Built to handle billions of content moderation requests with consistent sub-50ms response times. Our horizontally auto-scaling infrastructure, global CDN, and edge computing architecture guarantees 99.99% uptime at any traffic volume.
Our infrastructure dynamically provisions and de-provisions compute nodes based on real-time demand, predictive analytics, and traffic patterns. No manual intervention, no capacity planning headaches.
Horizontal auto-scaling is the backbone of our content moderation infrastructure. Unlike vertical scaling that simply adds more power to a single machine, our approach adds or removes entire server instances across a distributed cluster. When traffic spikes occur during viral content events, breaking news cycles, or seasonal surges, new compute nodes spin up within seconds, distributing the workload evenly across the fleet. This architecture means there is no theoretical upper limit to the throughput we can achieve.
Predictive scaling algorithms analyze historical traffic data, seasonal patterns, and real-time signals to pre-provision resources before demand materializes. Machine learning models trained on years of traffic data anticipate load increases with remarkable accuracy, ensuring that capacity is always ahead of demand rather than reacting to it. During low-traffic periods, the system gracefully scales down, terminating idle instances and consolidating workloads to minimize cost without compromising response times or availability guarantees.
Our scaling controller monitors dozens of signals simultaneously: CPU utilization, memory pressure, request queue depth, average latency percentiles, error rates, and custom application-level metrics. Each signal feeds into a multi-objective optimization engine that balances performance, cost, and reliability in real-time. The system can scale from a handful of instances to thousands within minutes, a capability that has been tested and proven during some of the largest content events on the internet.
Content moderation decisions happen at the edge, closer to your users, eliminating round-trip latency and delivering consistent sub-50ms performance worldwide.
Our global content delivery network spans over 50 points of presence across six continents. Each edge location runs a complete moderation inference stack, including optimized AI models for text analysis, image classification, and policy evaluation. When a moderation request arrives, it is routed to the nearest available edge node via Anycast DNS, ensuring the shortest possible network path between the client and our processing infrastructure.
Edge computing transforms content moderation from a centralized bottleneck into a globally distributed, low-latency service. Rather than routing every request to a distant data center, our edge nodes handle the full moderation pipeline locally. This means a user in Tokyo experiences the same sub-50ms response time as a user in Frankfurt or Sao Paulo. Each edge node maintains a synchronized copy of the latest moderation models, policy configurations, and threat intelligence feeds, updated through an efficient binary delta replication protocol that propagates changes across the entire network within minutes.
The CDN layer also provides intelligent request routing and failover capabilities. If an edge node becomes unavailable or experiences elevated latency, requests are automatically redirected to the next closest healthy node with zero client-side configuration changes. Geographic load balancing ensures that no single region becomes a bottleneck, while our proprietary traffic steering engine optimizes routes in real-time based on network conditions, BGP path changes, and observed latency measurements. This multi-layered approach to global distribution means that catastrophic regional failures, undersea cable cuts, and cloud provider outages do not impact our service availability or performance guarantees.
Every layer of our stack is engineered for extreme throughput, fault tolerance, and consistent low-latency performance under unpredictable workloads.
Application-aware load balancers inspect request content, route based on moderation type, and distribute traffic using weighted round-robin, least-connections, and latency-based algorithms to ensure even resource utilization across all backend nodes.
Data is horizontally partitioned across database shards by tenant, content type, and geographic region. Each shard operates independently, providing linear scalability and isolation that prevents any single customer workload from impacting others.
A distributed Redis cluster with over 2TB of in-memory capacity caches moderation results, model outputs, and policy configurations. Cache hit rates exceed 85%, reducing backend load and delivering cached responses in under 5ms.
Hot data, including active policy rules, threat signatures, and rate limit counters, resides in a distributed in-memory data grid that provides microsecond-level access times. This tier eliminates disk I/O from the critical path entirely.
Asynchronous message queues buffer burst traffic, decouple processing stages, and guarantee at-least-once delivery. Priority queues ensure that high-severity content reviews are processed ahead of routine classification requests.
Persistent connection pools to databases, caches, and downstream services eliminate connection establishment overhead. Pool sizes auto-tune based on concurrency levels, preventing connection exhaustion under extreme load conditions.
Architecture Principle: Every component in our stack is stateless and horizontally scalable. State is pushed to purpose-built data stores (Redis for ephemeral state, sharded PostgreSQL for durable state, object storage for binary content). This separation allows each tier to scale independently based on its specific bottleneck, whether that is CPU for inference, memory for caching, or IOPS for persistence.
Our multi-tier load balancing architecture operates at three distinct layers. At the network edge, DNS-based global load balancing directs traffic to the optimal regional cluster. Within each region, Layer 4 TCP load balancers distribute connections across application gateway pools. Finally, Layer 7 HTTP-aware load balancers route individual requests based on content type, tenant priority, and real-time backend health scores. This three-tier approach provides defense in depth against overload scenarios and enables sophisticated traffic management policies.
