Production LLM apps in regulated industries; AI agent products with elevated abuse risk (browser agents, code execution); enterprise rollouts requiring documented AI safety controls.
Internal-only LLM use with low-stakes outputs; experimentation phase before product-market fit; teams committed to building guardrails in-house.
What is Lakera?
Lakera is an AI security company focused on protecting LLM applications from prompt injection, jailbreaks, data exfiltration, and abuse. Their Gandalf AI security education game went viral in 2023 and produced one of the largest datasets of attempted attacks on LLMs, which now informs their Lakera Guard product. Series B raised $20M in 2024 from Atomico. Used by Citi, Dropbox, Allianz, and Reka.
Key features
Integrations
What people actually pay
No price data yet — be the first to share
No price data yet for Lakera. Help the community — share what you pay (anonymized).
AI security for production LLM apps that take it seriously
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.
Production LLM apps in regulated industries (financial services, healthcare, government); AI agents with elevated abuse risk.
Internal-only LLM use with low-stakes outputs, experimentation phase, or teams committed to in-house guardrails.
Written by StackMatch Editorial. StackMatch editorial reviews are independent analyst commentary, not user reviews. We have no affiliate relationship with this tool. See user reviews below for community perspective.
Before you buy Lakera
Vendors don't tell you about their competitors. We do — with verdicts attached when we have them.
What Lakera actually costs
Sticker price isn't the real cost. We add implementation, training, and a probability-weighted lock-in penalty.
When to negotiate Lakera
Vendor sales pressure is non-uniform — quarter-close, year-end, and post-funding-round are your high-leverage windows.
Strong negotiation window. Reps will push for end-of-quarter signature. Don't move first — let them initiate the discount. Target 15-30% off list plus negotiated terms.
Take this to your sales call
10 questions vendor sales teams steer around — generated from Lakera's pricing tier, lock-in profile, and editorial verdict.
- 1PRICINGLakera is professional-tier on the public site. What's the discount path for medium-sized teams committing annually vs. monthly?
- 2PRICINGWhat overages or seat-overflow charges should we plan for? Show me the worst-case bill if our usage grows 2x in year 1.
- 3CONTRACTAuto-renewal: how many days notice is required to terminate, and what happens if we miss the window? Will you commit to a renewal-reminder email at 90 and 60 days?
- 4MIGRATIONData export: what's the complete spec — format, frequency, and what data does the export NOT include? After contract end, how long do we have read-only access?
- 5MIGRATIONImplementation runs days. Who from your team is included by default, and who do we add at additional cost? Is a CSM assigned?
- 6FITLakera is best for: Production LLM apps in regulated industries (financial services, healthcare, government); AI agents with elevated abuse risk.. We're [describe your situation]. Walk me through the failure modes if our profile doesn't match.
- 7FITConnect us with 2-3 reference customers at our company size in Financial Services — not the case-study list, customers who've been live for 18+ months and have churned at least one tool from your stack.
- 8INTEGRATIONLakera lists 5 integrations including OpenAI, Anthropic, AWS Bedrock. Which of OUR existing tools — bring our list — have you confirmed shipping integration with versus "on roadmap"? Show me the actual status.
- 9VENDORTrack record over the last 18 months: any pricing model changes, executive departures, layoffs, M&A activity, or material customer churn we should know about?
- 10VENDORIf you're acquired or shut down, what's the contractual continuity — source-code escrow, data portability, transition period? Show me the actual clause.
What to actually test in the demo
Vendor sales teams script demos to maximize close rate. Here's what they'd rather you not test — derived from Lakera's lock-in profile and editorial verdict.
- 1PERFORMANCEBring YOUR data, not their demo data. Insist on running the demo workflow against a sample of your real records, files, or queries. If they refuse — that's a signal.
- 2PERFORMANCELakera demo will be built around the happy path. Ask: "Show me what happens when [the most common failure mode in our context]" — make them improvise.
- 3EDGE CASESPush the limits live: largest dataset, longest workflow, most users concurrent. Vendors prep demos for medium loads — your real-world usage might 10x what they show.
- 4EDGE CASESMobile and offline behavior: how does Lakera degrade on slow connections, on iPad, in airplane mode? Test in the demo if your team uses these surfaces.
- 5PRICINGModel your worst-case bill: 2x the seats, 3x the usage. Show the exact dollar figure on screen during the demo. Refuse "we'll get back to you" — get the math live.
- 6INTEGRATIONVendors love their integration logo wall. Test the actual depth: pick the 2-3 (OpenAI, Anthropic-style) integrations you depend on most, and ask the rep to demo a real two-way data sync, not a marketing screenshot.
- 7INTEGRATIONAPI and webhook reality check: rate limits, payload size limits, retry behavior, auth refresh handling. Ask for actual API docs in the demo, not "we'll send those."
- 8MIGRATIONDemo the full data export workflow. Even with low lock-in, you want to see how clean the exit looks before signing.
- 9SUPPORTSubmit a real support ticket DURING the demo. Use the actual support channel customers use, not the rep's email. Time the response. This is your most honest data point about post-sale reality.
- 10SUPPORTAsk to be connected with a customer in the demo who you can email TODAY (not "we'll arrange a reference call next week"). The vendor's confidence in their references is a tell.
User Reviews
Be the first to review this tool