abliteration.ai

What is abliteration.ai?

abliteration.ai is an inference API that delivers unrestricted, uncensored access to open-weight large language models. Instead of relying on prompt-based jailbreaks, the platform uses refusal vector ablation—a weight-modification technique that removes the refusal direction from models at the weight level. Developers can drop in by simply changing their SDK base URL to https://api.abliteration.ai/v1, keeping the same code while getting fewer refusals. The service includes an optional Policy Gateway for teams needing governance controls like policy-as-code rules, quotas, audit logs, and custom enforcement modes. By default, abliteration.ai practices zero data retention: prompts are never stored, outputs are never logged, and no metadata is kept.

Key Features

• Unrestricted Open-Weight Model Access

  Hosted abliterated-model that answers prompts other LLMs refuse, including security research, training data generation, and edge-case evals without system-prompt jailbreaks.

• Drop-in SDK Compatibility

  Fully compatible with OpenAI Chat Completions API, OpenAI Responses API, and Anthropic Messages API—change only the base URL, no code rewrites needed.

• Built-in Policy Gateway

  Opt-in governance layer with policy-as-code rules offering five outcomes (allow, refuse, rewrite, redact, escalate) plus reason codes streamed to Splunk, Datadog, Elastic, or S3.

• Zero Data Retention

  Prompts never stored, outputs never logged, metadata never kept, and no training signal used—audit logs contain nothing identifying users, prompts, or responses.

• Multi-Modal Capabilities

  Supports streaming, tool calling, image inputs, and video inputs on Chat Completions, with vision input and function calling available across endpoints.

• Pay-As-You-Go Pricing

  Three subscription tiers (Builder at $20/month, Team at $50/month, Enterprise with custom pricing) plus prepaid credit packs starting at $100 for 33M tokens that never expire.

Use Cases

• Synthetic Data Generation : Generate labeled datasets at scale including preference pairs, eval rows, classifier examples, and edge cases that other LLMs refuse to write, with schema-validated outputs ready for training pipelines.
• Security & Red-Team Research : Security teams can draft PoCs, run recon techniques for authorized engagements, and generate red-team evals without provider refusals blocking legitimate defense work.
• Trust & Safety Testing : Trust teams can generate harmful behavior examples, harassment scenarios, and safety evals to test and improve their own moderation systems.
• Production AI with Custom Governance : Teams in high-risk industries (healthcare, finance, legal) can run AI with their own policy layer, audit logs, and compliance controls instead of inheriting hidden provider refusals.
• Model Training & Eval Development : Researchers can generate training data, create eval cases for edge scenarios, and build classifier examples without refusal theater blocking legitimate research prompts.

FAQs