What you actually get from Hive AI
Hive is a San Francisco based AI company founded by Kevin Guo and Dmitriy Karpman that raised $85m in April 2021 at a $2 billion valuation. Best known for content moderation APIs used by platforms like Reddit, Giphy, BeReal, and Truth Social, Hive also sells a dedicated AI Image + Deepfake Classifier alongside AI Video Detection and AI Audio Classification models. Pricing is fully usage-based and published on its own site down to the dollar amount per 1,000 requests, with a free daily request limit and pay-as-you-go billing after a card is added. Larger volumes, higher rate limits, and the video-frame pipeline move to a custom Enterprise quote.
Hive AI pricing
Pay-as-you-go: AI Image + Deepfake Classifier is $6.00 per 1,000 requests, 100 free/day; higher limits and video are custom quote.
| Plan | Price | What it covers |
|---|---|---|
| Developer | $50+ in free credits after adding a payment method | Pay-as-you-go access to 10+ Hive models, default rate limits, AutoML custom model training |
| AI Image + Deepfake Classifier | $6.00 / 1,000 image requests | Limit of 100 requests/day at this rate; identifies AI-generated images and deepfakes |
| AI Video Detection | $6.00 / 1,000 video frames | Limit of 100 requests/day at this rate; identifies AI-generated video clips |
| Enterprise / higher limits | Custom quote | All Hive models, Hive Moderation Dashboard, highest rate limits, multi-region support, premium support |
Source: thehive.ai/pricing, read 2026-09-07.
Strengths
- Exact self-serve per-request pricing published for image, video, and audio AI-content detection
- Proven at scale, powers moderation for Reddit, Giphy, BeReal, and Truth Social
- Covers image, video (frame-sampled), and audio synthetic-media detection in one API family
- 10+ models plus AutoML custom model training available on the Developer tier
- Free daily request allowance (100/day) to test before paying
Weaknesses
- Deepfake detection is bundled inside a much larger moderation product line, not a standalone dedicated tool
- Higher-volume access and video pricing at scale require Contact Sales, not published
- No forensic/legal-report output like the vendors purpose-built for investigations
- Usage-based pricing means costs scale unpredictably with volume compared to a flat subscription
Best for
- Developers and platforms needing a programmatic API for deepfake and AI-image detection
- Companies already using Hive for content moderation who want to add deepfake detection
- Teams that prefer transparent pay-as-you-go pricing over a sales call
Not ideal for
- Non-technical users wanting a no-code web dashboard for one-off checks
- Legal or forensic investigations needing court-ready reports
- High-volume users unwilling to negotiate a custom Enterprise rate
Hive AI is built for volume. Reputation damage is a tail-risk problem
Hive AI handles throughput well, and for the operational job of processing a lot of items consistently it is a sound choice. High-volume tooling is genuinely hard to build.
The mismatch is statistical. Volume tools optimise the average: more reviews, more mentions, more coverage, better aggregate numbers. Reputation risk does not live in the average. It lives in the tail, in the single post, the single article, the single thread that behaves differently from the other ten thousand. Optimising the mean is close to useless against a distribution where one observation carries most of the damage.
A summariser does not average a thousand data points. It reaches for the outlier.
This is why businesses with healthy dashboards still get blindsided. Aggregate health and tail exposure are close to independent. A system tuned to raise your average buries the one signal that mattered inside the noise it was built to produce. The tool was not wrong. It was answering a different question.
Where this lands in 2026
AI answers amplify the tail specifically. A model summarising a subject does not average a thousand data points into a fair picture. It picks the most quotable, most linked, most specific claim it can find, and that is the outlier. The one bad thing is exactly what a summariser reaches for.
That is why this review sits on the site of a firm that competes with Hive AI. The argument is not that Hive AI is a bad product. Its category was designed against a version of your problem that no longer describes the whole surface. The part it was never built to cover is the part growing fastest.
A detection score is evidence, not a remedy. The work that ends a deepfake is the takedown chain: platform reports, hosting complaints, registrar escalation, and the paper trail that survives a re-upload.
Key facts
- Founded by Kevin Guo and Dmitriy Karpman; raised $85m in April 2021 at a $2 billion valuation
- Content moderation models used by Reddit, Giphy, BeReal, and Truth Social
- Also branded as Hive Moderation for its moderation-focused product line
- AI Image + Deepfake Classifier and AI Video Detection are both listed as distinct priced products on thehive.ai/pricing
Hive AI covers one lane. We cover the surface.
You get deepfake & synthetic-media removal as one protocol inside a measured engagement, not as a standalone subscription. Your exposure is measured first against a Bayesian baseline, then the counter-strike work runs, then your surface stays monitored across search, review platforms, and the AI answer layer.
Sources checked
We read the vendor's own pages for every price on this review and record the date we read them. Where a figure comes from a third party we say so. Method: review methodology.