Process and moderate content in real-time with sub-50ms latency powered by distributed architecture, event-driven pipelines, and intelligent edge deployment. Built for platforms handling millions of concurrent requests with consistent performance worldwide.
Our real-time content moderation engine is built on a globally distributed, event-driven architecture that processes every piece of content the moment it is created, ensuring harmful material never reaches your users.
Content enters an event-driven pipeline where messages are processed asynchronously through specialized microservices, enabling parallel analysis of text, images, and video without blocking.
AI models deployed across 50+ edge locations on six continents process content at the nearest node, eliminating round-trip delays and delivering consistent sub-50ms response times globally.
Kubernetes-orchestrated containers scale from baseline to 10x capacity within seconds, handling viral events and traffic spikes without any manual intervention or performance degradation.
Content hash caching, semantic similarity caching, and edge CDN caching return previously analyzed content results in under 5ms, dramatically reducing average response times for similar content.
Persistent WebSocket connections deliver moderation results as a continuous stream, ideal for live chat, real-time feeds, and streaming content that requires instant feedback loops.
High-volume batch endpoints process millions of historical content items concurrently, enabling retroactive policy enforcement and bulk content audits without impacting real-time performance.
Every millisecond matters in real-time content moderation. Our latency optimization strategy attacks delay at every layer of the stack, from network routing and TLS handshake acceleration to model inference optimization and response serialization. The result is a median processing time of 18ms for text and 35ms for images, with P99 latencies consistently below 50ms even under heavy load.
We achieve these numbers through quantized AI models that reduce inference time by up to 4x, ONNX Runtime acceleration on dedicated GPU clusters, and connection pooling that eliminates per-request overhead. Anycast DNS routing directs every request to the geographically closest edge node, while TCP Fast Open and HTTP/2 multiplexing further shave precious milliseconds from every API call.
Key optimizations include: Model distillation for edge hardware, speculative pre-computation of likely moderation paths, adaptive batching that groups concurrent requests for GPU efficiency, and intelligent load shedding that prioritizes latency-sensitive synchronous requests over background processing.
Our content moderation infrastructure spans a network of over 50 strategically positioned edge nodes connected by a private fiber backbone. Each node runs the complete suite of moderation models, meaning content never needs to travel to a centralized data center for analysis. This fully distributed topology provides natural fault tolerance, as any single node failure simply reroutes traffic to the next nearest location within milliseconds.
The network operates on a mesh architecture where edge nodes communicate through low-latency internal links, synchronizing model updates, policy changes, and threat intelligence in near-real-time. New moderation policies propagate to every node within 60 seconds of deployment, ensuring globally consistent enforcement without service interruption. Each node maintains local replicas of moderation databases, caches, and model weights for fully autonomous operation.
Architecture highlights: Multi-region active-active deployment, automated failover with zero-downtime recovery, geographic data residency controls for GDPR and regional compliance, and private network interconnects between all major cloud providers.
Our processing pipeline sustains 10 million requests per second at steady state and can burst to over 50 million requests per second during peak events like breaking news cycles, viral content surges, or coordinated platform attacks. These numbers are achieved through a combination of horizontal auto-scaling, GPU cluster elasticity, and intelligent request routing that distributes load evenly across all available compute resources.
Burst handling is powered by a multi-tier queuing system that absorbs sudden traffic spikes without dropping requests. The first tier is an in-memory ring buffer at each edge node that handles microsecond bursts. The second tier is a distributed message queue (based on Apache Kafka) that absorbs sustained surges for seconds to minutes. The third tier triggers auto-scaling events that provision additional compute capacity within 15 seconds, ensuring the system gracefully handles any traffic pattern.
Throughput metrics: Text analysis at 50,000 items/sec per node, image classification at 12,000 images/sec per GPU, video frame analysis at 240 frames/sec per stream, and WebSocket message throughput of 2 million messages/sec across the cluster.
Every moderation request flows through a carefully orchestrated pipeline designed for both speed and accuracy. The journey begins at the API gateway, which authenticates the request, applies rate limiting, and routes it to the optimal processing node. The content then enters the classification router, which determines the required analysis types based on content format, customer policy configuration, and threat intelligence signals.
