Lakera is strong when the primary problem is detecting unsafe prompts. RAGuard is built for the broader runtime governance problem that emerges when AI systems use tools, memory, workflows, and enterprise data.
Blocks prompt injection, jailbreaks, and unsafe content before the model follows them.
Mediates trusted instructions, tool permissions, memory, and policy decisions once the model starts doing work.
Prompt injection defence, jailbreak detection, and a focused AI firewall story for conversational GenAI deployments.
Runtime governance, MCP and tool mediation, instruction provenance, stateful attack awareness, and evidence for enterprise AI policy enforcement.
| Question | Lakera | RAGuard |
|---|---|---|
| Is the main focus prompt attack detection? | Yes | Included, but not the only focus |
| Does it govern tool execution? | Limited | Yes |
| Does it support MCP-style governance? | Limited | Yes |
| Does it reason over stateful attacks? | Limited | Yes |
| Does it track instruction provenance? | Limited | Yes |
| Does it provide governance evidence? | Limited to moderate | Yes |
| Is it designed as an AI runtime control plane? | No | Yes |
Lakera is a strong choice when the immediate requirement is prompt-level protection. Many GenAI applications need exactly that: a security layer that screens input and output for prompt injection, jailbreaks, unsafe content, and data leakage.
RAGuard addresses a different operational question. Once AI systems retrieve documents, persist context, call tools, or act across workflows, the security problem changes from "is this prompt malicious?" to "should this system be allowed to take this action in this context?"
Your main concern is prompt injection and jailbreak detection, you need a focused AI firewall layer, and your primary deployment pattern is conversational GenAI.
Your AI systems call tools or APIs, you are adopting MCP-enabled workflows, and you need runtime policy enforcement with auditable governance evidence.