[{"data":1,"prerenderedAt":1248},["ShallowReactive",2],{"docs-page-en-\u002Fdocs\u002Fai-analysis":3,"docs-navigation-en":1153,"docs-surround-en-\u002Fdocs\u002Fai-analysis":1243},{"id":4,"title":5,"body":6,"claimDependencies":1135,"description":15,"extension":1139,"locale":1140,"meta":1141,"missingMedia":1142,"navigation":1143,"owner":1144,"path":1145,"reviewStatus":1146,"seo":1147,"staleLinks":1148,"stem":1149,"technicalRisk":1150,"translationKey":1151,"__hash__":1152},"docs_en\u002Fdocs\u002F5.ai-analysis.md","AI-Powered Analysis",{"type":7,"value":8,"toc":1106},"minimark",[9,16,21,24,30,33,37,42,45,50,69,74,88,93,107,112,126,128,132,136,141,161,166,180,185,202,204,208,211,317,322,333,335,339,344,349,363,368,382,387,398,403,414,419,430,432,436,439,444,553,558,584,586,590,593,598,612,617,631,636,650,655,681,683,687,691,697,702,716,721,727,729,733,738,742,756,760,766,768,772,777,781,795,799,805,807,811,816,820,834,838,844,846,850,854,857,862,888,893,907,912,926,928,932,936,941,967,972,986,990,995,1009,1014,1028,1030,1032,1036,1074,1076,1081,1098,1100],[10,11,12],"blockquote",{},[13,14,15],"p",{},"How Jetscale's AI generates cost optimization recommendations while maintaining performance and reliability",[17,18,20],"h2",{"id":19},"overview","Overview",[13,22,23],{},"Jetscale uses advanced AI to analyze your cloud infrastructure and generate safe, validated recommendations. Our AI doesn't just identify cost savings. It ensures those savings maintain performance, availability, and reliability.",[13,25,26],{},[27,28,29],"strong",{},"This is what sets Jetscale apart from generic cost reports.",[31,32],"hr",{},[17,34,36],{"id":35},"what-makes-jetscales-ai-different","What Makes Jetscale's AI Different",[38,39,41],"h3",{"id":40},"service-specific-intelligence","Service-Specific Intelligence",[13,43,44],{},"Unlike generic cloud cost tools that provide simple alerts (\"Your CPU is low, downsize!\"), Jetscale's AI understands the nuances of each cloud service:",[13,46,47],{},[27,48,49],{},"For RDS\u002FAzure SQL Databases:",[51,52,53,57,60,63,66],"ul",{},[54,55,56],"li",{},"Multi-AZ failover patterns and capacity headroom",[54,58,59],{},"Read replica lag tolerances and replication overhead",[54,61,62],{},"Connection pooling behavior and max connections limits",[54,64,65],{},"Query workload characteristics (OLTP vs OLAP)",[54,67,68],{},"Backup window impact and maintenance considerations",[13,70,71],{},[27,72,73],{},"For EC2\u002FAzure VMs:",[51,75,76,79,82,85],{},[54,77,78],{},"Burstable instance credit balance patterns (T3\u002FT4g)",[54,80,81],{},"Auto Scaling Group headroom requirements for scale events",[54,83,84],{},"Spot instance interruption tolerance and fallback strategies",[54,86,87],{},"Load balancer health check thresholds and timing",[13,89,90],{},[27,91,92],{},"For EBS\u002FAzure Disks:",[51,94,95,98,101,104],{},[54,96,97],{},"IOPS burst patterns vs sustained requirements",[54,99,100],{},"Throughput needs compared to provisioned capacity",[54,102,103],{},"Snapshot strategies and retention policies",[54,105,106],{},"Volume type performance characteristics across workloads",[13,108,109],{},[27,110,111],{},"For ElastiCache\u002FAzure Cache:",[51,113,114,117,120,123],{},[54,115,116],{},"Eviction rates indicating memory pressure",[54,118,119],{},"Connection counts and client connection patterns",[54,121,122],{},"Replication lag in clustered configurations",[54,124,125],{},"Cache hit\u002Fmiss ratios and optimization opportunities",[31,127],{},[17,129,131],{"id":130},"how-ai-analysis-works","How AI Analysis Works",[38,133,135],{"id":134},"_1-comprehensive-data-collection","1. Comprehensive Data Collection",[13,137,138],{},[27,139,140],{},"Performance Metrics:",[51,142,143,146,149,152,155,158],{},[54,144,145],{},"CPU utilization (average, p50, p95, p99, max)",[54,147,148],{},"Memory