Health checking is continuous and multi-dimensional. Each backend node reports not just binary up/down status, but also current request queue depth, average response latency, error rate, CPU utilization, and available memory. The load balancer uses these signals to make weighted routing decisions, gradually shifting traffic away from nodes showing early signs of degradation long before they reach failure thresholds. This proactive approach prevents the cascading failures that plague simpler architectures.
Rate limiting is implemented at multiple levels to protect the system and ensure fair resource allocation. Global rate limits prevent any single client from monopolizing shared resources. Per-tenant rate limits enforce contractual throughput guarantees. Per-endpoint rate limits protect computationally expensive operations like video analysis from being overwhelmed. Our token bucket algorithm supports configurable burst allowances, enabling clients to absorb short traffic spikes without throttling while maintaining sustainable throughput over longer time windows.
Our database tier uses a consistent hashing-based sharding strategy that distributes data across dozens of independently managed database instances. Each shard holds a roughly equal slice of the total dataset, and the sharding key is chosen to co-locate related data for efficient query processing. For content moderation, we shard primarily by tenant ID, which means that all data for a given customer resides on a predictable set of shards, enabling efficient tenant-level queries and simplifying data residency compliance.
Shard rebalancing happens online, without downtime, using a dual-write strategy that ensures data consistency during migration. When a new shard is added to accommodate growth, the system identifies the optimal data segments to redistribute, begins dual-writing to both old and new locations, and transparently switches reads once the migration is verified. This process is fully automated and has been executed hundreds of times in production without any service impact.
Our three-tier caching architecture is designed to minimize redundant computation and database queries. The first tier is a CDN-level cache that serves identical moderation results for duplicate content submissions, a surprisingly common pattern since the same viral image or text snippet may be submitted millions of times across different platforms. The second tier is a distributed Redis cluster that caches model inference results, policy evaluations, and user reputation scores with configurable TTLs. The third tier is a process-local in-memory cache on each application server that holds the hottest data with sub-microsecond access times.
Cache invalidation uses a publish-subscribe model where policy changes, model updates, and threat intelligence feeds trigger targeted invalidation events that propagate across the entire cache hierarchy. This event-driven approach ensures freshness without the overhead of periodic polling or aggressive TTL expiration, striking an optimal balance between data accuracy and cache utilization.
End-to-end observability with real-time dashboards, distributed tracing, and automated remediation ensures we consistently deliver on our 99.99% uptime commitment.
Our observability stack ingests over 10 million metrics per minute across every layer of the infrastructure. Custom-built dashboards provide real-time visibility into request latency distributions (p50, p95, p99, and p99.9), error rates by category, throughput per region, cache hit ratios, queue depths, and resource utilization. Automated anomaly detection algorithms continuously analyze these metrics, identifying deviations from expected patterns and triggering alerts before issues impact users.
Distributed tracing follows every moderation request from ingress to response, capturing timing information for each processing stage: authentication, rate limit evaluation, content preprocessing, model inference, policy evaluation, result caching, and response serialization. This end-to-end visibility makes it possible to pinpoint latency bottlenecks with microsecond precision, even in a system processing millions of concurrent requests across dozens of microservices.
Our 99.99% uptime SLA translates to no more than 52.6 minutes of total downtime per year, and our actual measured availability consistently exceeds 99.995%. This is achieved through active-active multi-region deployments, automated failover, self-healing infrastructure that replaces unhealthy instances within seconds, and rigorous chaos engineering practices that continuously validate our resilience. We publish real-time and historical uptime data on our public status page, and SLA credits are automatically applied if we ever fall short of our commitment.
Our published throughput benchmarks are derived from continuous production monitoring across our entire fleet, not synthetic tests. Text content moderation processes at an average of 18ms per request, with p99 latency at 42ms. Image analysis averages 35ms, including model inference and policy evaluation. Combined multi-modal analysis of text-plus-image content completes in an average of 48ms. These numbers hold steady under sustained loads exceeding one million requests per second.
Cold starts are the enemy of consistent performance in auto-scaling systems. We have invested heavily in eliminating cold start penalties through several techniques. First, our inference models are pre-loaded into shared memory segments that new instances can map instantly rather than loading from disk. Second, connection pools are pre-warmed during instance initialization using synthetic traffic that exercises all downstream dependencies. Third, our container images are optimized for fast startup, with layered caching and lazy loading of non-critical components. The result is that a newly scaled instance begins processing production traffic within 8 seconds of launch, compared to the industry average of 30 to 60 seconds.
From viral content explosions to planned product launches, our infrastructure absorbs traffic surges gracefully while maintaining performance guarantees.
Burst traffic is an inherent characteristic of content moderation workloads. A single piece of viral content can trigger millions of moderation requests within minutes as it spreads across platforms. A breaking news event can cause traffic to spike by 20x or more within seconds. A major product launch can generate sustained high-volume traffic for hours. Our infrastructure is engineered to handle all of these scenarios without degradation.
