> ## Documentation Index > Fetch the complete documentation index at: https://docs.qwedai.com/llms.txt > Use this file to discover all available pages before exploring further. # QWED performance and cost benchmarks > QWED latency benchmarks for Math, Logic, Code, SQL, and other verification engines. Most verifications complete under 100ms with detailed cost comparisons. **Test Environment:** * Hardware: AWS EC2 t3.medium (2 vCPU, 4GB RAM) * Python: 3.11 * QWED: v1.1.0 * Date: December 2025 *** ## Performance benchmarks (latency) ### Verification engine latency | Engine | Operation | Typical Latency | P95 Latency | P99 Latency | | ------------- | ----------------------------- | --------------- | ----------- | ----------- | | **Math** | Simple arithmetic | 5ms | 12ms | 18ms | | **Math** | Calculus (derivative) | 15ms | 28ms | 45ms | | **Math** | Matrix (3×3) | 22ms | 40ms | 65ms | | **Math** | Financial (compound interest) | 8ms | 15ms | 25ms | | **Logic** | Simple SAT | 25ms | 50ms | 80ms | | **Logic** | Z3 theorem proving | 120ms | 250ms | 400ms | | **Code** | AST security scan | 35ms | 70ms | 110ms | | **Code** | Symbolic execution (simple) | 2,500ms | 8,000ms | 15,000ms | | **SQL** | Query parsing | 18ms | 35ms | 55ms | | **SQL** | Schema validation | 45ms | 85ms | 130ms | | **Stats** | Mean/median | 12ms | 25ms | 40ms | | **Stats** | Regression (100 rows) | 95ms | 180ms | 280ms | | **Fact** | TF-IDF grounding | 60ms | 120ms | 200ms | | **Image** | Metadata check | 15ms | 30ms | 50ms | | **Consensus** | 3-model check | 4,500ms | 7,000ms | 12,000ms | ### Key observations * ✅ **Most verifications \< 100ms** - Suitable for real-time applications * ⚠️ **Symbolic execution slow** - Use only for simple functions * ⚠️ **Consensus expensive** - 3× LLM API calls required *** ## Cost comparison ### Scenario: financial calculator application **Use Case:** Verify 1,000 compound interest calculations per day | Approach | Description | Cost per 1K Verifications | Annual Cost | | ------------------------- | ---------------------- | ------------------------- | ----------- | | **No Verification** | Trust LLM blindly | \$0 | \$0 | | **QWED (Math Engine)** | 1 LLM call + SymPy | **\$0.50** | **\$183** | | **Self-Consistency (3×)** | Call LLM 3 times, vote | \$1.50 | \$548 | | **Self-Consistency (5×)** | Call LLM 5 times, vote | \$2.50 | \$913 | | **Human Review** | Manual checking | \$500 | \$182,500 | **QWED saves 80% vs self-consistency, 99.9% vs human review.** *** ### Scenario: SQL query generation (RAG application) **Use Case:** Verify 5,000 SQL queries per day | Approach | LLM Calls | Verification | Cost per 1K | Annual Cost | | ------------------------- | --------- | ------------- | ----------- | ----------- | | **QWED (SQL Engine)** | 1 | SQLGlot parse | **\$0.50** | **\$913** | | **Self-Consistency (3×)** | 3 | Majority vote | \$1.50 | \$2,738 | | **Manual Review** | 1 | DBA checks | \$250 | \$456,250 | **QWED saves 67% vs self-consistency, 99.8% vs manual review.** *** ### Scenario: code security scanning **Use Case:** Verify 500 code snippets per day | Approach | Detection Rate | Cost per 1K | Annual Cost | False Positive Rate | | ------------------------- | -------------- | ----------- | ----------- | ------------------- | | **QWED (Code Engine)** | 100% | **\$0.50** | **\$91** | 0% | | **GPT-4 Security Review** | 85% | \$2.00 | \$365 | 15% | | **Manual Code Review** | 95% | \$400 | \$73,000 | 5% | **QWED: 100% detection + \$0 false positive cost.