Lakera vs LangSmith

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Lakera Evaluation	LangSmith Evaluation
Tagline	Runtime security and guardrails for GenAI apps, agents, and RAG systems.	LangChain's eval + observability platform.
Category	Evaluation	Evaluation
Pricing	Freemium· Free community/developer tier at platform.lakera.ai; paid Enterprise plans (custom pricing, contact sales). No public price list.	Freemium· Free starter; Plus $39/mo per seat
Model	Proprietary in-house classifiers; model-agnostic (works in front of GPT-4o, Claude, Gemini, Llama, and custom LLMs)	Platform (any LLM)
Editorial score	—	8.7 / 10
Use cases	Prompt injection defense for chatbotsRAG guardrails against indirect injectionAgent tool-call abuse monitoringPII and secret leakage preventionJailbreak and policy-violation blockingMultilingual content moderation for LLM appsShadow AI discovery inside enterprisesGenAI gateway policy enforcementLLM red-teaming and adversarial evaluationCompliance auditing of LLM traffic	LLM tracingevalsLangChain integration
Pros	Purpose-built for GenAI threats — prompt injection, jailbreaks, PII leakage, and indirect-injection in RAG contexts Very low added latency (sub-50ms) makes it viable inline in front of production chat and agent traffic Model-agnostic and multi-lingual (100+ languages), so it fits mixed OpenAI/Anthropic/open-source stacks Detectors are hardened by data from Gandalf, a large-scale adversarial red-team game with millions of attack prompts Central policy management and Shadow AI discovery give security teams governance beyond just runtime blocking API-first with SDKs and framework integrations, so it drops into existing LangChain / gateway architectures Backed by Check Point post-acquisition, which reassures enterprise procurement and compliance reviewers	Tight LangChain integration Strong tracing UX Mature dataset/eval flows Reasonable per-seat pricing
Cons	Pricing is not public — Enterprise plans are quote-only, which slows evaluation for smaller teams Closed-source, so you cannot self-host the detectors or fully audit their logic Adds an external network hop for every LLM call unless you deploy a regional/edge instance Overlaps with newer guardrail options (NVIDIA NeMo Guardrails, Protect AI, Guardrails AI) that may be cheaper or OSS Effectiveness against novel jailbreaks depends on Lakera's detector update cadence, which is a vendor black box Overkill for hobby projects or prototypes that don't yet handle sensitive data or untrusted inputs	Best value if you're on LangChain UI can feel dense
Website	www.lakera.ai	www.langchain.com

Pick Lakera if

✅ Purpose-built for GenAI threats — prompt injection, jailbreaks, PII leakage, and indirect-injection in RAG contexts
✅ Very low added latency (sub-50ms) makes it viable inline in front of production chat and agent traffic
✅ Model-agnostic and multi-lingual (100+ languages), so it fits mixed OpenAI/Anthropic/open-source stacks
✅ Detectors are hardened by data from Gandalf, a large-scale adversarial red-team game with millions of attack prompts

Pick LangSmith if

✅ Tight LangChain integration
✅ Strong tracing UX
✅ Mature dataset/eval flows
✅ Reasonable per-seat pricing

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