Side-by-Side Comparison: 8 Content Moderation APIs

Every app with user-generated content eventually needs a way to catch the bad stuff. Harassment, spam, graphic content, exploitation - the list keeps growing.

If you don't want to build (and maintain) this yourself, that's where a content moderation API comes into play.

This piece covers eight APIs: Stream, Hive, Sightengine, WebPurify, Alice (formerly ActiveFence), Checkstep, Besedo, and Cleanspeak. We look at what each one actually catches, use cases, limitations, and the legal side to help you make the right choice for your app.

Do You Need an API?

Before evaluating APIs, we find it helps to consider your alternatives.

Two come up often when deciding whether to buy a moderation API:

1. Ask a general AI model to do it
2. Or, hire people to review content by hand

Both are real options but not sustainable for many apps.

API vs. Prompting an LLM

With models like GPT, Claude, and Gemini, it's tempting to just prompt one to flag bad content instead of buying a dedicated moderation product.

Here's why an LLM might be the right choice:

• No separate contract or minimum commitment. For a very early-stage product with low content volume, using a model you're likely already paying for elsewhere avoids adding another vendor to evaluate and pay for.
• Speed to test an idea. A quick prompt lets you validate whether moderation is even needed, or get a rough sense of what kind of content your users post, without setting up a vendor relationship or writing integration code.
• Flexibility for edge cases. An LLM can be asked to explain its reasoning in plain language, which can help a small team manually understand a handful of tricky cases early on.

At real scale, though, an LLM falls short for reasons specific to moderation rather than AI limitations in general.

Here's why an LLM might be the wrong choice:

• Consistency. A general-purpose model can give different answers to the same piece of content on different runs. A moderation decision that flips back and forth is hard to defend to a user who got banned, or to a regulator asking why two similar posts were treated differently.
• CSAM requires a different approach entirely. Having a general AI model directly analyze suspected child sexual abuse material is a serious legal and safety problem on its own. Dedicated moderation vendors instead match content against hash databases of known illegal material (a method called hash-matching, more on this later), so no model or human ever needs to view the actual content to flag it.
• Prompt injection. User-generated content can contain text specifically designed to manipulate an AI model's judgment - things like instructions embedded in a post telling the model to ignore its rules.
• Audit trails. Compliance requirements (more in the Legal section below) often require showing exactly what was flagged, why, and what happened next. A one-off prompt to an LLM doesn't produce that record in a usable form.
• Cost and speed at real volume. Sending every piece of content to a frontier language model gets expensive and slow once you're processing millions of posts a day. Purpose-built classifiers are cheaper and faster per item, since that's the one thing they're built to do.

For a weekend prototype or a handful of daily posts, prompting an LLM might be enough. Past that, a dedicated moderation API is built to handle volume, consistency, and legal requirements.

API vs. Human Review

Another alternative is skipping automation and just hiring people to review content directly.

Human review could be the right choice when:

• You're pre-launch or at very low volume. A handful of posts a day is easy for one or two people to review directly, and there's no real cost benefit yet to automating something that small.
• Your content is highly context-dependent. Things like nuanced community disputes, satire, or culturally specific speech are still genuinely hard for automated systems to judge correctly, and a person with context can make a better call.
• You need a final decision-maker for appeals. Even the best automated system needs someone to review contested decisions, so some level of human review usually exists no matter what else you use.

However, human review is likely the wrong choice if:

• You need volume. Human reviewers can't keep up with the amount of content most platforms generate. Without some form of automated first pass, review queues back up fast, and harmful content stays up longer while it waits for a person to see it.
• Cost and employee well-being matter. Staffing a review team at scale is expensive, and repeated exposure to graphic or disturbing content takes a real toll on the people doing that work.
• You need consistency across reviewers. Different people can judge the same borderline content differently depending on mood, fatigue, or individual judgment, which makes it hard to apply your own rules the same way every time.

In practice, many apps end up picking a combination of the above. For example, you could use automated moderation to handle the clear, high-volume cases, and route the harder, ambiguous ones to human reviewers.

Either way, an API is likely still on your roadmap.

What Differs Between Moderation APIs

Not all APIs are created equally.

