Sightengine vs Hive vs AI Image Detector API: 2026 Comparison

Choosing between AI image detection APIs in 2026 is a real decision. The three names that come up most often in vendor evaluations — Sightengine, Hive, and our own AI Image Detector API — each take a different approach, and the right pick depends on what you're building.

This is a vendor comparison written by one of the vendors. We'll be honest about where competitors are stronger; readers can discount our enthusiasm where appropriate. Where we're better, we'll show numbers.

We'll cover positioning differences, accuracy and coverage, latency and throughput, pricing, integration ergonomics, support and SLA, and which use cases each is best for. At the end, you'll have enough to either pick one or design a multi-vendor strategy.

The shortest possible summary

Each is the right answer for some buyers. Skip to the use case fit section below if you want to triangulate fast.

Positioning and approach

Sightengine

Sightengine has been in image moderation since 2014 — long before AI generation was a meaningful category. They built their reputation on NSFW detection, content classification (violence, weapons, drugs), and brand-safety analysis. AI-detection was added to their feature catalog in 2023 as one operation among many.

This works well if you need a single vendor for all content classification — NSFW + violence + AI-detection + OCR + face analysis — because Sightengine bundles these into a unified API. If you mostly need AI-detection specifically, you'll find their detection model is competitive but not specialized.

Strengths: mature company, broad feature set, good API documentation, transparent pricing, EU-based with strong privacy stance.

Weaknesses: AI-detection model wasn't their original focus; per-model attribution (Midjourney vs Stable Diffusion vs Flux) is less detailed than specialist vendors; pricing tiers can get expensive for high-volume AI-only use cases.

Hive

Hive is the heavyweight enterprise player. Founded in 2017, focused on large-scale content moderation for major platforms (Reddit, Vimeo, BeReal, dozens of others). Their AI-detection capability was added in 2022 and is integrated into their broader moderation suite.

Hive's strength is scale: they handle billions of moderation operations per month and have dedicated enterprise sales, custom-model training, and white-glove support. Their detection model is well-trained and reliable.

Strengths: enterprise-grade SLAs, custom model training, strong coverage of all moderation categories, established vendor with major-platform reference customers, sophisticated dashboards and analytics.

Weaknesses: no public pricing or self-serve free tier — you talk to sales. Minimum commitments are typically high (annual contracts, $30K+ floors). Not a fit for small teams or evaluation.

AI Image Detector API (us)

We're the youngest of the three, founded in 2024 specifically to build the best AI-detection API rather than to add it as a feature to a broader moderation suite. Our model is purpose-built for the AI-detection problem with detailed model attribution, calibrated confidence scores, and adversarial robustness.

We don't try to replace Sightengine or Hive for general moderation needs — we focus on doing AI-detection (image and video) very well, with developer-friendly pricing, transparent free tier, and direct technical support.

Strengths: highest detection accuracy on AI-specific benchmarks, most detailed model attribution (down to version: Midjourney v6 vs v7), sub-100ms latency, transparent free tier and self-serve pricing, modern developer experience.

Weaknesses: newer company with shorter track record. We don't offer the broader moderation suite — if you need NSFW + violence + brand safety together with AI-detection, you'll need a separate vendor for those (or use us alongside Sightengine).

Accuracy comparison

Accuracy is a tricky metric to compare across vendors because each publishes different benchmarks and defines accuracy differently. Our accuracy guide goes deeper on methodology — read that if you're evaluating any AI-detection API seriously.

For this comparison, we ran our own internal benchmark in early 2026 across three vendors using a set of 5,000 images: 2,500 real photos (mix of professional, smartphone, edited, compressed) and 2,500 AI-generated (mix across Midjourney v6/v7, Flux Pro, DALL-E 4, Stable Diffusion 4, Sora-image).

Headline numbers from this benchmark:

Caveats:

• These are our numbers, run on our benchmark. Hive and Sightengine likely have benchmarks where they perform better than us. Always run your own evaluation.
• The benchmark is composed to reflect the AI-detection use case specifically; for general content moderation, all three vendors have different but comparable performance.
• These numbers will change. Detection vendors update their models continually; the spread between us could narrow or widen.

If accuracy at deployment threshold is the make-or-break criterion, we recommend running a 1,000-image evaluation across all candidate vendors before committing. The setup time is a day; the data is worth it.

Model attribution

This is where specialist vs general-purpose vendors diverge most sharply.

Sightengine returns a binary ai_generated: true/false with confidence. They do not return which model produced the image.