utilization and swap\u002Fpaging pressure",[54,150,151],{},"Network throughput, packet rates, and bandwidth patterns",[54,153,154],{},"Disk I\u002FO operations, throughput, and latency",[54,156,157],{},"Database connections, active queries, and deadlocks",[54,159,160],{},"Cache hit\u002Fmiss ratios and eviction rates",[13,162,163],{},[27,164,165],{},"Cost Data:",[51,167,168,171,174,177],{},[54,169,170],{},"Current spend per resource (hourly granularity)",[54,172,173],{},"Reserved Instance utilization and coverage gaps",[54,175,176],{},"Savings Plan coverage and commitment utilization",[54,178,179],{},"On-demand vs committed pricing comparisons",[13,181,182],{},[27,183,184],{},"Configuration Data:",[51,186,187,190,193,196,199],{},[54,188,189],{},"Instance types, sizes, and generation",[54,191,192],{},"Volume types, IOPS settings, and throughput limits",[54,194,195],{},"Database engine versions and parameter groups",[54,197,198],{},"Networking configurations (VPC, security groups)",[54,200,201],{},"High-availability settings (multi-AZ, replicas)",[31,203],{},[38,205,207],{"id":206},"_2-intelligent-workload-classification","2. Intelligent Workload Classification",[13,209,210],{},"Jetscale's AI categorizes your resources into optimization patterns:",[212,213,214,233],"table",{},[215,216,217],"thead",{},[218,219,220,224,227,230],"tr",{},[221,222,223],"th",{},"Pattern",[221,225,226],{},"Characteristics",[221,228,229],{},"Optimization Strategy",[221,231,232],{},"Example",[234,235,236,253,269,285,301],"tbody",{},[218,237,238,244,247,250],{},[239,240,241],"td",{},[27,242,243],{},"Steady-State",[239,245,246],{},"Consistent utilization, predictable traffic",[239,248,249],{},"Reserved Instances, right-sizing",[239,251,252],{},"Production API servers",[218,254,255,260,263,266],{},[239,256,257],{},[27,258,259],{},"Bursty",[239,261,262],{},"Low average, high peak spikes",[239,264,265],{},"Burstable instances (T3\u002FT4g), auto-scaling",[239,267,268],{},"Background job processors",[218,270,271,276,279,282],{},[239,272,273],{},[27,274,275],{},"Scheduled",[239,277,278],{},"Regular on\u002Foff patterns (business hours)",[239,280,281],{},"Scheduling, serverless alternatives",[239,283,284],{},"Development environments",[218,286,287,292,295,298],{},[239,288,289],{},[27,290,291],{},"Idle",[239,293,294],{},"Minimal or zero utilization",[239,296,297],{},"Shutdown or significant downsize",[239,299,300],{},"Forgotten test instances",[218,302,303,308,311,314],{},[239,304,305],{},[27,306,307],{},"Over-Provisioned",[239,309,310],{},"High capacity, consistently low usage",[239,312,313],{},"Right-sizing with safety margin",[239,315,316],{},"Over-spec'd databases",[13,318,319],{},[27,320,321],{},"Real Example Analysis:",[323,324,329],"pre",{"className":325,"code":327,"language":328},[326],"language-text","Resource: web-server-prod-01 (t3.2xlarge)\nPattern: Over-Provisioned Steady-State\n\nHistorical Usage:\n- Avg CPU: 12% | Peak CPU: 34%\n- Avg Memory: 28% | Peak Memory: 45%\n- Network: Consistent 100 Mbps (well below limits)\n- Uptime: 100% (24\u002F7 operation)\n\nAI Recommendation:\n→ Downsize to t3.xlarge (half the capacity)\n→ Estimated savings: $248\u002Fmonth (50% reduction)\n→ Safety margin: Peak usage will be 68% CPU (well within safe limits)\n→ Risk: LOW (3x headroom above peak, steady workload)\n→ Implementation: Zero-downtime via create-before-destroy\n","text",[330,331,327],"code",{"__ignoreMap":332},"",[31,334],{},[38,336,338],{"id":337},"_3-multi-layered-safety-validation","3. Multi-Layered Safety Validation",[13,340,341],{},[27,342,343],{},"Before recommending ANY change, Jetscale's AI validates:",[13,345,346],{},[27,347,348],{},"CPU Headroom:",[51,350,351,357,360],{},[54,352,353,356],{},[27,354,355],{},"Minimum 20% buffer"," above historical peak