When evaluating options, consider how the following apply to your use case:

What to check: Ask how much delay pre-publish checking adds at your expected volume, and how long harmful content typically stays up under an after-the-fact approach.

What to check: Ask whether you can adjust this threshold yourself, and how granular that control actually is. Some APIs give you a single strictness dial. Others let you tune by content category.

What to check: Ask how review cases are routed and how quickly a human decision typically comes back, since this affects both cost and how fast genuinely unclear cases get resolved.

What to check: Ask for real performance numbers on the specific content type you actually need.

What to check: Ask for an example of a custom policy another customer has built instead of just the default categories.

What to check: Ask directly about performance in the specific languages your users actually speak, rather than assuming coverage is even everywhere a vendor claims support.

What to check: Ask what's included out of the box versus what you'd need to log yourself, since this ties directly to the legal requirements covered next.

How Legal Requirements Impact Your Choice

On the legal side, there are also a few considerations.

None of this replaces legal advice specific to your platform. It's a starting point for the questions you should ask a vendor and/or your own counsel before you commit to an API.

CSAM Reporting

U.S. law requires online platforms to report suspected child sexual abuse material, online enticement, and child sex trafficking to NCMEC's CyberTipline the moment they become aware of it.

The REPORT Act, signed into law in 2024, expanded this requirement to cover enticement and trafficking, not just CSAM itself, and extended the mandatory preservation period for reported material from 90 days to a full year. Platforms can also voluntarily preserve material beyond that year specifically to help combat child sexual exploitation and abuse.

-> How this impacts your choice: Ask whether a vendor's moderation system connects directly to CSAM hash-matching databases and can generate a compliant CyberTipline report on its own, or whether that's something your team would need to build separately.

The EU's Digital Services Act

The DSA requires platforms operating in the EU to report on content moderation decisions in a standardized format, offer users a way to appeal moderation decisions, and, in some cases, give researchers structured access to moderation data.

Non-compliance can carry fines up to 6% of global turnover for the largest platforms.

-> How this impacts your choice: Ask whether a vendor's reporting output is already formatted for DSA transparency requirements, or whether you'd need to reformat it yourself.

Section 230

In the US, Section 230 shields platforms from liability for user-generated content, and specifically protects platforms that choose to moderate content in good faith from losing that protection.

As of early 2026, several reform proposals are circulating in Congress, but none have passed. This is worth monitoring rather than assuming it's settled permanently, since the rules platforms operate under today may not be the same in a year or two.

-> How this impacts your choice: Ask whether a vendor can show good-faith, consistently applied enforcement in its logs, since that record is what Section 230's moderation protection turns on if a decision is ever challenged.

Quick Reference Table

Before the full breakdown, here's how all eight content moderation APIs compare side by side.

8 Moderation APIs - Compared

Stream AI Moderation

Stream's AI Moderation works as a standalone product or bundled with Stream's chat, feeds, and video SDKs. The tool offers SSO/SAML and two-factor authentication, audit logs, a dedicated AWS region stack for data residency requirements, and 24/7 emergency support for enterprise clients. This is great for apps operating under strict compliance or data-handling requirements.

What it catches. Text, image, video, and audio content, in real time. Stream runs both traditional AI classifiers (NLP-based detection for things like profanity, hate speech, and spam) and LLM-based moderation, which evaluates flagged content for context and tone rather than relying on keyword matching alone. Detection covers 50+ languages.

How review actually works. Trust and safety teams get a no-code policy builder for setting if/then enforcement logic (for example: flag this category at this confidence level, then take this action) without needing engineering support for every policy change. Stream reports that LLM-based review tools can speed up moderator throughput by up to 25% through features like contextual views and queues designed to reduce reviewer fatigue. Teams can build a fully custom review dashboard on Stream's APIs, or use Stream's own dashboard as-is.

Where it fits:

• Teams that want automated policy logic they can configure themselves, without engineering involvement for every change.
• Regulated industries like healthcare, gambling, and financial services, given GDPR, CCPA, HIPAA, SOC 2, and ISO 27001 certifications, plus transparency and human-oversight features aligned with EU AI Act requirements for automated decision systems.
• Teams already using Stream for chat, feeds, or video that want moderation under the same data model and vendor relationship.
• Enterprise teams needing 24/7 support, SSO/SAML, audit logs, a dedicated AWS region stack, or a 99.999% uptime SLA.

