Serve any LLM to millions of users at sub-10 ms TTFT.
Getting a model to work in a notebook is easy. Keeping it fast, cheap, and available when 50,000 users hit it simultaneously is hard. CogniCloud's inference stack combines continuous batching, KV-cache reuse, speculative decoding, and a global edge network so your users always get the first token in under 10 milliseconds.
<10ms
Global p99 time-to-first-token
1,847
Tokens per second per GPU
94%
Average cache-hit ratio
<2 s
Cold start from scale-to-zero
The Challenge
Naive inference deployments fail at scale: GPUs idle between requests, cold starts take seconds, costs explode with traffic, and a single regional outage takes down your product. Production inference needs an entirely different architecture.
How CogniCloud helps
vLLM-powered continuous batching processes tokens from multiple concurrent requests in a single forward pass. GPU utilisation stays above 90% even with bursty, unpredictable traffic.
System prompts, RAG context, and few-shot examples are computed once and cached. Subsequent requests with identical prefixes skip the computation entirely — up to 80% cost reduction.
Anycast BGP routes each request to the nearest GPU cluster across globally distributed points of presence. Users globally get single-digit millisecond TTFT.
Deployments with no traffic cost nothing. Sub-2-second cold starts mean the first request after an idle period still meets most real-world latency budgets.
A small draft model predicts multiple tokens ahead; the main model verifies them in parallel. Reduce median TTFT by up to 40% with no measurable quality loss.
Serve hundreds of LoRA adapters on a single deployment. The runtime hot-swaps the adapter per request, sharing base model weights across all adapters to maximise GPU utilisation.
How it works
Point the CLI at any Hugging Face model ID or private weights URI. CogniCloud builds the optimised serving container and deploys to the nearest GPU cluster.
$ cogni deploy \
--model meta-llama/Llama-3-70B-Instruct \
--hardware high-perf \
--regions auto
✓ Building vLLM serving container
✓ Loading weights to Neural Cache
✓ Endpoint ready in 14s
https://api.cognicloud.net/v1/chatOne line change in your existing code. The API accepts the same request shape as OpenAI, so migration is a URL swap.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cognicloud.net/v1",
apiKey: process.env.COGNI_API_KEY,
});
const stream = await client.chat.completions.create({
model: "meta-llama/Llama-3-70B-Instruct",
stream: true,
messages: [{ role: "user", content: prompt }],
});The dashboard shows live request rates, TTFT percentiles, cache-hit ratios, and per-token cost. Set autoscale policies once and never touch them again.
# Real-time metrics
p50 TTFT: 4.1ms
p99 TTFT: 8.7ms
Cache hit: 94.2%
Throughput: 1,847 tok/s
Cost/1M tok: $0.42
Active GPUs: 12 (auto)Built on
Core serving engine with continuous batching and speculative decoding
KV-cache reuse that slashes cost on repeated prefixes
Global edge PoPs for single-digit ms TTFT
High-performance inference instances with NVLink for large model serving
LLM Fine-Tuning
Adapt foundation models to your domain — faster and cheaper.
RAG Pipelines
Ground your LLMs in real knowledge at billion-document scale.
AI for Startups
Move fast, iterate daily — without a dedicated MLOps team.
Enterprise AI
Secure, compliant, and governed AI infrastructure at any scale.
Batch & Offline AI
Process millions of records overnight — at the lowest cost per token.
CogniCloud is in active development. Join the waitlist to get early access and stay updated on our roadmap. No pricing yet — we'll work with each team to find the right fit.
No spam. No pricing pitches. We reach out personally to discuss your use case.