Home / Directory / AI SaaS tooling / Security, safety & guardrails / Lakera
Lakera
Security layer for LLM applications, aimed at prompt injection, data loss, and related abuse on model inputs and outputs.
Teams shipping LLM features that accept user input and can place a check on the request path.
Buyers who want a one-time filter to replace app auth, data permissions, or a human review policy.
Verdict
Put Lakera on the path when users or documents can steer a model that has tools or private context. Tune it on your traffic so it blocks the cases you care about. It does not replace authorization on the tools the model can call. Skip it as a substitute for access control, or if you have no path to insert a check before the model acts.
Score Breakdown
How Lakera scores on the jobs buyers hire it for, and on the company and commercial side of the deal.
Use-case matrix
| Use case | Fit | Notes |
|---|---|---|
| User-facing LLM with tools | Strong | Where a guardrail earns a seat. |
| Basic content screening | Mixed | Depends on the policy you configure. |
| Replacing auth and data permissions | Poor | Different control. |
| No insertion point in the app | Poor | The check never runs. |
Who it’s for
Good fit
- LLM apps with untrusted input
- Teams that can tune on production-like traffic
- Products where the model can call tools
Poor fit
- Auth replacement projects
- Apps with nowhere to put a check
- Buyers seeking a complete security program in one SKU
Review Excerpts
Below are excerpts from public reviews. Our team scoured public reviews, forums, and chat rooms to get a balanced view of customers' experience with this company. Paid reviews and pay-for-play sites such as Clutch were excluded.
“Most tools I've found (Lakera Guard, LLM Guard, Azure Prompt Shields) are still text-only in their public APIs. The research papers describe the attacks well but I haven't seen many production-grade defences for image/audio/document injection.”
“Most of the defenses discussed here (Lakera, LLM Guard, Prompt Shields) are detection layers — they try to classify whether an input is malicious before it reaches the model. The problem with semantic attacks is exactly what you identified: you can't reliably detect "act as a SQL expert" as benign vs. malicious without full context.”
Methodology
This page is an independent evaluation of Lakera for buyers comparing options in security, safety & guardrails. AI Industry Reviews accepts no sponsorships, advertising, or pay-for-placement fees. Lakera did not pay for this review.
What we scored
The headline number is an Overall Score on a 0-10 scale. Eight criteria sit under it in two groups.
- Buyer outcomes (for security, safety & guardrails)
- Injection and abuse checks
- Request-path fit
- Tuning on real traffic
- Limit of a filter
- Company & commercial
- Innovation &
- product leadership
- Project management &
- communication
- Pricing
- Contract fairness
Pricing measures whether the price looks fair for the value delivered, including packaging and renewal friction that show up in real buying cycles.
Score Composition
| Input | Weight | What it covers |
|---|---|---|
| Reviews | 40% | A proprietary read of what practitioners say about likes, complaints, and day-to-day use, including public review sites, forums, and private chat rooms. Paid reviews and pay-for-play sites such as Clutch are out of scope. |
| Product | 35% | Hands-on look at screens and workflows. |
| Pricing | 15% | Whether the price looks fair for what you get. |
| Docs & training | 10% | Docs, tutorials, and training material. |
How we balanced the evidence
The Overall Score is the simple average of the eight criteria. Recommendation language follows that score and the fit pattern described above.
Scope
Lakera is graded here as security, safety & guardrails. Criteria scores can move as more review volume and product checks are added.