Where it falls short:

• No in-house human moderation workforce. Trust and safety teams get the tooling to build and run a review workflow instead of a staffed team to handle cases.
• Not built for bulk or offline moderation of existing content libraries. Instead, the API is designed for real-time, in-app activity (chat, feeds, live video).

Hive

Hive provides moderation through a REST API and web dashboard, without a pre-built chat or community UI to plug into, so most of the integration work happens on the API side.

What it catches. Text, image, video, and audio content. CSAM detection runs on two nonprofit partnerships delivered through Hive's API rather than being built in-house: Thorn's Safer Match, which Thorn reports matches against 57 million+ known CSAM hashes and separately scores previously unseen images and video for novel CSAM, and IWF's keyword, URL, and hash lists, integrated into text moderation. A newer CSE Text Classifier, also powered by Thorn, flags text indicating child exploitation. Hive also runs a multimodal LLM for image-and-text moderation, alongside standard classifiers for nudity, violence, hate symbols, drugs, and spam.

How review actually works. Hive's Moderation Dashboard lets teams configure moderation rules, confidence thresholds, and content categories, and manage human review queues and escalations directly in the dashboard. iOS and Android apps let teams manage moderation queues away from a desk.

Hive doesn't provide its own in-house review workforce. The dashboard supports your team's review process rather than replacing it with Hive's own reviewers.

Where it fits:

• Teams that already have a human review process in place, or plan to build one, and want a strong detection engine to feed it rather than an outsourced review team.
• Platforms with meaningful risk in AI-generated content or CSAM specifically, given the dedicated IWF hash-matching integration and multimodal LLM.

Where it falls short:

• No in-house or outsourced human moderation team. Staffing the actual review process is on your team.
• No pre-built chat or community UI, since Hive is API-and-dashboard only, meaning more integration work than a bundled product.
• No no-code, if/then policy builder for custom enforcement logic. The dashboard configures thresholds and categories, but not automated decision rules.

Sightengine

Sightengine is built around a pure API model. There's no dashboard-based review workforce or built-in human escalation path, since the company explicitly designs the product around images and content never being seen by a human reviewer at all, on either Sightengine's side or yours, unless you build that step separately.

What it catches. Text, image, video, and audio, plus OCR and QR code moderation, and AI-generated content detection covering major generators including Midjourney, DALL-E 3, Hunyuan Image 3.0, Stable Diffusion, and Flux, updated continuously as new generators are released. Detection is pixel-based rather than relying on metadata, EXIF tags, or C2PA provenance data, which Sightengine notes are easily stripped when images are re-uploaded to social platforms or messaging apps.

How review actually works. There isn't a human review workforce or escalation dashboard built into the product. Content is scored and returned via API, and Sightengine's own positioning is that no human, at Sightengine or elsewhere, needs to view flagged content for it to be moderated.

Where it fits:

• Privacy-conscious teams, particularly those operating primarily in the EU, given Sightengine's Paris-based hosting, regional data restrictions on Enterprise plans, and immediate data deletion after processing.
• Platforms fighting synthetic media specifically, given the deepfake and AI-generated content detection built into the core product.
• Teams that want GDPR compliance handled directly rather than through a third-party datacenter partner, plus ISO 27001, SOC 2, and PCI DSS through Sightengine's datacenter partners.

Where it falls short:

• No human review workforce or built-in escalation path. Teams needing human-in-the-loop review for compliance or ambiguous cases build that workflow themselves.
• No spending controls on paid plans. Unlike the Free tier's hard stop at 2,000 operations a month, Starter and Pro plans have no budget cap or automatic halt, so a traffic spike or misconfigured integration generates real overage costs before anyone notices, since downgrades only take effect at month-end.
• No dedicated CSAM hash-matching integration.

WebPurify

WebPurify combines its Automated Intelligent Moderation (AIM) system with an in-house team of live human moderators, positioned specifically around cases where AI alone gets it wrong. Images approved by AI systems, including its own, get caught and rejected by human moderators afterward.

