No restriction AI: unrestricted models with your restriction layer
No restriction AI does not mean no governance. It means unrestricted model behavior with your own restriction layer: policy-as-code, quotas, and audit logs.
No restriction AI sounds like chaos, but professional teams mean something specific: no blanket provider restrictions on what the model may discuss.
The alternative to provider restrictions is not anarchy. It is a restriction layer you own, version, and audit.
abliteration.ai pairs a reduced-refusal model with policy-as-code, per-project quotas, and audit logs so restrictions live where they belong: with you.
No restriction AI: unrestricted models with your restriction layer
No restriction AI is model access without blanket provider-imposed usage restrictions, paired with customer-owned policy, quota, and audit controls.
- Provider restrictions change without notice, breaking production behavior and eval comparability.
- Blanket restrictions cannot express your context: authorized security work, internal research, or regulated-industry rules.
- Owning the restriction layer makes behavior explicit, reviewable, and consistent across sessions.
- 01Serve unrestricted model behavior through the OpenAI-compatible endpoint with model: abliterated-model.
- 02Encode your restrictions as policy-as-code with allow, rewrite, redact, escalate, and refuse outcomes.
- 03Apply per-project keys and quotas so each workload carries its own limits.
- 04Audit every governed decision for compliance and incident review.
curl https://api.abliteration.ai/v1/responses \
-H "Authorization: Bearer $ABLIT_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "abliterated-model",
"input": "List common prompt-injection techniques our gateway policy should detect, with a short example of each."
}'Run unrestricted models under your own restrictions
Create a key, then layer policy-as-code on top with Policy Gateway when you need it.
Get an API keyNo restrictions vs no governance
- The model answers without blanket provider restrictions; the delivery path is still governed.
- Restrictions are expressed as code, reviewed and tested like any other change.
- Shadow and canary rollouts let you tighten policy without breaking production.
- Audits and quotas replace vague, shifting provider policies.
Restriction layers teams typically add
- Content-category rules per product surface or customer segment.
- Rate and spend quotas per project, key, or end user.
- Escalation to human review for edge cases.
- SIEM-exported audit logs for compliance and forensics.
Frequently asked questions.
Is no restriction AI the same as no governance?
No. The model behaves without blanket provider restrictions, while your organization supplies governance through policy-as-code, quotas, and audit logs.
What replaces the provider's restrictions?
Your own restriction layer: application rules, Policy Gateway policies, per-project quotas, and human escalation paths where required.
Can restrictions differ per project?
Yes. Project-scoped keys and per-project policies let a red-team workload and a customer-facing workload follow different rules on the same account.
Who typically needs no restriction AI?
Cybersecurity teams, AI red teams, trust and safety groups training classifiers, ML researchers, and defense or government contractors with authorized workloads.
Does zero prompt retention still apply?
Yes. Zero prompt retention is the default regardless of how permissive or strict your policy layer is.