Why We Built BatchIn: AI Inference Without Filters

Most inference platforms optimize for safety policy, not developer control. That works for some customers, but it leaves a gap for teams building research tools, uncensored assistants, workflow agents, creative products, and compliance-sensitive systems that need auditable output paths.

We built BatchIn around a different thesis. The API should stay OpenAI-compatible, pricing should stay aggressive, and the platform should make operator boundaries obvious. Routing, billing, batch scheduling, and audit trails are our layer. Hidden prompt rewriting and silent content filtering are not.

That philosophy shapes the product. We expose batch-first pricing, USDC payments, verifiable Ed25519 audit records, and GPU leasing with SSH root access. If a team wants to migrate from a more constrained hosted vendor, the switch should be one base_url change, not a full rewrite.

We are still early, but the direction is now clear: make BatchIn the most developer-respectful inference platform on the market. Lower cost, fewer hidden constraints, better operator tools, and a public product surface that tells the truth about what the system actually does.