What it catches. Text, image, video, and audio content. AIM covers image and video, flagging content likely to violate policy so it can be routed to a human moderator for the final call. Audio is transcribed and moderated in 15 languages, combining human review with AI-driven text analysis of the transcript.

A free, opt-in PhotoDNA integration (Microsoft's CSAM hash-matching technology) checks images against NCMEC's database of known child sexual abuse material. A match is flagged with a moderation status of "3," triggers an account alert, and for U.S.-based customers, surfaces a direct link to the NCMEC CyberTip reporting form.

How review actually works. Human review is a built-in, staffed offering. Live moderators are available 24/7 to make final calls on flagged content. WebPurify also offers trust and safety consulting, including help defining moderation strategy, workflow design, and training your own in-house moderators.

Where it fits:

• Platforms with heavy user-generated photo or video content, like dating apps, marketplaces, and online communities.
• Teams that want a staffed human review layer without hiring and managing one themselves.
• U.S.-based platforms specifically needing a straightforward CSAM reporting workflow, given the direct NCMEC CyberTip link built into the PhotoDNA integration.

Where it falls short:

• Audio moderation only supports 15 languages, narrower than Stream's 50+ or Checkstep's 100+.
• PhotoDNA hash-matching is opt-in and images only, so it has to be switched on, and it does not extend to text or video the way Hive's Thorn and IWF integrations do.
• No no-code policy builder for automated enforcement logic. WebPurify's model leans more on its human moderation team for judgment calls, which works well for nuanced content but gives trust and safety teams less direct, self-serve control over automated decision rules.

Alice

Alice (formerly ActiveFence) is a trust and safety platform built around detecting coordinated, high-risk abuse and providing threat intelligence across large-scale online platforms and AI systems.

What it catches. For UGC moderation: text, image, video, and audio content, with particular depth in hate speech, grooming, coordinated disinformation, violent extremism, and CSAM. For AI model security: prompt injection, jailbreak attempts, unsafe or policy-violating model outputs, and model drift over time.

Both are powered by Rabbit Hole, a proprietary threat intelligence engine built on more than 10 billion analyzed toxic, manipulative, and adversarial samples across 100+ languages.

How review actually works. For UGC, Alice combines pre-trained models, no-code workflows, and human expertise, with AI-only, human-only, or hybrid review options depending on risk level.

For AI model security specifically, Alice offers automated red-teaming (stress-testing a model for vulnerabilities before launch), real-time runtime guardrails (filtering unsafe inputs and outputs from a live model or agent), and ongoing drift detection as models get fine-tuned or updated over time. Alice packages these as WonderSuite: pre-launch testing, runtime guardrails, and ongoing production monitoring.

Where it fits:

• Large platforms facing coordinated abuse, extremism, or disinformation campaigns, not just individual pieces of harmful content.
• Teams building, fine-tuning, or deploying their own generative AI models or agents that need security testing and runtime protection, not just content classification.
• Teams with strict data residency requirements. Alice can deploy inside a customer's own AWS environment so interaction data doesn't leave that perimeter.

Where it falls short:

• No public pricing or self-serve option, which makes it harder to evaluate cost early without a sales conversation.
• No lightweight, developer-first integration path. Alice is built for large-scale enterprise trust and safety operations.
• Less suited to teams that just need routine content classification (spam, basic profanity, nudity) without the added threat-intelligence layer, which may be more platform than a smaller or lower-risk app needs.

Checkstep

Checkstep is built around governance and control rather than pure detection speed. It's a SaaS platform that lets teams connect their own models, manage human review processes, and export detailed compliance reports, positioned specifically for teams that need to explain and defend moderation decisions.

What it catches. Text, image, video, and audio content across 100+ languages. Teams can connect their own custom-trained detection models to Checkstep's platform rather than relying solely on Checkstep's built-in classifiers, which is a meaningfully different approach from most vendors on this list.

How review actually works. Human review is available as an optional module, with tooling for queues, escalation, and quality control, but Checkstep doesn't provide its own staffed review workforce the way WebPurify or Besedo do. The platform's real differentiator is on the back end; it offers detailed audit trails and compliance reports built specifically for DSA and Online Safety Act reporting requirements.

