Lakera

Lakera Guard catches prompt injection, jailbreaks, PII leakage, and abuse in production LLM apps. The Gandalf game gave them the largest attack dataset in the field. Buy if you're running real LLM workloads in regulated or abuse-prone settings.

Lakera's competitive moat is unusual: their Gandalf AI security education game went viral in 2023, attracted 50M+ attempted attacks across all skill levels, and produced the largest publicly-known dataset of real-world LLM attack patterns. Lakera Guard is essentially a runtime layer that pattern-matches against that corpus, plus PII detection, toxicity classifiers, and configurable policy rules. The result: detection rates that beat homegrown guardrails substantially, especially for novel attacks.

The customer profile is right for the product. Citi, Dropbox, Allianz, and Reka use Lakera for production LLM workloads with elevated abuse risk or regulatory exposure. The deployment is meaningfully easier than building guardrails in-house: SDK or proxy mode, integrates with OpenAI / Anthropic / Bedrock / LangChain in a day. Lakera Red (automated red-teaming) is a separate product that's become useful for pre-launch security testing.

The weakness is overhead for low-risk applications. If your LLM use is internal-only with non-sensitive data, the runtime cost (latency + dollars + complexity) of Lakera Guard exceeds the marginal risk reduction. Buy Lakera for production LLM apps in financial services, healthcare, or government; for AI agents with elevated abuse risk (browser agents, code execution); or for any LLM-facing surface where prompt injection has meaningful blast radius. Skip for internal experimentation or low-stakes outputs.