Guardrails AI: Pricing, Tiers and What's Actually Covered
Python framework for LLM validators (PII, profanity, jailbreak, toxicity, custom). Open-source Hub plus a managed Pro tier with observability and VPC deployment.
Pricing tiers
Verbatim from the Guardrails AI pricing page on 2026-06-19. Quote-only tiers are surfaced as such, never with an inferred price.
| Tier | Price | Free allowance | Notes |
|---|---|---|---|
| Open source (Guardrails Hub) | Free / month | All open-source validators | Self-hosted, infrastructure cost on the buyer |
| Guardrails Pro | Quote only | - | Quote only. Managed hosting, observability, enterprise support, VPC deployment. |
Source: https://www.guardrailsai.com/ | Verified 2026-06-19
OWASP LLM Top 10 coverage
What Guardrails AI defends against, as claimed on its product pages and security documentation. Coverage flags are binary: an item is listed only if the vendor names the attack class explicitly.
- LLM01 Prompt injection
- LLM02 Sensitive information disclosure
- LLM03 Supply chain
- LLM04 Data and model poisoning
- LLM05 Improper output handling
- LLM06 Excessive agency
- LLM07 System prompt leakage
- LLM08 Vector and embedding weaknesses
- LLM09 Misinformation
- LLM10 Unbounded consumption
Hidden costs
Beyond the line-item price, every runtime AI guardrail carries operating costs the vendor page does not surface. Plan for these in any board paper.
- Per-call latency overhead. Guardrail check sits in the request path; budget 50 to 300 ms extra per LLM call.
- False-positive remediation. Allocate engineering time to tune policies after every guardrail rule change.
- Logging and retention. Guardrail decisions need to land in your SIEM. See siemcostcalculator.com.
- On-call coverage. Treat AI security alerts like SOC alerts. See securityoperationscost.com.
What Guardrails AI is best for
Python framework for LLM validators (PII, profanity, jailbreak, toxicity, custom). Open-source Hub plus a managed Pro tier with observability and VPC deployment.