> For the complete documentation index, see [llms.txt](https://cluster-protocol.gitbook.io/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cluster-protocol.gitbook.io/whitepaper/core-infrastructure/overview.md).

# Overview

The Cluster Protocol ecosystem is a meticulously designed network, crafted by a team of experts with diverse backgrounds in AI, blockchain, and high-performance computing.

Here is quick overview to how does it look like infra wise.

<figure><img src="/files/fnvPFnGndi1YlHxxigyO" alt=""><figcaption></figcaption></figure>

The Cluster Protocol stack is organized into four horizontal layers, each independently functional and collectively composable. Every layer feeds into the ones below it, and all activity settles through a single on-chain settlement layer on Base.

**Inference Engine** The topmost layer and the primary service interface. Handles all AI model requests across the platform.

* 500+ open-source models accessible via a single OpenAI-compatible API
* Multi-modal coverage: chat completions, embeddings, image generation, text-to-speech, speech-to-text, and document reranking
* Multi-provider routing with automatic failover — if one provider drops, the gateway reroutes transparently
* Pay-per-token billing with no subscriptions, no minimums, no lock-in

**Tokenized Data Marketplace + AI Services** The middle layer, split into two interconnected components that feed into each other.

Tokenized Data Marketplace:

* Datasets stored on IPFS for decentralized persistence
* Ownership represented as ERC-721 NFTs on Base
* Revenue automatically distributed on every purchase via PaymentRouter smart contract
* Data quality maintained through community reviews, ratings, and access-gated previews

AI Services:

* Fine-tuning pipeline: bring a base model and training data, Cluster handles compute and outputs a live inference endpoint
* Pre-configured templates and multi-step workflows for common patterns (RAG pipelines, classification, reasoning chains)
* Direct feed from the data marketplace — tokenized datasets can be used as fine-tuning inputs without leaving the platform

The data marketplace feeds the AI services layer. A dataset tokenized on Cluster can be used to fine-tune a model on Cluster, and the resulting model is served through the inference engine above — creating a closed-loop value chain within one platform.

**Settlement Layer — Base (Ethereum L2)** The on-chain backbone that handles all payments, ownership, and contract logic.

* Smart contracts deployed and verified on Base mainnet: DatasetNFT, DatasetRegistry, PaymentRouter, ClusterToken
* x402 payment protocol enabling HTTP-native micropayments — AI agents and developers pay per request with no accounts or API keys
* Balance system for traditional deposit-and-spend users
* Python SDK providing unified access to inference, data, and on-chain queries in a single client

**CodeXero — Application Layer** The consumer-facing surface built natively on top of all three infrastructure layers.

* Prompt-to-dApp: natural language input becomes a compiled, deployed, tokenized on-chain application
* Browser-native runtime via WebContainer — no local setup, no CLI, no dependencies
* Every deployment consumes Cluster inference for code generation, Cluster compute for compilation, and Cluster hosting for delivery — all settled at the infrastructure layer
* Outputs: live dApps, autonomous agents, and protocol integrations — all running on Cluster infrastructure underneath