The problem arb402 solves

1. Overpaying for AI Inference
   The Problem:
   SMEs, indie devs, and early teams pay far more than they consume.
   They lack optimization expertise (batching, scaling, quantization, cold-start fixes), making small-scale inference extremely expensive.

The Solution:
We provide an optimized inference layer that performs all cost-reduction techniques behind the scenes.
Users get much cheaper inference without needing infra knowledge, and only pay for what they use.

2. Subscription-Based Model Limitations
   The Problem:
   Users are forced into monthly subscriptions even with low usage.
   They pay for idle compute, with no real pay-per-use option.

The Solution:
We offer true per-request billing.
Every request is individually priced, with no monthly commitments or unused credits.
Perfect for SMEs with unpredictable or low-volume workloads.

3. Centralized Payment & API Key Management
   The Problem:

Traditional AI platforms rely on:
API keys
centralized identity
centralized payment processing

This exposes sensitive information and forces users to trust a closed system.

The Solution
We use wallet-based authentication — no API keys needed.
Payments are transparent, verifiable, and on-chain, removing the need to trust any centralized provider.

4. Gas Cost Barrier for Micro-Payments
   The Problem:
   Micro-payments (<$0.001 per request) are impossible on most chains because gas fees are higher than the inference itself.

The Solution:
We implement gasless micro-payments by batching and settling on-chain efficiently.
Users pay near-zero gas, enabling real micro-transactions for each model request.

5. Lack of a Decentralized AI Marketplace

The Problem:
There is no open marketplace where:
model providers can list models
users can pay per request
usage is trustlessly tracked
settlements happen automatically

The Solution:
We provide a decentralized marketplace for AI models.
Users can select any model (LLMs, embeddings, vision models).
Providers earn directly and transparently.
All usage and payments are recorded on-chain.

6. Opaque Pricing Models
   The Problem:
   Today’s pricing is unclear:
   users can’t verify their usage
   prices don’t correlate with compute
   billing is non-transparent

The Solution:
We offer transparent, auditable pricing:
Every request is logged immutably, linking:
tokens used → cost → on-chain settlement.

Challenges we ran into

1. Making Pay-Per-Request Efficient

The biggest challenge was ensuring that each inference call is cheap, predictable, and doesn’t create unnecessary on-chain overhead.
How I approached it:
Researched different metering patterns, measured gas usage per model type, and optimizing the flow to minimize writes and reduce the cost per request.