** *** ## API pricing (QWED Cloud) ### Free tier * 1,000 verifications/month * All 8 engines * No credit card required ### Pro (\$49/month) * 50,000 verifications/month * Custom timeout limits * Priority support * SLA: 99.9% uptime ### Enterprise (custom) * Unlimited verifications * On-premise deployment * Custom SLA * Dedicated support ### Pay-as-you-go * \$0.0005 per verification (beyond free tier) * Volume discounts available *** ## ROI calculator ### Example: finance application **Assumptions:** * 10,000 calculations/day * LLM cost: \$0.50 per 1K calls * Error rate without QWED: 5% * Average error cost: \$1,000 per error | Metric | Without QWED | With QWED | | ----------------------- | ------------- | -------------- | | **Daily verifications** | 0 | 10,000 | | **LLM cost** | \$5/day | \$5/day | | **QWED cost** | \$0/day | \$5/day | | **Errors per day** | 500 | 0 | | **Error cost** | \$500,000/day | \$0/day | | **Total cost** | \$500,005/day | \$10/day | | **Savings** | - | **\$500K/day** | **Payback period: \< 1 day** *** ## Latency optimization tips ### 1. Enable Redis caching ```python theme={null} from qwed_sdk import QWEDClient client = QWEDClient( cache_enabled=True, redis_url="redis://localhost:6379" ) # Repeated verifications: 0.5ms (99% faster) ``` ### 2. Async verification ```python theme={null} import asyncio async def verify_batch(items): tasks = [client.verify_async(item) for item in items] return await asyncio.gather(*tasks) # 10× throughput improvement ``` ### 3. Selective engines ```python theme={null} # Only enable engines you need client = QWEDClient(engines=["math", "code"]) # Faster startup ``` ### 4. Timeout tuning ```python theme={null} # Reduce timeout for simple operations result = engine.verify_math(expr, timeout_ms=1000) # 1s max ``` *** ## Throughput benchmarks | Configuration | Requests/Second | Avg Latency | | ----------------------- | --------------- | ----------- | | Single thread | 15 req/s | 65ms | | 4 workers | 55 req/s | 70ms | | 8 workers | 95 req/s | 80ms | | 16 workers (with Redis) | 180 req/s | 85ms | **Recommendation:** 8 workers for production *** ## Comparison with alternatives ### vs Guardrails AI | Feature | QWED | Guardrails AI | | ----------------- | ----------------- | ------------------- | | **Deterministic** | ✅ Yes (SymPy, Z3) | ❌ No (regex, ML) | | **Provable** | ✅ Math proofs | ❌ Heuristic | | **Latency** | 5-100ms | 50-200ms | | **Cost** | \$0.50/1K | \$0.80/1K | | **Security** | ✅ AST analysis | ⚠️ Pattern matching | ### vs self-consistency | Metric | QWED | Self-Consistency (5×) | | ----------------- | ---------------- | --------------------- | | **Accuracy** | 100% (in domain) | 85-95% | | **Cost** | \$0.50/1K | \$2.50/1K | | **Latency** | 50ms | 5,000ms (100× slower) | | **Deterministic** | ✅ Yes | ❌ No | ### vs manual review | Metric | QWED | Human Review | | --------------- | --------- | ------------ | | **Speed** | 50ms | 5 minutes | | **Cost** | \$0.0005 | \$5 | | **Accuracy** | 100% | 95% | | **Scalability** | Unlimited | Limited | *** ## Production scaling ### Architecture for 1M verifications/day ``` Load Balancer ├── API Server 1 (8 workers) ├── API Server 2 (8 workers) ├── API Server 3 (8 workers) └── Redis Cluster (3 nodes) Estimated cost: $200/month (AWS) Handles: 1.2M verifications/day Latency: p95 < 100ms ``` *** ## Summary | Question | Answer | | ------------------------- | --------------------------------------------------- | | **Typical latency?** | 5-100ms for most engines | | **Cost vs alternatives?** | 80% cheaper than self-consistency | | **Scalability limit?** | 180 req/s per server (tested) | | **Best use case?** | High-stakes domains (finance, healthcare, security) | *** **Need custom benchmarks?** Contact: [support@qwedai.com](mailto:support@qwedai.com)