AI Inference Acceleration and LLM API Delivery
For inference APIs, agent products and AIGC platforms: move the entry point next to your users to cut time to first token, stream SSE responses through without buffering, and stop abusive calls before they burn GPU time.
Get an AI delivery plan- 3,000+Global edge entry points
- Zero-bufferSSE streaming passthrough
- 7T+DDoS mitigation capacity
Four walls every AI product hits after launch
The experience bottleneck is rarely the model itself. It sits in the network and governance layer — before the request reaches the model, and before the tokens reach the user.
Time to first token feels like a hang
A cross-region call spends hundreds of milliseconds on routing and the TLS handshake before inference even starts. Two or three seconds of blank screen after hitting send is all it takes for a conversational product to lose the user.
Streams die halfway — and you still pay
SSE and WebSocket connections are long-lived. Path jitter, proxies that buffer by default and 60-second idle timeouts all cut a half-written answer short. The user sees a failure; the GPU time is already spent.
Abuse turns straight into a compute bill
A key leaked in frontend code, a free-tier endpoint someone found, or a farm of throwaway accounts gets hit hundreds of times a second by a script. By the time the invoice looks wrong, days of inference budget are gone on requests no real user made.
No fallback when an upstream fails
Provider rate limits, regional outages and deprecated model versions all surface as 5xx on your critical path. With no single entry point running health checks and failover, the options are waiting it out or shipping an emergency patch.
Six core capabilities for AI traffic
Entry, acceleration and abuse control run on one network, so an inference request never takes an extra hop just to pass through security.
Local entry, fewer round trips
3,000+ nodes let clients complete TCP and TLS locally, turning an intercontinental connection RTT into a metro one and pulling first-token latency down with it.
SSE and WebSocket passthrough
Response buffering and recompression are disabled on streaming paths, idle timeouts are extended and connections kept alive, so each token is forwarded the moment it is produced.
Multi-upstream routing and failover
Health checks across model vendors and self-hosted clusters shift traffic away from a throttled or timing-out upstream automatically, while fetches ride our private backbone and 300+ PNIs.
Per-key quotas and rate limits
Set QPS, concurrency and daily call ceilings by API key, user ID, path or source IP. Requests over the limit get a 429 at the edge and never consume compute.
Anti-abuse, anti-scraping, leak containment
Scripted callers, throwaway accounts and anomalous fingerprints are identified against a frequency baseline, with challenges where needed — plus 7T+ DDoS scrubbing and CC defence on the same path.
AIGC asset delivery
Generated images, audio and video are cached and served from the edge instead of re-fetching from your generation service, with hotlink protection and signed, expiring URLs.
Four steps to put your AI endpoints on the edge
Onboarding happens at the network layer only — no changes to your inference service or prompt logic, and rollback is a single DNS change.
Point your API hostname
Add the inference entry hostname and set the origin to your own cluster or an upstream model API, with several upstreams grouped by weight and health.
Enable streaming mode
Disable buffering, extend idle timeouts and keep connections alive on chat and generation paths so tokens leave as they are produced.
Set quotas and rules
Define QPS, concurrency and daily ceilings per key, user and path, tighten limits on expensive models, and watchlist anomalous sources.
Canary, then observe
Compare TTFT, stream-drop rate and block counts on a slice of traffic before full cutover; per-key logs keep every call traceable.
Typical results after onboarding
Ranges observed across AI customers; actual results depend on model size, upstream location and how calls are structured.
- 30–50%Lower time to first token
- 20–40%Less compute spent on invalid calls
- 99.99%Inference endpoint availability
- 5 minTime to cut over
AI acceleration and protection FAQ
Split TTFT into three parts: client-to-entry connection and TLS, entry-to-cluster forwarding, and the model’s own prefill. The first two shrink at the edge — clients handshake locally while the long-haul leg reuses warm connections over a private backbone, removing a fresh intercontinental round trip per request. The third improves by shortening system prompts, enabling KV caching and routing easy queries to a smaller model. Doing both moves the first visible character noticeably earlier.
It can under default settings, which shows up as users waiting for the whole generation before seeing anything. We disable response buffering and recompression on chat and generation paths and extend idle and keep-alive timeouts beyond a full generation, so each token is forwarded as it appears. Before go-live, run one long generation and confirm both the first-character timing and the token-by-token cadence.
Stop the bleeding first: block or hard-throttle the leaked key at the edge, then group the scripted traffic by IP range, user agent, body signature and call cadence and block it in bulk. The durable fix is pushing quotas down to the edge — per-key QPS, concurrency and daily ceilings so over-limit calls get a 429 and never reach the cluster. Browsers should never call the model API directly; issue short-lived tokens from your own backend.
Yes. Cross-border slowness usually comes from public-internet detours, inter-network packet loss and a fresh handshake on every call. After onboarding, fetches take a low-jitter path over our private backbone and 300+ PNIs and reuse pooled connections to the upstream, cutting retransmits and handshake overhead. Health checks across upstreams also fail over to a backup region or vendor automatically, with no redeploy.
Choose entry regions by where your users actually are: local nodes for North America and Europe, the same for Southeast Asia and the Middle East, and CN2 direct routes for mainland China. On the data side, separate the inference entry from asset storage by region and retain logs locally so raw conversation content is not shipped across borders unnecessarily. A free 24-hour trial lets you benchmark latency and success rate per region with your own traffic.
Write generated assets to object storage and serve them through the CDN rather than re-fetching from the generation service on every preview. Name assets by content hash and cache them long, so a hit never touches the origin, then add hotlink protection, URL authentication and signed expiring links so other sites cannot spend your bandwidth. Images can also be converted to WebP/AVIF and thumbnailed at the edge so list views load small variants.
Still haven't found what you're looking for? Talk to our team.
Make every inference call fast — and worth paying for
Tell us where your models run, where your users are, and what your current TTFT and abuse levels look like. We will propose an entry, throttling and delivery configuration to match.