Hive returns a binary verdict plus attribution among broad categories: GAN-based, diffusion-based, etc. They don't typically distinguish between specific generators (Midjourney vs Flux vs Stable Diffusion).

AI Image Detector API returns model attribution down to the specific generator and version. A typical response includes confidence breakdowns across Midjourney v6, Midjourney v7, Flux Schnell, Flux Pro, DALL-E 3, DALL-E 4, Stable Diffusion XL/3/4 fine-tunes, Sora-image, Imagen, and several less-common generators.

If you don't care which generator produced the image, Sightengine and Hive are sufficient. If you care — for example, because your fraud team is tracking which generators are being used in attacks against you, or because you want to apply different policies to different generators (Midjourney content allowed and labeled; Stable Diffusion content under stricter scrutiny because it's more often used for non-consensual content) — model attribution matters.

Latency and throughput

Latency comparison from our benchmarks (p50 / p99, single-image scan, US East region):

For real-time content moderation flows (where the user is waiting on the upload), <100ms is roughly the threshold below which detection is invisible. Above 200ms you start to add perceptible delay.

For async/batch flows (where detection runs in the background after upload), latency matters less and throughput matters more.

Pricing comparison

Sightengine's published pricing as of mid-2026:

• Free tier: 2,000 operations/month
• Starter: $99/month (10K ops)
• Pro: $399/month (100K ops)
• Enterprise: custom

Hive does not publish public pricing. Customer-reported pricing typically:

• Minimum annual commitment $30K-$100K
• Per-scan rates around $0.001-$0.005 depending on volume
• Custom-model training adds significant cost

Our pricing (see /pricing/):

• Free: 500 scans/month, no credit card
• Pro: $49/month (50K scans)
• Enterprise: custom (typically $500-$5K/month based on volume)

The Pro tier comparison is striking: $49/50K scans (us) vs $99/10K scans (Sightengine) vs custom-but-typically-thousands (Hive). The reason is positioning — we're an AI-detection specialist optimizing for that specific cost; Sightengine and Hive bundle other capabilities into the price.

If AI-detection is your only use case, the cost difference is significant. If you need the bundle, it might be worth it.

Integration experience

All three vendors offer REST APIs with official SDKs in Python, Node.js, and a few other languages. Documentation quality varies.

Sightengine has good documentation, a clean OpenAPI spec, and decent example code. The free-tier signup is straightforward; you get an API key in minutes. The API surface is large because they cover so many operations — finding the right endpoint sometimes takes a minute.

Hive has comprehensive enterprise documentation but most of it is gated behind a sales conversation. Self-serve evaluation is limited. Once you're a customer, the dashboards and analytics are top-notch.

Our API is built explicitly for self-serve. Sign up takes 30 seconds; first API call is documented in our quickstart at under 2 minutes. SDKs in Python, Node, Go, Ruby, PHP, and Java. The API is small (one main detection endpoint plus utilities for batch and webhooks) so integration is fast.

For developer experience, we prioritize ours; for enterprise integration with a dedicated rep, Hive's experience is more mature.

Support and SLA

Sightengine:

• Free and Starter tiers: email/forum support, no SLA
• Pro tier: email support with 24h response target
• Enterprise: SLA-backed support, 99.9% uptime guarantee

Hive:

• All tiers (which start at enterprise): SLA-backed, 24/7 support, dedicated CSM, 99.95%+ uptime
• Custom contractual terms negotiable

Us:

• Free: community forum, no SLA
• Pro: email support, ~6h response target during business hours, 99.9% uptime targeted
• Enterprise: SLA-backed, 99.95% uptime guarantee, dedicated Slack channel, custom contracts

For mission-critical pipelines, Hive's enterprise support is the most mature in the industry. For mid-market and growth-stage companies, our Pro tier offers reasonable support without the enterprise contract overhead.

Use case fit

Boiling down to recommendations by use case:

You need general content moderation including NSFW, violence, brand safety, AND AI-detection in one vendor: Sightengine. Their bundle is the right shape; AI-detection is good enough.

You're a major platform processing millions of images per day and need enterprise-scale support, custom training, white-glove service: Hive. They're built for this.

You're an AI-detection-specific use case (insurance fraud, journalism verification, content authenticity, marketplace product photos) and need best-in-class detection: us. We're built specifically for this.

You're a small team or startup evaluating options and need a free tier to actually try: us or Sightengine. Hive doesn't offer a self-serve free tier.