Burst absorption works through a combination of pre-provisioned headroom, instant auto-scaling, and intelligent request buffering. We maintain a baseline capacity that exceeds our average load by 3x at all times, providing immediate absorption capacity for sudden spikes. When a spike exceeds this headroom, auto-scaling provisions additional capacity within seconds. Meanwhile, our queue-based architecture buffers requests during the brief scale-up window, ensuring that no requests are dropped even during the most extreme surges. Priority queuing ensures that latency-sensitive synchronous requests are processed first, while batch and asynchronous operations are gracefully deferred.
Our capacity planning process combines deterministic models with machine learning forecasts. We analyze historical traffic patterns, upcoming events calendars, customer growth projections, and seasonal trends to build a detailed capacity model that projects resource requirements months into the future. This forward-looking approach ensures that our infrastructure fleet, network bandwidth, and storage capacity are always ahead of demand, eliminating the risk of capacity-related outages.
Rate limiting protects both the system and individual tenants from abuse and runaway workloads. Our rate limiting engine operates at multiple granularities: per-API-key, per-tenant, per-endpoint, and per-region. The token bucket algorithm provides a burst allowance that accommodates natural traffic variability while enforcing sustainable throughput limits. When a client approaches their rate limit, our API returns informative headers indicating remaining quota, reset time, and retry-after guidance, enabling clients to implement intelligent backoff strategies.
Beyond simple request counting, our rate limiting considers the computational cost of different request types. A text moderation request consumes fewer resources than a high-resolution image analysis, which in turn requires fewer resources than a video moderation request. Cost-weighted rate limiting ensures fair resource allocation by accounting for these differences, preventing computationally expensive operations from crowding out simpler ones.
Proven at Scale: During a recent global content event that generated 47x normal traffic within a 12-minute window, our infrastructure scaled from 340 instances to 4,200 instances while maintaining p99 latency below 55ms throughout the entire surge. Zero requests were dropped and no SLA violations occurred.
Common questions about our scalability architecture, performance guarantees, and infrastructure capabilities.
Our infrastructure combines three mechanisms to absorb sudden traffic spikes. First, we maintain a permanent 3x capacity headroom above average load, providing instant absorption for most spikes. Second, horizontal auto-scaling provisions new compute instances within 8 seconds when utilization exceeds configurable thresholds. Third, asynchronous message queues buffer incoming requests during the brief scale-up window, processing them in priority order as capacity becomes available. This layered approach has been tested in production with spikes exceeding 47x normal traffic without any request drops or SLA violations. Additionally, predictive scaling uses machine learning models to pre-provision resources before anticipated traffic increases based on historical patterns, events calendars, and real-time trend analysis.
Our 99.99% uptime SLA allows for a maximum of 52.6 minutes of total downtime per year, measured on a rolling basis across all API endpoints. In practice, our measured availability consistently exceeds 99.995%, which translates to less than 26 minutes of cumulative downtime annually. This is achieved through active-active multi-region deployments where traffic automatically routes around any regional failure, self-healing infrastructure that replaces unhealthy instances in seconds, and zero-downtime deployment procedures. If we fail to meet the SLA for any billing period, service credits are automatically applied to your account without requiring a support ticket. We publish real-time and historical availability data on our public status page for full transparency.
Sub-50ms global response times are the result of multiple optimization layers working together. Edge computing brings the full moderation inference stack to over 50 points of presence worldwide, eliminating long-haul network latency. Optimized AI models use quantization, pruning, and knowledge distillation to reduce inference time without sacrificing accuracy. A multi-tier caching architecture, including CDN caches, distributed Redis clusters, and process-local in-memory caches, serves over 85% of requests from cache in under 5ms. Connection pooling eliminates TCP and TLS handshake overhead for backend communications. Cold start optimization ensures new instances serve production traffic within 8 seconds. Together, these techniques deliver average text moderation latency of 18ms and image analysis latency of 35ms, with p99 latency consistently below 50ms regardless of client location.
Absolutely. Our microservices architecture separates processing pipelines by content type, allowing each to scale independently based on its specific resource demands. Text moderation runs on CPU-optimized instances, image analysis uses GPU-accelerated nodes, and video processing leverages specialized inference hardware. Cost-weighted rate limiting ensures fair resource allocation by accounting for the differing computational costs of each content type. Queue-based routing directs requests to the appropriate specialized processing cluster, and priority scheduling ensures that latency-sensitive synchronous requests are handled before batch operations. This architecture means you can submit a mix of text, image, and video content through a single API, and each request is processed on infrastructure optimized for its specific workload.
Enterprise customers have access to comprehensive performance dashboards showing real-time and historical metrics for their moderation workloads. These dashboards display request latency distributions (p50, p95, p99), throughput by content type, cache hit rates, error breakdowns, and usage against rate limits. Our API returns detailed timing headers with every response, enabling client-side observability and integration with your own monitoring systems. For capacity planning, we provide monthly usage reports with trend analysis, growth projections, and recommendations for optimizing your integration patterns. Dedicated technical account managers work with enterprise customers to review performance data, identify optimization opportunities, and plan for anticipated traffic events. Custom alerting rules can trigger notifications when usage approaches plan limits or when latency exceeds configurable thresholds.
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