usage",[54,358,359],{},"Burst capacity analysis for unexpected traffic spikes",[54,361,362],{},"Growth trend analysis (increasing usage requires larger buffer)",[13,364,365],{},[27,366,367],{},"Memory Headroom:",[51,369,370,376,379],{},[54,371,372,375],{},[27,373,374],{},"Minimum 15% buffer"," above peak memory usage",[54,377,378],{},"Swap\u002Fpaging risk assessment to avoid OOM conditions",[54,380,381],{},"Memory leak detection and trend analysis",[13,383,384],{},[27,385,386],{},"I\u002FO Capacity:",[51,388,389,392,395],{},[54,390,391],{},"IOPS: Sustained vs burst analysis",[54,393,394],{},"Throughput: Requirements vs volume limits",[54,396,397],{},"Queue depth and latency patterns under load",[13,399,400],{},[27,401,402],{},"Network Capacity:",[51,404,405,408,411],{},[54,406,407],{},"Bandwidth utilization trends and peak analysis",[54,409,410],{},"Packet rate limitations for instance size",[54,412,413],{},"Network performance class suitability (up to 100 Gbps)",[13,415,416],{},[27,417,418],{},"High Availability:",[51,420,421,424,427],{},[54,422,423],{},"Multi-AZ requirements preserved for production workloads",[54,425,426],{},"Failover capacity validated (can survive AZ failure)",[54,428,429],{},"Replication lag impact assessed across zones",[31,431],{},[38,433,435],{"id":434},"_4-cost-performance-trade-off-analysis","4. Cost-Performance Trade-off Analysis",[13,437,438],{},"Jetscale's AI evaluates multiple alternatives and selects the optimal balance:",[13,440,441],{},[27,442,443],{},"Example: RDS db.r5.2xlarge Database",[212,445,446,465],{},[215,447,448],{},[218,449,450,453,456,459,462],{},[221,451,452],{},"Option",[221,454,455],{},"Monthly Cost",[221,457,458],{},"Performance Impact",[221,460,461],{},"Risk Level",[221,463,464],{},"AI Score",[234,466,467,483,500,520,537],{},[218,468,469,472,475,478,481],{},[239,470,471],{},"db.r5.2xlarge (current)",[239,473,474],{},"$1,180",[239,476,477],{},"Baseline",[239,479,480],{},"-",[239,482,480],{},[218,484,485,488,491,494,497],{},[239,486,487],{},"db.r5.xlarge",[239,489,490],{},"$590",[239,492,493],{},"-5% query latency",[239,495,496],{},"Medium",[239,498,499],{},"6.8\u002F10",[218,501,502,505,508,511,514],{},[239,503,504],{},"db.r6i.xlarge",[239,506,507],{},"$540",[239,509,510],{},"Same\u002Fbetter, newer gen",[239,512,513],{},"Low",[239,515,516,519],{},[27,517,518],{},"9.2\u002F10"," ✓",[218,521,522,525,528,531,534],{},[239,523,524],{},"db.t3.2xlarge",[239,526,527],{},"$350",[239,529,530],{},"CPU credit risk on spikes",[239,532,533],{},"High",[239,535,536],{},"3.5\u002F10",[218,538,539,542,545,548,550],{},[239,540,541],{},"db.r6g.xlarge (Graviton)",[239,543,544],{},"$475",[239,546,547],{},"Same\u002Fbetter, ARM-based",[239,549,496],{},[239,551,552],{},"8.5\u002F10",[13,554,555],{},[27,556,557],{},"AI selects db.r6i.xlarge because:",[51,559,560,566,572,578],{},[54,561,562,565],{},[27,563,564],{},"54% cost reduction"," ($640\u002Fmonth savings)",[54,567,568,571],{},[27,569,570],{},"Zero performance degradation"," (same memory, improved CPU)",[54,573,574,577],{},[27,575,576],{},"Low implementation risk"," (in-place modification supported)",[54,579,580,583],{},[27,581,582],{},"Same reliability"," (multi-AZ configuration preserved)",[31,585],{},[38,587,589],{"id":588},"_5-evidence-based-recommendations","5. Evidence-Based Recommendations",[13,591,592],{},"Every Jetscale recommendation is backed by transparent evidence:",[13,594,595],{},[27,596,597],{},"Usage Charts:",[51,599,600,603,606,609],{},[54,601,602],{},"Historical CPU utilization graph with percentile bands",[54,604,605],{},"Historical memory utilization graph showing peaks",[54,607,608],{},"Peak vs average comparison with trend lines",[54,610,611],{},"Percentile analysis (p50, p95, p99) for capacity planning",[13,613,614],{},[27,615,616],{},"Cost