This project is scheduled for launch
Launch date: Monday, January 25, 2027 at 08:00 AM UTC
Scheduled
packet.ai is an LLM inference and GPU cloud platform built by hosted.ai. It delivers top-tier GPU performance on NVIDIA B200 (bare metal), A100, L40S, RTX Pro 6000, and RTX 4090 at up to 99% less than typical providers. Same models. Same API. Dramatically lower cost.
At its core is Token Factory, packet.ai's LLM inference layer that's fully OpenAI-compatible. Drop it in as a replacement for OpenAI, Anthropic, or any major inference provider without changing your code. Sub-second latency for interactive apps. Batch throughput for high-volume workloads.
Key Features:
- Up to 99% cheaper than OpenAI for the same LLM output
- OpenAI SDK compatible, zero migration friction
- NVIDIA B200 bare metal, A100, L40S, RTX Pro 6000, RTX 4090 GPU access
- Dynamic and Dedicated GPU plans with monthly subscriptions
- Sub-second latency for real-time applications
- 99.9% SLA, 24/7 human support
- No credit card required to start, 10K free tokens
- No contracts on inference, cancel any time
Use Cases:
For AI startups and developers running LLM inference at scale, packet.ai eliminates the biggest cost: the compute bill. Whether you're building a RAG pipeline, an AI agent, a chat interface, or a batch processing workflow, you get the same NVIDIA silicon at a fraction of what you'd pay elsewhere. For teams doing GPU-intensive training or fine-tuning, choose between Dynamic (scheduler-enforced pool access) or Dedicated (reserved GPU) monthly plans.
Pricing:
You can begin by signing up for free, no credit card required. GPU compute available on monthly Dynamic and Dedicated subscription plans. ~30-35% lesser than hyperscalers
Why packet.ai:
Most platforms claim GPU sharing but deliver one of two things: hard partitioning via NVIDIA MIG (fixed slices, rigid, requires a reboot to resize) or best-effort sharing via NVIDIA MPS (concurrent but no real memory isolation between tenants). packet.ai uses a third approach: scheduler-enforced dynamic sharing across a pool of GPUs. Your container gets zero direct GPU access. Every call is proxied, so isolation and VRAM limits are enforced in software, at any size, across a whole pool of GPUs instead of one card at a time. A pool, not a partition.
a hosted·ai project.
Comments
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Publisher
D V Jayanth
Launch Date2027-01-25
Platformweb
Pricingpaid
Tech Stack
#NVIDIA CUDA#Python#OpenAI SDK#Kubernetes#REST API
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