7 Best Lakera Guard Alternatives for AI Agent Security in 2026

Enterprise pricing after the Check Point acquisition

Since Check Point completed the ~$187M acquisition in October 2025, Lakera Guard has been folded into Check Point's enterprise AI security platform with enterprise-only pricing. The original self-serve tier ($0 to start, pay-per-call) is gone. Teams under 50 engineers now face multi-month procurement cycles and five-figure annual commitments for what used to be a simple API key.

External API dependency adds 50-200ms per call

Every scan requires a round-trip to Lakera's cloud API. Measured median latency is ~80ms from US-East, ~150ms from EU, and 200ms+ from APAC. For a ReAct agent making 8-12 tool calls per session, that's 0.6-2.4 seconds of added latency per conversation turn — noticeable in interactive UX.

Text-level classification only — no agent awareness

Lakera Guard classifies raw text strings as safe/unsafe. It has no concept of tool calls, function arguments, inter-agent delegation, or multi-step workflows. When an attacker injects instructions through a tool's return value (indirect injection), Lakera can't see the tool context to distinguish legitimate data from attack payloads.

Data leaves your infrastructure on every call

Full prompt and response text is sent to Lakera's API for classification. For teams handling PII, financial data, or health records, this creates a third-party data processing relationship that requires DPAs, privacy impact assessments, and potentially conflicts with data residency requirements (GDPR Art. 44, HIPAA BAAs).

No data exfiltration or secret detection

Lakera Guard focuses on prompt injection classification. It doesn't scan for data exfiltration patterns (e.g., an agent encoding sensitive data into a URL parameter), leaked API keys, database connection strings, or PII appearing in model outputs. These are distinct threat categories that require purpose-built scanners.

No real-time dashboard on lower tiers

The Lakera dashboard with alerting, event history, and analytics is gated behind enterprise contracts. Teams on smaller plans get API responses (safe/unsafe) with no visibility into attack patterns, false positive rates, or scanner performance over time.

Single-point detection model

Lakera runs a single ML classifier per request. While their Gandalf-trained model is strong for known injection patterns, it's a single layer. Novel attacks that evade the classifier have no fallback — there's no secondary vector check or LLM judge to catch what the primary model misses.

No custom policy engine

You can't define organization-specific rules like 'block any tool call to the payments API from an agent running a user-supplied prompt' or 'flag when an agent attempts to access more than 3 database tables in one session.' Lakera's policies are limited to their built-in threat categories.

How effectively the tool catches prompt injection, data exfiltration, and other LLM attacks without excessive false positives.

The overhead added to each LLM call. For interactive agents, anything above 50ms per scan is noticeable.

Native support for agent frameworks like LangChain, CrewAI, and MCP — not just raw text APIs.

Whether raw prompts and responses leave your infrastructure during scanning.

Self-serve pricing vs. enterprise-only sales motions. Important for startups and small teams.

Open-source toolkit from NVIDIA for adding programmable guardrails to LLM applications using Colang, a custom modeling language.

Python framework for validating LLM outputs with a library of pre-built validators for format, toxicity, and factuality checking.

Open-source toolkit for sanitizing and validating LLM inputs and outputs with a focus on PII detection and prompt injection.

Cloud API specializing in prompt injection detection, using fine-tuned models trained on adversarial prompt datasets.

Enterprise AI firewall from Arthur AI, providing input/output validation with hallucination detection and content safety scoring.

Cloud security platform offering AI content scanning as part of a broader API security suite including malware detection and data redaction.

Colang's flow programming model excels at controlling conversational agent behavior and topic adherence.

The largest validator library for ensuring LLM outputs match expected schemas and quality standards.

Simple self-hosted setup for teams that primarily need PII detection and basic input sanitization.

Is Rune a drop-in replacement for Lakera Guard?

The architectures differ fundamentally. Lakera is a cloud API you call explicitly before/after each LLM call. Rune is middleware that wraps your agent client — it intercepts all LLM calls, tool invocations, and inter-agent messages automatically. Migration takes 15-30 minutes: remove Lakera API calls, add `shield = Shield(api_key='...'); client = shield.wrap(OpenAI())`, and optionally customize the default YAML policy. Most teams report equivalent injection catch rates with 10-20x lower latency.

Does Rune detect everything Lakera Guard detects?

Yes, and more. Rune's three detection layers (regex patterns, vector similarity, optional LLM judge) cover the same prompt injection patterns Lakera's single ML classifier catches. Rune additionally detects data exfiltration, PII in outputs, leaked secrets, and privilege escalation — threat categories Lakera doesn't address. Rune also scans tool calls and inter-agent messages, which Lakera's text-level API can't see.

What happens to my data with Rune vs Lakera Guard?

With Lakera Guard, full prompt and response text is sent to their cloud API for every scan. With Rune, all scanning runs locally in your application process using embedded pattern databases and local models. Only structured metadata (event type, threat category, scan latency, result) flows to the Rune dashboard — never raw prompts or responses. This eliminates the need for DPAs and data residency assessments.

Is Lakera Guard still available as a standalone product?

Since Check Point Software completed the ~$187M acquisition in October 2025, Lakera Guard has been folded into Check Point's end-to-end AI security platform. The standalone API with self-serve pricing ($0 to start) is no longer offered. New customers go through Check Point's enterprise sales motion with annual contracts. Existing self-serve customers were given migration deadlines.

How do Rune's detection layers compare to Lakera's single classifier?

Lakera runs one ML classifier per request — fast for clear-cut attacks, but novel payloads that evade the classifier pass through unchecked. Rune uses three layers: L1 (regex/patterns, <3ms) catches known templates; L2 (vector similarity, 5-10ms) catches semantically similar variants; L3 (LLM judge, 100-500ms) handles ambiguous edge cases. L3 fires on ~5% of requests. This layered approach means a novel attack that bypasses L1 still gets caught by L2 or L3.

Can Rune scan tool calls? Lakera only scans text.

Yes — this is a key architectural difference. Rune's middleware intercepts tool/function calls and scans: (1) tool arguments before execution (e.g., detecting SQL injection in a database query tool's parameters), (2) tool return values after execution (e.g., detecting exfiltrated data in a web scraper's response), and (3) inter-agent messages in multi-agent systems. Lakera's text classification API has no awareness of tool boundaries or agent architecture.