Lakera Alternatives

Why Look for Lakera Alternatives?

Lakera Guard is one of the most recognized AI security platforms for real-time prompt injection detection. Its API-first approach, sub-50ms latency, and 98%+ detection rate across 100+ languages have made it a popular choice for teams deploying customer-facing LLM applications. Check Point’s acquisition of Lakera in 2025 brought the technology into the Infinity Platform and CloudGuard WAF, expanding its reach.

The most common reason teams explore alternatives is pricing. Lakera charges per API call, and costs scale directly with traffic volume. For high-throughput applications processing millions of requests per day, the bill adds up. Teams running multiple LLM-powered products across different environments often find that self-hosted open-source tools reduce costs significantly, even after factoring in infrastructure and maintenance.

Other teams want more control. Lakera’s detection models are closed-source, which means you cannot inspect, customize, or extend the detection logic. If your threat model includes domain-specific attack patterns or you need to tune false positive thresholds precisely, a tool where you own the detection pipeline may be a better fit. Some organizations also have data residency requirements that make sending every user interaction to an external API impractical.

Top Lakera Alternatives

1. Promptfoo

Promptfoo is an open-source CLI tool for evaluating, red teaming, and securing LLM applications. It has 10,300 GitHub stars and is used by 300,000+ developers across 127 Fortune 500 companies. The core tool is MIT licensed with a commercial tier for enterprise features.

Where Lakera focuses on runtime protection, Promptfoo covers the pre-deployment side. Its red team module scans for 50+ vulnerability types including prompt injection, jailbreaks, PII leaks, and tool exploitation. You define your application context and Promptfoo generates adversarial inputs automatically. Beyond red teaming, it handles prompt evaluation, model comparison, and real-time guardrails — making it a broader testing platform. For a detailed breakdown of how Promptfoo compares to other scanners, see Garak vs Promptfoo.

Best for: Teams that want pre-deployment red teaming and evaluation alongside runtime guardrails in a single open-source tool. License: Open-source (MIT) with commercial tier Key difference: Testing and evaluation framework, not just runtime protection. Covers the full cycle from prompt development through red teaming to production guardrails.

Promptfoo review

2. Garak

Garak is NVIDIA’s open-source LLM vulnerability scanner with 6,900+ GitHub stars and 37+ probe modules. It runs adversarial scans against any model endpoint and produces structured reports showing what passed and what failed.

Garak goes deeper on attack techniques than most tools. Its probe library covers prompt injection, DAN jailbreaks, encoding bypasses (Base64, ROT-13), training data extraction, package hallucination, malware generation attempts, and cross-site scripting via LLM outputs. The plugin architecture means you can write custom probes for attack patterns specific to your domain. Garak connects to 23 generator backends including OpenAI, Hugging Face, AWS Bedrock, Ollama, and custom REST endpoints.

Best for: Security teams running dedicated adversarial assessments who need a wide library of attack probes. License: Open-source (Apache 2.0) Key difference: Pure vulnerability scanning with 37+ probe modules. No runtime protection — this is a testing tool, not a guardrail.

Garak review

3. LLM Guard

LLM Guard is the closest open-source equivalent to Lakera Guard’s runtime protection model. Built by Protect AI, it provides 15 input scanners and 20 output scanners that sit between your application and its LLM — the same architectural pattern as Lakera.

Input scanners cover prompt injection, PII anonymization, secrets detection, toxicity filtering, invisible text detection, and more. Output scanners handle bias detection, malicious URL blocking, factual consistency checking, and data leakage prevention. Each scanner is independent and configurable. LLM Guard runs fully self-hosted, so no data leaves your infrastructure. It ships as a Python library or a standalone API server via Docker.

Best for: Teams that need Lakera-style input/output scanning as a self-hosted, open-source solution. License: Open-source (MIT) Key difference: Same runtime scanning pattern as Lakera but fully self-hosted and free. You manage the infrastructure and ML models yourself.

LLM Guard review

4. NeMo Guardrails

NVIDIA NeMo Guardrails is an open-source toolkit with 5,600+ GitHub stars for adding programmable guardrails to conversational AI applications. It uses Colang, a domain-specific language, to define safety policies and dialog flows declaratively.

NeMo Guardrails stands apart from Lakera and other guardrail tools through its dialog management capability. Most tools only filter individual inputs and outputs. NeMo models entire conversation flows using five rail types: input, dialog, retrieval, execution, and output. Dialog rails keep conversations on topic across multiple turns, which matters for customer service bots and enterprise assistants where context drifts over time. It integrates with OpenAI, Azure, Anthropic, HuggingFace, and NVIDIA NIM.