You need detailed model attribution (which generator, which version): us. Sightengine and Hive don't offer this at our level of detail.

You need the absolute lowest latency for real-time moderation flows: us. Our p50 of 87ms is the lowest among the three.

You're sensitive about data residency, EU privacy, GDPR: Sightengine has the most mature EU presence; we offer EU regional endpoints; Hive is US-headquartered with EU options on enterprise.

You're considering a multi-vendor strategy for cross-validation: Run two of these together. We pair well with Sightengine (their general moderation + our AI-specific detection covers everything; minimal overlap). We also pair with Hive for high-stakes cases where you want two independent AI-detection signals.

Multi-vendor strategy

For high-stakes use cases (insurance fraud at scale, financial-services KYC, court-evidence verification), running two independent detectors is increasingly common. Pattern:

• Vendor A scans every image. Score above its threshold → flag.
• Vendor B re-scans flagged images. Score above its threshold → high-confidence flag.
• Disagreement → human review.
• Both clear → trust the verdict.

This roughly doubles the detection cost but reduces both false positives (require agreement) and false negatives (catch what either alone would miss). For workflows where the cost of a missed fraud case is six figures and the cost of a scan is cents, the math is obvious.

Our pillar guide on detecting AI-generated images covers the broader detection workflow including multi-vendor patterns.

What's not in the comparison

Several considerations we didn't cover in depth, in case they matter to you:

• CSAM detection — none of the three vendors above is the right answer for CSAM detection workflows. Use NCMEC PhotoDNA or Thorn for that specific need.
• Face recognition — different problem; different vendors (Clearview, AWS Rekognition, etc.).
• Text-in-image OCR — Sightengine includes basic OCR; Hive offers OCR; we don't.
• Brand and logo detection — Sightengine and Hive cover this; we don't.
• Custom model training — Hive offers most-flexible custom training; we offer fine-tuning at the Enterprise tier; Sightengine doesn't offer custom AI-detection training.
• On-premise / air-gapped deployment — Hive offers this for very large enterprise contracts; we and Sightengine are SaaS-only.

If any of these matter, factor them into your evaluation accordingly.

Frequently asked questions

Which has the highest accuracy?

Depends on the benchmark. On our own internal benchmark focused on AI-detection specifically (composed to match real-world distributions), we measured ours highest. On general moderation benchmarks (NSFW + violence + AI-detection blended), Hive's blended score is competitive. Sightengine performs well on their published moderation benchmarks. Always run your own evaluation on data that matches your use case.

Can I use multiple vendors together?

Yes — this is increasingly common for high-stakes use cases. The integration overhead is moderate (a single call site abstraction can wrap both APIs). The redundancy improves both false-positive and false-negative rates.

Is there a vendor not in this comparison I should look at?

Several other AI-detection-capable APIs exist: Reality Defender (focused on deepfakes specifically), Truepic (focused on provenance and C2PA), Optic AI or Not (smaller but growing), AWS Rekognition (AI-detection added 2024). For specialized needs (real-time deepfake detection in video calls, broadcast journalism verification, etc.) any of these may be the right pick. We're focused on image (with video supplemental); for video-first workflows, Reality Defender is worth evaluating.

How do I evaluate the right vendor for my use case?

Build a 1,000-2,000 image test set that matches your production distribution. Run it through every candidate vendor. Compute precision, recall, and false-positive rate at the threshold you'll deploy at. Inspect the false positives and false negatives — patterns in errors matter more than aggregate scores. The full methodology is in our accuracy guide.

Are pricing comparisons stable?

Vendor pricing changes; this is mid-2026 data. Sightengine raised prices about 15% in 2025; Hive's enterprise contracts vary; ours have been stable since launch. Verify pricing directly with each vendor before final selection.

Can I switch vendors easily later?

Yes — all three offer roughly comparable API shapes. The integration is small enough that switching takes 1-2 weeks of engineering work. Most companies do build a vendor abstraction layer so they can A/B test or switch without major rework.

The right detection vendor depends on what you're building, what your scale is, what your budget is, and how much you need detection-specific depth vs general moderation breadth. The honest version of this comparison: Sightengine is the best general-moderation choice; Hive is the best enterprise-scale choice; we're the best AI-detection-specialist choice.

If you want to actually try us — free tier covers 500 scans per month with no credit card — grab an API key in 30 seconds and run your own test. We'll send you our most recent benchmark and calibration plot on request so you can compare apples-to-apples against any vendor you're evaluating.