Artificial intelligence has entered its gold rush period, and major large language models (LLMs) are developing at a breakneck pace. LLMs largely follow the same developmental playbook. The more data they collect, the more prompts and uploaded documents are indexed, the more the model improves.
That also means that user data is being fed into models like a corporate training pipeline. For anyone working with sensitive information, or anyone who simply values digital sovereignty, the options are frustratingly limited. You either trust a cloud service with your data or spend thousands on the hardware needed to run the models locally.
Venice AI claims to promise a third way. Founded by the former CEO of the crypto platform ShapeShift, Erik Voorhees' vision for the future of LLMs seems to be one grounded in user privacy and decentralization. Here's everything you need to know about Venice.ai.
What is Venice AI?
An LLM with a significant privacy focus
Venice AI operates on a philosophy that's a refreshing departure from the mainstream LLM services, stating, "You don't have to protect what you do not have" on the subject of data processing. Whereas OpenAI, Google and Anthropic promise to safeguard data behind firewalls, Venice simply refuses to store it.
This is because the user's conversation history on the platform isn't stored on a server, and instead lives on their browser's local storage. If the browser cache is cleared or if the user switches their device without manually exporting the logs, the data is lost entirely. In regard to user's personal information and conversational data, Venice claims no information besides their email (used at the time of signing up) and IP address is collected, and neither of the two data points are shared with their servers. It is possible to use the service without signing up and while using a VPN.
The user also takes ownership of any output generated by the model. According to its terms of service, Venice explicitly assigns any rights it might have in the content back to the user.
I don't pay for ChatGPT, Perplexity, Gemini, or Claude – I stick to my self-hosted LLMs instead
There's no point in relying on AI tools when my local LLMs can handle everything
How does Venice AI work?
The "Zero-knowledge" architecture is its USP
When a prompt is sent, the user's prompt is encrypted and relayed through a Venice-controlled proxy to a decentralized network of GPUs. This is where its defined "zero knowledge" philosophy takes its technical form. The proxy's primary job is to strip away any metadata that can be used to identify a user or trace back any requests before the request reaches the hardware. Functionally, it's quite reminiscent of how the TOR network operates.
To ensure privacy in between the relay, Venice utilizes a transient memory model once the response is streamed back to the browser. Once a response is received, the data is immediately purged from the systems used for the purpose of inference; and because the inference takes place on distributed hardware, the user's identity is effectively separated from the query itself. Data is never written to a disk or used for training and monitoring. The service claims to not know what the user asked because a record simply wasn't created.
The models are completely unrestricted
AI without the guardrails sponsored by Big Tech
A key driver behind the migration to local LLMs is users' desire for a clean experience, wherein they are free from guardrails, censorship and friction that are associated with using a service from large corporations in business. While mainstream models are tuned to avoid controversial subjects, Venice AI takes a different position by offering a platform that remains neutral. It has poised itself as a truly permission-less utility.
The service relies on open-source models like Llama 3 and Flux. Unlike other centralized services with stringent protocols, Venice provides their models in their raw and transparent state, which means it effectively eliminates the friction that comes with refusal to respond to a certain request.
There are, of course, limitations to this. Venice notes that while the platform itself doesn't inject its own set of protocols or biases onto the models that are hosted, each model still carries the inherent rules and boundaries set by the original publisher.
Venice doesn't exactly seek to compete with or replace local AI, but rather to provide an alternative to the increasingly invasive cloud-based services.
Is Venice AI more secure than using AI locally?
If you were wondering the same, the answer is certainly no. If you have the hardware to run a model entirely offline, you achieve a level of air-gapped digital sovereignty that no hosted service can replicate. There are no proxies, metadata transmission, or reliance on a third party's "zero-knowledge" promises involved.
It is important to note though, that Venice doesn't exactly seek to compete with or replace local AI, but rather to provide an alternative to the increasingly invasive cloud. While it doesn't match the level of control you would get with a local setup, it does represent a significant leap towards decentralizing AI. For the average user, the trade-offs are rather evident, and the service provides a satisfactory level of transparency in its terms of service to manage user expectations surrounding it. Users are promised the convenience of the hardware without the investment that comes with it, and alongside it, an architecture that overwhelmingly favors the user.