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Compare · Architecture Fit

Sunglasses vs Lakera Guard

Layered security, not winner-take-all
sunglasses://compare/sunglasses-vs-lakera
The framing

This comparison is architecture fit, not winner-take-all branding. Other AI security tools like Lakera Guard, NVIDIA NeMo Guardrails, and Azure Prompt Shields use cloud-based ML to catch novel attacks. That's powerful — and we respect it. Sunglasses is a fast, local pre-ingestion filter — you can place it in front of these tools in the same pipeline.

Where Sunglasses fits

Sunglasses is Layer 1 — local, instant, free. It catches known attacks in ~0.26ms and never sends a byte of your data anywhere. Cloud tools like Lakera are Layer 2 — ML-based, global threat intelligence that catches novel attacks pattern matching can’t.

Side by side

Same problem space, different architecture. Pick by where you need the scan to run and what you can send off your machine.

Sunglasses
Lakera Guard
Model
Local pattern + normalization engine
Cloud ML detection
Where it runs
100% on your machine
Hosted API
Data leaves your machine
No — no API key, no cloud, no telemetry
Yes — prompts sent to the API
License
MIT — free forever
Commercial
Latency
~0.26ms local scan
Network round-trip to the API
Best at
Fast, known-pattern pre-ingestion filtering
ML detection of novel attacks
Layer
Layer 1 — local, instant, free
Layer 2 — cloud threat intelligence

Better together

sunglasses://compare/sunglasses-vs-lakera/together
Two layers, one pipeline

Sunglasses is a fast, local pre-ingestion filter — you can place it in front of these tools in the same pipeline. We handle the fast, local scan first; they handle the deep, cloud-based analysis second. Two layers. One pipeline. Better together.

The adapter system

Sunglasses ships an adapter system and integrates with LangChain, CrewAI, and MCP workflows, so it slots in front of existing security stacks rather than replacing them.

When to pick which

sunglasses://compare/sunglasses-vs-lakera/pick
Pick Sunglasses when

you need local-only, offline, zero-cost scanning — compliance, privacy, air-gapped environments, budget, or principle. One pip install takes you from nothing to defended against known attacks.

Pick a cloud guardrail when

you need ML detection of novel attacks and can send input to a third-party API.

Pick both when

you want defense in depth: the local filter reduces load and cost on the cloud layer, and the cloud layer covers the local filter’s blind spots.

FAQ

Is Sunglasses a replacement for Lakera Guard?+
No. Comparison here is architecture fit, not winner-take-all. Sunglasses is a fast, local, MIT-licensed pre-ingestion filter; Lakera Guard is a cloud ML guardrail. You can place Sunglasses in front of Lakera in the same pipeline — the local scan runs first, the cloud analysis second.
When should I use Sunglasses instead of a cloud tool?+
When you cannot send agent input to a third-party API — because of compliance, privacy, cost, air-gapped environments, or principle. Sunglasses runs entirely on your machine with no API key, no cloud, and no telemetry.
Can I run both together?+
Yes. Sunglasses ships an adapter system and integrates with LangChain, CrewAI, and MCP workflows, so it can sit in front of cloud tools in the same pipeline. Every attack caught locally is one fewer cloud API call.