non-explicit nudity, swimwear or underwear, violence, visually disturbing content, drugs and tobacco, alcohol, rude gestures, gambling and hate symbols.
Rekognition says what it is not. AWS's own documentation states that Rekognition is not an exhaustive filter of inappropriate content, and that the image and video moderation APIs do not detect whether an image includes illegal content such as CSAM. That is a sensible disclosure, and it matters if "illegal" is a category you need covered.
Two services, two integrations. Text and media go through different APIs with different request shapes, limits and pricing units. Comprehend bills in units of 100 characters; Rekognition bills per image and per minute of video. Our pricing breakdown walks through both.
Output is scores, and the decision is yours. Neither service returns severity bands. You get scores and confidences, and you pick the thresholds. For some teams that control is the point. For others it is a tuning project.
No conversation-context mechanism in the API. DetectToxicContent takes a list of text segments. We did not find a parameter in the API reference for passing conversation history or for flagging that a message cannot be judged alone. If you need that, you build it yourself.
The options at a glance
| Text | Images | Video | Labels | Context flag | Best for | |
|---|---|---|---|---|---|---|
| AWS (Comprehend + Rekognition) | Yes, Comprehend | Yes, Rekognition | Stored video, async | 7 text labels; 3-level visual taxonomy | Not found in API reference | Teams already on AWS |
| Supervisor | Yes | Yes | Yes, up to 60 s / 10 MB | 16 labels | Yes, include_context | Communities, games and platforms needing broad labels |
| Hive | Yes | Yes | Yes, including live streams | Text severity levels; deep visual classes | Not found in docs | Visual moderation at scale |
Alternatives worth testing
Supervisor
Supervisor is what we make, so weigh this accordingly. It is a paid moderation API with a live demo so you can test it before committing.
What it adds compared with the AWS pair:
- 16 labels in one response: profanity, toxicity, harassment, hate, insult, sexual, sexual (unlawful), sexual (explicit), sensitive, violence, self-harm, medical, spam, promotional, scam and illegal.
- Context flagging. With
include_context, the response tells you whether a messageneeds_contextand whichcontext_labelsit might fall under. "Stop it" is the classic example: harmless alone, harassment in the right thread. - Implicit harm.
implicit_labelscovers content that is harmful without saying so outright. - Text, images and short video in one API. Video is limited to 60 seconds and 10 MB, with up to 20 frames analysed.
- Non-English handling. Non-English messages are translated before they are classified, including slang and mixed-language messages.
- Three model tiers (Observer, Sentinel, Arbiter), a 50% discount on batches of two or more, a username check endpoint, SDKs for Python, JavaScript, Go, Rust and Java, and a ready-made Discord bot.
What it does not do: its video limit is 10 MB and 60 seconds, so long stored videos (Rekognition handles files up to 10 GB and 6 hours) are not its territory. Depending on volume and on whether you send images, it can cost more than a pay-per-use AWS call, which we cover plainly in the pricing post.
Best for: teams who want broad labels, context and one integration, and are happy with one subscription instead of metered AWS billing.
Hive
Hive is the strongest alternative if your main problem is images and video. Its visual moderation covers sexual content, violent imagery, drugs, hate imagery and image attributes, supports live streams (RTMP and HLS), and its text moderation returns severity levels from 0 (benign) to 3 (most severe). We cover it in more depth in the Hive review. Pricing details should be confirmed with Hive.
Best for: visual moderation at volume, or teams that want severity levels and an enterprise plan.
Other routes
- OpenAI's moderation endpoint is free and a common first stop for application filtering. We compare it in Supervisor vs OpenAI's moderation endpoint.
- Discord's built-in AutoMod is the free floor if the problem is a Discord server rather than your own product. See Discord AutoMod alternatives.
Should you leave AWS at all?
Often not completely. Keeping Rekognition for stored-video jobs or custom adapters, and adding a second layer for the labels AWS does not return, is a perfectly reasonable setup. Switching makes sense when the integration cost of two services is higher than the cost of one.
Test on your own content rather than a feature table. Paste a message your current setup misses into the live demo, read the API reference, or create an account and get an API key.