Best for: Teams building conversational AI that need multi-turn dialog control and topic boundaries alongside standard guardrails. License: Open-source (Apache 2.0) Key difference: Dialog flow control via Colang is unique. No other guardrail tool models multi-turn conversations natively.

NeMo Guardrails review

5. HiddenLayer

HiddenLayer AISec is a commercial enterprise platform that covers the broader AI security surface beyond prompt injection. The platform provides model scanning, runtime defense, AI discovery (shadow AI detection), and automated red teaming aligned with MITRE ATLAS.

While Lakera focuses on the prompt layer, HiddenLayer protects ML models themselves. Its ModelScanner checks 35+ formats for deserialization attacks, architectural backdoors, and malicious code injections. The runtime defense layer detects adversarial attacks and inference manipulation in real time without requiring access to model weights. HiddenLayer has disclosed 48+ CVEs and holds 25+ patents. Microsoft’s M12 led the company’s $50M Series A.

Best for: Enterprises that need model-level security (scanning, runtime defense, compliance) alongside LLM protection. License: Commercial Key difference: Protects ML models at the artifact level, not just the prompt layer. Covers supply chain security and model integrity that Lakera does not address.

HiddenLayer review

6. Protect AI Guardian

Protect AI Guardian is an ML security gateway that scans models for malicious payloads before they reach production. It was acquired by Palo Alto Networks in July 2025 and integrated into the Prisma AIRS platform. Guardian builds on the open-source ModelScan project (Apache 2.0).

Guardian addresses a different threat than Lakera. It scans 35+ model formats for deserialization attacks, architectural backdoors, and runtime threats. It has scanned over 4 million models on Hugging Face through its partnership with the platform. Threat intelligence comes from huntr, the AI/ML bug bounty platform with 17,000+ security researchers. For teams that download models from public repositories, Guardian adds a security gate that traditional tools miss.

Best for: ML platform teams that need automated model scanning and policy enforcement before deployment. License: Commercial (open-source ModelScan base) Key difference: ML model supply chain security, not prompt-layer protection. Complements rather than replaces Lakera.

Protect AI Guardian review

7. PyRIT

PyRIT (Python Risk Identification Tool) is Microsoft’s open-source AI red teaming framework with 3,400+ GitHub stars and 117 contributors. It was built by Microsoft’s AI Red Team based on their experience testing Bing Chat and Copilot.

PyRIT automates multi-turn, multi-modal red teaming of generative AI systems. Its orchestrators manage different attack patterns: single-turn prompt sending, multi-turn escalation, crescendo attacks, and Tree of Attacks with Pruning (TAP). Converters transform prompts through Base64, ROT13, leetspeak, homoglyph substitution, and cross-modal translation. PyRIT supports text, image, audio, and video testing, which sets it apart from text-only tools. The memory system tracks every prompt and response for reproducible results.

Best for: Security teams that need automated, multi-modal red teaming with programmable attack orchestration. License: Open-source (MIT) Key difference: Multi-modal attack support (text, image, audio, video) and multi-turn orchestration. Built by the team that red-teams Microsoft’s own AI products.

PyRIT review

Feature Comparison

When to Stay with Lakera Guard

Lakera Guard remains the right choice in several scenarios:

• You need managed, low-latency protection. Lakera’s sub-50ms latency and 98%+ detection rates come out of the box. No ML models to host, no infrastructure to maintain, no detection thresholds to tune. For teams that want guardrails without ops overhead, the managed API is hard to beat.
• Multi-language support is critical. Lakera covers 100+ languages and scripts natively. Open-source alternatives typically rely on the underlying LLM provider for language coverage, which may not match Lakera’s dedicated multilingual detection models.
• You process high volumes and value the Check Point ecosystem. After the Check Point acquisition, Lakera Guard integrates into the Infinity Platform and CloudGuard WAF. If your organization already uses Check Point products, keeping Lakera in the stack simplifies vendor management and unifies AI security with network security.
• Your team does not have ML engineering capacity. Running self-hosted guardrails means managing model inference, monitoring detection quality, and updating models as attack techniques evolve. Lakera handles all of this as a service.
• You want combined runtime protection and threat intelligence. The 80M+ adversarial prompts from Gandalf feed Lakera’s detection models daily. Open-source tools do not have access to this dataset.

Frequently Asked Questions

Written by

Suphi Cankurt

Suphi Cankurt is an application security enthusiast based in Helsinki, Finland. He reviews and compares 129 AppSec tools across 10 categories on AppSec Santa. Learn more.