Where it fits:

• Teams needing documented, defensible moderation decisions for regulators, auditors, or internal legal review.
• Platforms operating in the EU or UK specifically, given Checkstep's built-in DSA and Online Safety Act reporting tooling.
• Teams that already have their own detection models trained and want a governance layer to sit on top of them, rather than replacing them.

Where it falls short:

• Less suited to teams that want to move fast with pre-trained, out-of-the-box detection. Checkstep's value is concentrated in governance and custom-model support, which matters less for a team without its own models or complex compliance needs.
• No in-house human moderation workforce.
• No published per-item or per-request pricing, so cost isn't estimable without a sales conversation.

Besedo

Besedo has operated in trust and safety for over 20 years and reports processing billions of moderated items annually across marketplaces, dating, communities and forums, gaming, and news and publishing.

What it catches. Text, image, video, and audio content, with particular strength in fraud, scam, and counterfeit-listing detection, plus real-time toxicity, harassment, and spam detection for community platforms. A language detection feature (covering 123 languages) is specifically used to catch fraud that other detection methods miss - for example, scammers posting listings in a different language than a site's expected one, which flags for review even when the content itself doesn't trip a standard filter.

How review actually works. Besedo offers real flexibility here: platforms can outsource moderation entirely to Besedo's own human moderators, keep it fully in-house using Besedo's tooling, or run a hybrid where AI handles clear cases and humans handle gray areas. Besedo reports automated decisions averaging under 50ms. This range, from fully outsourced to fully self-managed, is broader than most vendors on this list, which tend to pick one model or the other.

Where it fits:

• Platforms that want the option to outsource human review entirely, not just get tooling to build their own team.
• Marketplaces specifically fighting fraud, scams, and listing quality or community and forum platforms needing real-time moderation at high volume.
• Global platforms with multilingual user bases, with 123-language detection that captures cultural and slang-level nuance.

Where it falls short:

• No dedicated CSAM hash-matching integration.
• No published per-item or per-request pricing, so cost isn't estimable without a sales conversation.
• No published compliance certifications (SOC 2, ISO 27001, GDPR-specific attestation) or enterprise security tooling like SSO/SAML and audit logs; we recommend confirming directly with Besedo if that's a hard requirement for your platform.

Cleanspeak

Cleanspeak is built specifically around real-time text filtering rather than broad multimodal detection, with deep roots in gaming and kids' communities. It was originally built for an online kids' game, which shows in its focus on username filtering, kid-safe whitelisting, and COPPA-relevant use cases.

What it catches. Text, image, and video content, with filtering for profanity, slurs, personal information (email, phone, PII/PHI), and URLs across 15+ languages. Cleanspeak can monitor and manage audio content but cannot filter it directly.

How review actually works. Teams choose between two models: let Cleanspeak take automated action based on user scoring (reducing moderation costs but with less human judgment involved), or route flagged content to a moderation queue for human review. Both can be configured per "Application," meaning different rules for chat, forum posts, and usernames within the same account.

Where it fits:

• Gaming platforms and kid-focused communities specifically, given Cleanspeak's origins and COPPA-relevant features like whitelist-based kids' chat filtering.
• Teams needing very high-throughput, low-latency text filtering. Cleanspeak reports filtering 10,000+ messages per second on a single server.
• Teams that want flexible hosting: self-hosted, on-premise, or Cleanspeak's private managed cloud, rather than being locked into one deployment model.

Where it falls short:

• Cannot filter audio content directly, only monitoring and management.
• Less suited to platforms needing broad, multimodal AI detection (deepfakes, nuanced image classification, contextual LLM-based review). Cleanspeak's strength is real-time text and username filtering specifically.

Picking the Right Fit

Selecting the right content moderation API starts with an honest look at your platform's risk profile. Do you serve minors or other vulnerable users? Operate in a regulated industry? Handle image, video, or audio content in addition to text? Face coordinated abuse or just occasional user complaints?

Then, start testing.

Most of these vendors offer some kind of free trial or sandbox, and a real test with your platform's actual content will surface things a spec sheet won't.

At the end of the day, the right API is the one that fits the risk you have now and still fits when your volume is ten times higher.