From the router, content flows through parallel analysis stages. Text is simultaneously checked for toxicity, hate speech, spam, and policy violations. Images undergo visual classification, OCR text extraction, and NSFW detection in parallel. Video content is split into keyframes for visual analysis while the audio track is transcribed and analyzed separately. All results converge at the decision engine, which applies the customer's policy rules and returns a unified moderation verdict.
Pipeline stages: Ingestion and validation (2ms), content routing (1ms), parallel analysis (10-30ms), policy evaluation (3ms), response assembly (2ms). Total end-to-end latency is consistently under 50ms for all content types, with caching reducing repeat content analysis to under 5ms.
Real-time content moderation represents one of the most demanding distributed computing challenges in modern technology. Unlike traditional batch processing systems that can afford to accumulate content and process it in scheduled intervals, real-time moderation must analyze and render a verdict on every piece of content before it reaches any other user. This constraint transforms the problem from a throughput challenge into a latency challenge, where every architectural decision must be evaluated against its impact on end-to-end response time.
The foundation of our real-time processing engine is an event-driven architecture built on the principles of reactive systems. When content is submitted through our API, it is immediately published as an event to our distributed message bus. This event triggers a cascade of processing steps that execute concurrently across multiple specialized services. The text analysis service, image classification service, and policy evaluation service all receive the event simultaneously and begin their work in parallel, dramatically reducing total processing time compared to sequential architectures.
Each processing service is stateless and horizontally scalable, meaning the system can add capacity for any specific analysis type independently. If image analysis is the bottleneck during a period of heavy photo uploads, only the image processing tier scales up, while text analysis continues at its current capacity. This granular scaling approach minimizes cost while maximizing performance across all content types.
The physics of network latency impose hard limits on how fast content can travel between distant locations. A round trip from Tokyo to Virginia takes approximately 150ms at the speed of light through fiber optic cables, and real-world routing adds additional overhead. To achieve sub-50ms latency globally, we deploy complete processing stacks at edge locations close to where content originates. Our edge network spans North America, South America, Europe, Africa, Asia, and Oceania, ensuring that the vast majority of global internet users are within 30ms of an edge node.
Each edge node is a fully autonomous processing unit capable of rendering moderation decisions without communicating with any other node. This design eliminates the single point of failure inherent in centralized architectures and provides natural resilience against network partitions. If an undersea cable is severed or a regional outage occurs, affected edge nodes continue operating independently using their local model weights and policy configurations. When connectivity is restored, nodes synchronize their state automatically, and any policy updates that were queued during the partition are applied.
Edge deployment also enables compliance with data residency regulations. Content submitted from the European Union can be processed entirely within EU-based edge nodes, never leaving the geographic boundaries required by GDPR. Similarly, content from regions with strict data localization laws is processed and stored locally, with only anonymized analytics data aggregated centrally for reporting purposes.
Traffic patterns in content moderation are inherently unpredictable. A viral event, a breaking news story, or a coordinated attack can cause traffic to spike by 10x or more within minutes. Our auto-scaling infrastructure is designed to handle these sudden surges without any degradation in latency or accuracy. The scaling system monitors multiple signals including request queue depth, CPU utilization, GPU memory usage, and response time percentiles. When any signal indicates growing demand, the system proactively provisions additional compute capacity before latency is affected.
We maintain a warm pool of pre-initialized containers at every edge location, ready to accept traffic within seconds of a scale-up signal. These warm containers have already loaded model weights into GPU memory, completed health checks, and registered with the load balancer. This pre-warming strategy reduces scale-up time from minutes to single-digit seconds, ensuring that even sudden traffic spikes are absorbed gracefully. During normal operations, the warm pool represents a modest resource overhead, but it eliminates the cold-start penalty that would otherwise cause latency spikes during scaling events.
Scale-down is equally important for cost efficiency. Our system uses predictive algorithms trained on historical traffic patterns to anticipate demand reductions and gracefully drain excess capacity. Containers are removed only after all in-flight requests have completed, ensuring zero dropped connections during scale-down events. The predictive model accounts for time-of-day patterns, day-of-week cycles, seasonal trends, and known events to optimize resource allocation proactively.
