The only LLM security SDK with 4 layers of defense

Others scan inputs with regex. We protect the full lifecycle — detection, red testing, context understanding, and response judgment — all client-side.

Feature comparison

How LaunchPromptly stacks up against the most common alternatives.

Feature	LaunchPromptly	LLM Guard	Lakera Guard	Guardrails AI
L1: DetectionPII redaction (16 patterns + NER)	Yes	Partial	No	No
L1: DetectionPrompt injection (regex + semantic ML)	Yes	Yes	Yes	Yes
L1: DetectionCostGuard (per-customer spend limits)	Yes	No	No	No
L1: DetectionStream scanning (mid-flight)	Yes	No	Yes	No
L1: DetectionIn-SDK ML (no cloud calls)	Yes	Yes	No	Yes
L1: DetectionDe-redaction (restore PII post-LLM)	Yes	Partial	No	No
L1: DetectionUnicode sanitizer + secret detection	Yes	No	No	No
L1: DetectionClient-side (no API proxy)	Yes	Yes	No	No
L2: Red TeamRed Team Engine (80+ attack payloads)	Yes	No	No	No
L2: Red TeamOWASP LLM Top 10 vulnerability mapping	Yes	No	No	No
L3: ContextSystem prompt analysis (Context Engine)	Yes	No	No	No
L4: JudgeResponse boundary enforcement	Yes	No	No	Partial
L4: JudgeNLI semantic compliance checking	Yes	No	No	No
AgenticTool-use validation (SQL injection, SSRF)	Yes	No	No	No
AgenticChain-of-thought auditing	Yes	No	No	No
AgenticConversation memory guards	Yes	No	No	No
Compliance dashboard + audit trail	Yes	No	No	No

What makes us different

CostGuard

Unique to LaunchPromptly

Per-customer sliding window spend limits with pre-call budget estimation. Set hourly, daily, and monthly caps per customer. The SDK estimates token cost before the LLM call and blocks requests that would exceed the budget.

costGuard: {
  maxCostPerRequest: 0.50,
  dailyLimit: 10.00,
  monthlyLimit: 100.00,
  perCustomer: true  // Track per customerId
}

No other guardrail SDK offers per-customer spend tracking.

4-Layer Defense Architecture

No competitor has this

Competitors run one layer of scanning. LaunchPromptly runs four coordinated layers: L1 detects threats in real-time, L2 proactively tests for blind spots, L3 understands what your LLM should do, and L4 enforces those boundaries on every response.

This compound architecture means attacks that bypass L1 regex are caught by L4 semantic analysis. L2 red teaming finds gaps before production. L3 context extraction feeds directly into L4 enforcement.

Compliance Dashboard

Your customers will love this

Every guardrail decision is logged with timestamps, severity, and customer context. Export security reports that your customers' security teams can review during procurement.

Performance

Client-side means zero network latency. Your guardrails run as fast as a regex.

<5ms

L1 Regex pipeline

PII + injection + content filter

+30-100ms

ML enhancement (opt-in)

L1 ML + L3 extractor + L4 NLI

<1ms

L3 Context Engine

Cached per prompt hash

0ms

Network overhead

Client-side, no API round-trip

Lakera Guard advertises <50ms — that includes their cloud API round-trip. Our L1 regex pipeline runs in <5ms with zero network calls.

4-wheel defense architecture

Each layer addresses a different threat vector. ML is opt-in at every layer — no data leaves your infrastructure.

Input/Output Detection

14+ guardrails · <5ms

16 PII patterns + NER, injection scoring, jailbreak detection
Content filter (10 languages), cost guard, unicode sanitizer
Secret detection, topic guard, output safety, schema validation
Optional ML: DeBERTa injection, Toxic-BERT, NER PII, hallucination

Red Team Engine

80+ attacks · pre-deploy

80+ built-in attack payloads across 8 categories
Injection, jailbreak, PII exfil, prompt leak, encoding attacks
Multi-turn manipulation and chained attack sequences
Scored vulnerability report with OWASP LLM Top 10 mapping

Context Engine

7 constraint types · cached

Parses system prompt into structured ContextProfile
Extracts role, allowed/restricted topics, forbidden actions
Output format, knowledge boundaries, persona rules
Cached by prompt hash. Regex baseline + optional ML extractor

Response Judge

6 violation types · semantic

Checks topic drift, role deviation, forbidden actions
Format violations, grounding violations, persona breaks
Severity scoring with weighted violation types
Optional NLI cross-encoder for semantic compliance

All guardrails, organized by layer

L1: Input/Output Detection

PII Redaction

16 regex patterns + optional NER

Injection Detection

Rule-based + DeBERTa semantic ML

Jailbreak Detection

DAN-mode, persona hijacking

Content Filtering

11 categories, 10 languages

CostGuard

Per-customer spend limits

Streaming Guard

Mid-stream PII & injection

Unicode Sanitizer

Zero-width, homoglyphs, bidi

Secret Detection

AWS keys, JWTs, GitHub tokens

Topic Guard

Allowed/blocked topic enforcement

Output Safety

Harmful content, code injection

Prompt Leakage

System prompt leak detection

Schema Validation

Enforce JSON output structure

Hallucination Detection

Fact grounding check

Model Policy

Block models/params

L2: Red Team Engine

Red Team Engine

80+ attack payloads across 8 OWASP categories. Automated vulnerability report.

L3: Context Engine

Context Engine

System prompt analysis. Role, topics, constraints, persona. Regex + ML extraction.

L4: Response Judge

Response Judge

Boundary enforcement. 6 violation types. Severity scoring. Optional NLI model.

Agentic AI Guardrails

Tool Guard

Whitelist/blacklist tools, dangerous arg detection, output scanning

CoT Guard

Scan reasoning blocks for injection, leakage, goal drift

Conversation Guard

Multi-turn state tracking, risk accumulation, loop detection

Ready to see it in action?

Try the playground or start the beta — 4 layers of defense, free to start.

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