§ / Comparison · Last verified 10 June 2026
Zentric vs LLM Guard vs Lakera Guard: pick the right one — even if it isn't us.
Three different answers to prompt injection and PII risk. One is a free open-source toolkit you run yourself. One is an enterprise AI-security platform now inside a major security vendor. One — ours — is a small, deterministic API whose real product is the signed audit record. Here is the honest version of when each wins.
The 30-second answer
Use LLM Guard if…
You want free, open-source, self-hosted guardrails and you have the infrastructure (and team) to run, tune and update ML models yourself. MIT-licensed, 35+ input/output scanners, strong community. You pay with your own compute and ops time.
Use Lakera Guard if…
You're an enterprise that wants a managed, ML-based AI-security platform with red-teaming pedigree and vendor backing — Lakera was acquired by Check Point, and its detection is trained on the 80M+ attack patterns generated by its Gandalf project.
Use Zentric if…
You need to prove what your AI pipeline blocked, anonymized and let through — to an auditor, a DPO, or a customer's procurement team. Every Zentric verdict is deterministic (same input → same verdict) and ships with a signed audit record: SHA-256 hash, UUID, UTC timestamp, per request. Detection is the feature. The receipt is the product.
Side by side
| Property | LLM Guard | Lakera Guard | Zentric Protocol |
|---|---|---|---|
| What it is | Open-source Python toolkit (input/output scanners) | Managed AI-security API & platform | Managed detection API + native MCP server |
| Detection approach | Mix of ML models (prompt-injection scanner is a fine-tuned DeBERTa-v3 classifier) + rules | ML models trained on Gandalf adversarial data | Deterministic signature matching — 22 signatures, 7 languages. Not an ML classifier, and we say so. |
| Deterministic / reproducible verdicts | No — classifier scores can change with model versions and thresholds | No — model-based | Yes — same input, same verdict, always |
| Signed audit record per request | Not a built-in feature* | Not advertised as a per-request signed record* | Yes — SHA-256 + UUID + UTC, designed for GDPR Art. 30 documentation |
| Catches novel / unseen attack phrasings | Better positioned — ML can generalize beyond known patterns | Better positioned — large adversarial training set | Only within the signature catalogue. Semantic attacks outside the 22 signatures are not claimed. This is the trade-off for determinism. |
| Hosting | Self-hosted (your infra, your GPUs) | SaaS (EU-relevant: managed by vendor) | SaaS, EU-based company; EU data residency on Enterprise |
| Price | Free (MIT) — you pay infra + maintenance | Free Community developer tier; Pro/Enterprise: contact sales (quota figures not publicly verifiable — pricing sits behind the platform login) | Free 10k req/mo; $29 / $99 / $499 tiers; Enterprise custom |
| Latency | Hardware-dependent; Protect AI's own prompt-injection benchmark ranges ~8 ms (GPU+ONNX) to ~420 ms (CPU) average | "Low-latency API" — no public per-request figure we can verify | Deterministic engine, no model in the hot path. Published figure pending a real-dataset benchmark — we won't quote a number we can't show you the methodology for. |
| Owned by | Protect AI → acquired by Palo Alto Networks (completed Jul 2025) | Acquired by Check Point (announced 2025, expected close Q4 2025) | Independent, bootstrapped (Mataró, Spain) |
| Scope beyond injection/PII | Widest: toxicity, bias, code, topics, 35+ scanners | Broad platform incl. red teaming (Lakera Red) | Narrow on purpose: injection + PII + audit trail |
| MCP-native tool for agents | No | No native MCP server we could find* | Yes — analyze_prompt via npm zentric-protocol-mcp |
* Claim of absence, verified against public docs on 10 June 2026 — if either vendor ships this, tell us and we'll update the page. Every other claim is linked in Sources below.
What we won't tell you
We're not going to claim Zentric "detects more" than these two. It doesn't try to. LLM Guard's classifier and Lakera's models will generalize to attack phrasings Zentric's 22 signatures have never seen — that's the honest cost of a deterministic engine. What a probabilistic classifier cannot give you, by construction, is the property auditors care about: reproducibility. A score of 0.83 from model v2 that becomes 0.41 in model v3 is not evidence. Same input, same verdict, signed and timestamped — that's evidence.
Also true: if you never face a compliance review, never sign a DPA, and never have a customer ask "show me what your AI rejected" — you may not need Zentric at all. Use LLM Guard and ship.
The compliance angle (why we exist)
GDPR is in force and fining today; Art. 30 requires records of processing activities, and a per-request signed verdict log is strong supporting documentation. The EU AI Act's high-risk obligations have been pushed back by the Omnibus agreement — so the smart move isn't deadline panic, it's choosing infrastructure now that produces evidence by default. Zentric's audit record costs you one POST request and gives your future self the paper trail.
Sources
- LLM Guard repo, license & scanners: github.com/protectai/llm-guard (MIT; ~2.9k stars as of 10 Jun 2026, via GitHub API)
- LLM Guard prompt-injection scanner = DeBERTa-v3 classifier + latency benchmarks: official docs
- Palo Alto Networks completes Protect AI acquisition (Jul 22, 2025): press release
- Check Point acquires Lakera (incl. Gandalf 80M+ attack patterns): Check Point press release · CyberScoop
- Lakera Guard pricing tiers: platform.lakera.ai/pricing (page is app-rendered; exact quotas could not be independently verified on 10 Jun 2026)
- Zentric verdict & audit record format: quickstart · repo
Found an error on this page? Email core@zentricprotocol.com — corrections ship within 48h. An honest comparison is the only kind worth ranking for.