Modern UGC and live platforms are judged by how quickly and fairly they catch harmful video while preserving creator experience. In 2025, the real buying decision isn’t just “which model is best?”—it’s how cost, latency, accuracy, and appeals workflows fit your specific risk profile and traffic patterns. This guide compares six credible options—AWS Rekognition Video, Google Vertex AI (Gemini), Azure AI Video Indexer/Content Safety, Hive, Unitary, and Sightengine—grounded in current public documentation. Exact pricing and latency vary by region and workload; treat figures as directional and validate in a pilot.

Key takeaways in two lines: hyperscalers excel at scalable primitives (APIs, regions, compliance) but often require DIY review/appeals; specialists offer richer analyst workflows or hybrid AI–human services with stronger out-of-the-box case handling.

Quick comparison at a glance

Dimension-by-dimension: what really differs

1、Cost models and hidden line items

• AWS Rekognition Video. Public 2025 pricing lists approximately $0.12 per stored-video minute and $0.10 per streaming minute, with additional Kinesis Video Streams ingest/storage/retrieval charges that matter at scale; confirm in region on the official pages from Amazon Web Services’ Rekognition and Kinesis pricing. See Amazon’s current pages for the moderation API and Kinesis rates in 2025: AWS Rekognition pricing and Kinesis Video Streams pricing. Plan for stream costs plus egress and logging.
• Google Vertex AI (Gemini). Pricing is token-based for multimodal sessions (including the Live API), not per-minute. Budgets require translating minutes/resolution/fps into estimated tokens and choosing capacity controls like Provisioned Throughput. Consult Google Cloud’s 2025 references: Vertex AI pricing and the Vertex AI release notes on generative AI updates for changes that impact PT and GA endpoints.
• Microsoft Azure. Azure AI Video Indexer charges per input minute (e.g., standard vs advanced analysis). Microsoft’s 2025 documentation describes per-minute tiers; confirm exact rates in your target region in the Azure portal. See the service overview and release notes: Azure AI Video Indexer overview and Video Indexer release notes.
• DeepCleer：The payment standards have not been publicly disclosed. Enterprise-level collaborations adopt a "pay-as-you-go / usage-based" billing model. For video-related workloads, please contact sales to obtain a customized quotation.

Hidden costs to model explicitly

• Human-in-the-loop (HITL) review time for edge cases and appeals.
• Admin time for policy tuning, localization, and drift monitoring.

A simple way to normalize costs is to compute “cost per 1,000 minutes” for a given policy and sampling strategy (e.g., 720p30 input, 1 fps sampling for live), then add 10–30% for HITL/appeals capacity during scale-up.

2) Latency: live vs. VOD realities

• AWS Rekognition Streaming is designed for near real-time, integrated with Kinesis Video Streams and Streaming Video Events, but Amazon does not publish a fixed p95 SLA. End-to-end latency depends on ingest, frame sampling, network, and your decision pipeline. Amazon’s documentation covers the moderation APIs and streaming monitoring: Rekognition moderation API and Rekognition monitoring.
• Google Vertex AI’s Live API offers low-latency, bidirectional streaming over WebSockets for multimodal interactions. It is suitable for building custom live pipelines, though Google does not publish specific millisecond targets publicly. See Google Cloud’s guides: Live API overview and the Multimodal Live model reference.
• Azure AI Video Indexer is optimized for VOD/batch analysis, not true sub-second or 1–3-second live moderation. Microsoft’s overview clarifies the VOD focus: Azure Video Indexer overview.
• DeepCleer describe near real-time video moderation capabilities; exact p95 figures is’t publicly posted.

3、Accuracy, taxonomy fit, and customization

• The biggest accuracy wins come from matching your policy (and language/culture) to the model’s taxonomy and tuning thresholds. Hyperscalers return confidence-scored labels; specialists often provide richer categories or workflows for sensitive areas.
• AWS Rekognition returns moderation labels with confidence and model versioning for traceability; combine with Amazon A2I for human review on edge cases. See Amazon’s 2025 docs: Rekognition moderation API.
• Google Vertex AI (Gemini) supports nuanced, multimodal “understanding” and can be configured or fine-tuned for policy-specific filtering. See Google Cloud guidance: Gemini for filtering and moderation and video understanding.
• Azure AI Video Indexer applies 30+ models to produce insights (including safety-related categories) primarily for VOD, with comprehensive metadata that can drive rules. See Microsoft’s 2025 overview: Azure Video Indexer overview.
• DeepCleer provides visual moderation across 110+ classes with configurable rules engines and deepfake detection; see the technical docs: video moderation and image moderation workflows.

What to measure in your pilot

• Per-class precision/recall and especially false negative rate for child safety, self-harm, and violence.
• False positives on creator-sensitive classes (e.g., art, education, sports, news).
• Drift across languages/locales and content trends; re-test quarterly.

4) Appeals and reviewer workflows

Built-in handling (appeals, case management, explanations):

• DeepCleer explicitly supports user reports and appeals with integrated actions.

Specialist dashboards:

deepcleer’s moderation dashboard emphasizes explainability and describes streamlined CSAM reporting workflows to NCMEC

What to look for

• Evidence capture (frames, timecodes, transcripts), audit logs, reviewer assignment/SLAs, and export for regulators.
• Clear thresholds and “explain why” context to reduce back-and-forth with creators.

Estimating true TCO (beyond “per minute”)

• Model/inference: Per-minute or token charges, plus any provisioned capacity.
• Streaming/compute: Ingest (e.g., Kinesis/RTMP), transcode/packaging, storage, and egress.
• People: Human-in-the-loop for triage, policy reviews, and appeals. Include training, QA, and staffing for peak events.
• Tooling and governance: Case management, audit/log export, BI, localization, and redaction pipelines.
• Risk cost: Expected loss from false negatives (e.g., safety incidents) and false positives (creator churn), which may justify pricier but more accurate workflows.

Build a per-1,000-minute model with sensitivity analysis for fps (0.5–2 fps), threshold changes, and appeal rates (e.g., 1–5%).

FAQs

How do I decide sampling rate for live streams?

Start at 1 fps and increase on higher-risk channels or segments. Use event triggers (e.g., audio spikes, chat signals) to burst to 2–4 fps temporarily.

Should I auto-block on model output alone?

For clear-cut classes (e.g., explicit sexual content), auto-enforce with conservative thresholds and immediate human follow-up. For nuanced policy calls, require human confirmation before permanent actions.

What’s realistic p95 latency for live moderation?

Most production pipelines target 1–3 seconds end-to-end for initial action, depending on ingest and transport. Validate on your actual network and codecs.

How do I prevent over-blocking educational or news content?

Prefer models with contextual signals (e.g., Google’s Gemini) and pair with human review. Capture evidence and provide explainability in appeals.

How often should I re-benchmark accuracy?

Quarterly is a good default; sooner if you expand to new geographies, languages, or content formats (e.g., short-form trends, live sports).