For platforms with real-time content streams such as live chat, social feeds, and streaming platforms, our WebSocket API provides a persistent connection that delivers moderation results with minimal overhead. Unlike traditional REST APIs that require a new HTTP connection for each request, WebSocket connections remain open, eliminating connection setup latency and enabling bidirectional communication. Content flows into the WebSocket as a stream, and moderation verdicts flow back as a corresponding stream, creating a continuous feedback loop with sub-millisecond framing overhead.
The WebSocket interface supports multiplexing, allowing a single connection to carry moderation requests for multiple content streams simultaneously. This is particularly valuable for platforms managing thousands of concurrent chat rooms or live streams, where maintaining a separate connection per stream would be impractical. Each stream within the multiplexed connection receives its own flow control and backpressure signals, preventing a slow consumer from blocking delivery to other streams.
Our WebSocket implementation includes automatic reconnection with exponential backoff, message deduplication for at-least-once delivery guarantees, and sequence numbering that enables clients to detect and recover from missed messages. These reliability features ensure that moderation coverage is continuous even in the face of transient network disruptions.
While real-time processing handles content at the moment of creation, many platforms also need to moderate large volumes of historical content. This need arises when onboarding existing content libraries, retroactively applying new policies, or conducting periodic audits. Our async batch processing endpoint accepts bulk submissions of up to 10 million items per request and processes them through a dedicated high-throughput pipeline that shares GPU infrastructure with the real-time system but operates at lower priority to avoid impacting latency-sensitive workloads.
Batch jobs are distributed across all available edge nodes to maximize throughput. A batch of 10 million items typically completes within 30 minutes, with results delivered incrementally via webhook callbacks or stored in a downloadable results file. Customers can monitor batch progress in real-time through our dashboard and receive notifications when processing is complete. The batch pipeline supports the same moderation models and policy configurations as the real-time API, ensuring consistent results regardless of processing mode.
Caching plays a critical role in reducing both latency and compute costs in content moderation. Our caching strategy operates at three distinct levels, each optimized for different content patterns. The first level is an exact-match cache that uses content hashing to identify previously analyzed identical content. This cache is particularly effective for detecting reposted content, forwarded messages, and duplicated images, returning cached verdicts in under 5ms.
The second level is a semantic similarity cache that identifies content similar enough to a previously analyzed item that the same moderation verdict applies. This cache uses embedding-based similarity matching to find near-duplicates, slightly edited copies, and reformulated text that would receive the same classification. The semantic cache dramatically reduces processing load for content that is shared widely with minor modifications, such as memes with different captions or news articles quoted with personal commentary.
The third level is a predictive pre-computation cache that anticipates likely content patterns based on current trends and pre-populates verdicts for expected content variations. During a trending event, the system pre-computes moderation results for likely content permutations, enabling instant responses when those items are actually submitted. This predictive approach reduces average latency during high-traffic events by up to 60%.
A comprehensive breakdown of the capabilities that power our sub-50ms real-time content moderation processing engine.
Submit content via POST request and receive a moderation verdict in the same HTTP response. Average round-trip time of 35ms including network transit. Supports text, image URLs, base64 payloads, and multipart uploads.
Persistent bidirectional connections for real-time content streams. Sub-5ms framing overhead per message, multiplexed streams, automatic reconnection, and backpressure flow control for high-volume use cases.
Bulk processing of up to 10 million items per request. Results delivered via webhooks or downloadable files. Dedicated throughput pipeline with progress monitoring and incremental result delivery.
Intelligent rate limiting that adjusts to your traffic patterns. Burst allowances accommodate sudden traffic spikes, with token bucket algorithms providing smooth throughput during sustained high-volume periods.
Exact-match hashing, semantic similarity matching, and predictive pre-computation caches work together to return results in under 5ms for previously seen or similar content across your entire traffic volume.
Automatic fault isolation prevents cascade failures. If any processing service experiences issues, the circuit breaker routes traffic to healthy alternatives while maintaining overall system availability above 99.99%.
Common questions about our real-time content moderation processing capabilities.
See sub-50ms content moderation in action. Try our free demo or explore the API documentation to get started.