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Average Ratings 0 Ratings

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ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

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Description

Confidential AI offers a secure platform for private AI inference, agent environments, and confidential computing solutions tailored for AI laboratories, inference service providers, routers, and organizations that manage sensitive data. Its hosted inference service operates open-weight models within hardware-backed trusted execution environments (TEEs), utilizing technologies such as Intel TDX and AMD SEV-SNP confidential virtual machines, paired with NVIDIA GPUs in a confidential computing configuration. The system ensures that prompts, responses, and workload information remain safeguarded from infrastructure operators, allowing clients to authenticate attestation prior to submitting sensitive information. Users have the flexibility to implement an OpenAI-compatible API to establish inference endpoints and agent workloads, or alternatively, they can acquire the confidential-computing stack for deployment on their own premises, bare metal servers, or across major cloud platforms. This service is particularly beneficial for scenarios that involve serving models while restricting operator access to prompts and model assets, as well as executing sensitive AI workloads in a dedicated confidential computing environment. Additionally, the emphasis on robust security measures fosters trust in the processing of sensitive data, enabling clients to focus on innovation without compromising privacy.

Description

vLLM is an advanced library tailored for the efficient inference and deployment of Large Language Models (LLMs). Initially created at the Sky Computing Lab at UC Berkeley, it has grown into a collaborative initiative enriched by contributions from both academic and industry sectors. The library excels in providing exceptional serving throughput by effectively handling attention key and value memory through its innovative PagedAttention mechanism. It accommodates continuous batching of incoming requests and employs optimized CUDA kernels, integrating technologies like FlashAttention and FlashInfer to significantly improve the speed of model execution. Furthermore, vLLM supports various quantization methods, including GPTQ, AWQ, INT4, INT8, and FP8, and incorporates speculative decoding features. Users enjoy a seamless experience by integrating easily with popular Hugging Face models and benefit from a variety of decoding algorithms, such as parallel sampling and beam search. Additionally, vLLM is designed to be compatible with a wide range of hardware, including NVIDIA GPUs, AMD CPUs and GPUs, and Intel CPUs, ensuring flexibility and accessibility for developers across different platforms. This broad compatibility makes vLLM a versatile choice for those looking to implement LLMs efficiently in diverse environments.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

No images available

Screenshots View All

Integrations

OpenAI Yes 
Compute with Hivenet No 
DashboardGPT No 
Docker No 
DocsGPT No 
Hugging Face No 
KServe No 
Kubernetes No 
LFM3 No 
ModelLane No 
Muse Spark Open No 
NGINX No 
NVIDIA DRIVE No 
Nemotron 3.5 Bolt No 
Nemotron 3.5 Super VL No 
Nemotron 3.5 Transcribe No 
PyTorch No 
Recursive Flint No 
Thunder Compute No 
omp No 

Integrations

OpenAI Yes 
Compute with Hivenet Yes 
DashboardGPT Yes 
Docker Yes 
DocsGPT Yes 
Hugging Face Yes 
KServe Yes 
Kubernetes Yes 
LFM3 Yes 
ModelLane Yes 
Muse Spark Open Yes 
NGINX Yes 
NVIDIA DRIVE Yes 
Nemotron 3.5 Bolt Yes 
Nemotron 3.5 Super VL Yes 
Nemotron 3.5 Transcribe Yes 
PyTorch Yes 
Recursive Flint Yes 
Thunder Compute Yes 
omp Yes 

Pricing Details

$0.30 per 1M input tokens
Confidential Inference (OpenAI-compatible API, per 1M tokens):
DeepSeek V4.1 Flash (live): $0.30 input, $0.03 cached input, $1.20 output.
Kimi K3 (on request): $3.00 input, $0.30 cached input, $15.00 output.
GLM 5.3 (on request): $1.40 input, $0.26 cached input, $4.40 output.
Qwen3.8 27B (on request): $0.30 input, $0.03 cached input, $1.80 output.
DeepSeek V4 Flash is no longer offered; DeepSeek V4.1 Flash is the current live model.

Confidential GPU VMs (per GPU-hour, confidential computing mode):
NVIDIA B300: $7.50 (available).
NVIDIA B200: $6.50 (available).
NVIDIA H100: $3.25 (on request).
CPU VMs: $0.05 per core-hour plus $0.012 per GB-hour (AMD SEV-SNP and Intel TDX).

Licensed stack (per GPU per year): RTX PRO 6000 $2,789; H100 $3,876; H200 $4,974; B200 $7,625; B300 $9,884. CPU core license $48.85 per core per year. Standard support included; Enterprise support 25% of total license price.

Terms: minimum order $500. 50% on signed order, 50% on completion; monthly in arrears for longer engagements. USD by bank transfer (ACH or international wire). Card payments coming soon.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Confidential AI

Founded

2025

Country

United States

Website

confidential.ai

Vendor Details

Company Name

vLLM

Country

United States

Website

vllm.ai

Product Features

Virtual Machine

Backup Management No 
Graphical User Interface No 
Remote Control No 
VDI No 
Virtual Machine Encryption No 
Virtual Machine Migration No 
Virtual Machine Monitoring No 
Virtual Server No 

Product Features

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