Analysis:",[51,618,619,622,625,628],{},[54,620,621],{},"Current monthly cost (itemized by component)",[54,623,624],{},"Projected monthly cost after change",[54,626,627],{},"Annual savings projection with confidence interval",[54,629,630],{},"Break-even timeline (for Reserved Instance purchases)",[13,632,633],{},[27,634,635],{},"Configuration Comparison:",[51,637,638,641,644,647],{},[54,639,640],{},"Side-by-side current vs recommended settings",[54,642,643],{},"Highlighted changes with explanations",[54,645,646],{},"Performance impact notes and mitigation strategies",[54,648,649],{},"Compatibility checks (OS, app requirements)",[13,651,652],{},[27,653,654],{},"Implementation Risk Assessment:",[51,656,657,663,669,675],{},[54,658,659,662],{},[27,660,661],{},"Risk level",": Low, Medium, or High with justification",[54,664,665,668],{},[27,666,667],{},"Specific risks identified",": CPU pressure, memory constraints",[54,670,671,674],{},[27,672,673],{},"Mitigation strategies",": Gradual rollout, canary deployments",[54,676,677,680],{},[27,678,679],{},"Rollback instructions",": Step-by-step recovery process",[31,682],{},[17,684,686],{"id":685},"types-of-ai-analysis","Types of AI Analysis",[38,688,690],{"id":689},"_1-right-sizing-analysis","1. Right-Sizing Analysis",[13,692,693,696],{},[27,694,695],{},"What It Does:","\nDetermines optimal instance size based on actual usage patterns and workload characteristics.",[13,698,699],{},[27,700,701],{},"AI Factors Considered:",[51,703,704,707,710,713],{},[54,705,706],{},"Utilization trends over extended period (not just averages)",[54,708,709],{},"Traffic pattern classification (steady, bursty, scheduled)",[54,711,712],{},"Growth rate analysis (10% YoY growth requires bigger buffer)",[54,714,715],{},"Seasonal variation detection (holiday spikes, end-of-quarter)",[13,717,718],{},[27,719,720],{},"Example Recommendation:",[323,722,725],{"className":723,"code":724,"language":328},[326],"Current: t3.xlarge (4 vCPU, 16 GB RAM) = $730\u002Fmonth\nRecommended: t3.large (2 vCPU, 8 GB RAM) = $482\u002Fmonth\n\nReason: Average CPU 12%, peak CPU 34% over extended monitoring period.\nRecommended config provides 3x headroom above peak usage.\n\nMonthly Savings: $248 (34% reduction)\nAnnual Savings: $2,976\nRisk Level: LOW\nImplementation: Zero-downtime (create before destroy)\nRollback: Switch back in-place if needed\n",[330,726,724],{"__ignoreMap":332},[31,728],{},[38,730,732],{"id":731},"_2-reserved-instance-analysis","2. Reserved Instance Analysis",[13,734,735,737],{},[27,736,695],{},"\nIdentifies resources suitable for 1-year or 3-year commitments to unlock 40-60% discounts.",[13,739,740],{},[27,741,701],{},[51,743,744,747,750,753],{},[54,745,746],{},"Uptime patterns (must be predictable 24\u002F7 or scheduled)",[54,748,749],{},"Instance type stability (no recent size changes)",[54,751,752],{},"Workload criticality (production gets higher priority)",[54,754,755],{},"Flexibility requirements (can you commit for 1-3 years?)",[13,757,758],{},[27,759,720],{},[323,761,764],{"className":762,"code":763,"language":328},[326],"Current: 5x m5.large on-demand ($438\u002Fmonth each)\nTotal: $2,190\u002Fmonth | $26,280\u002Fyear\n\nRecommended: 5x m5.large 1-year Reserved Instance (No Upfront)\nCost: $1,314\u002Fmonth | $15,768\u002Fyear\n\nMonthly Savings: $876 (40% reduction)\nAnnual Savings: $10,512\nBreak-even: Immediate (No Upfront payment required)\nRisk Level: LOW (resources running 24\u002F7 for 6+ months)\nCommitment: 1 year, flexible across AZs\n",[330,765,763],{"__ignoreMap":332},[31,767],{},[38,769,771],{"id":770},"_3-graviton-migration-analysis","3. Graviton Migration Analysis",[13,773,774,776],{},[27,775,695],{},"\nIdentifies workloads that can benefit from 30-40% savings on ARM-based AWS Graviton instances.",[13,778,779],{},[27,780,701],{},[51,782,783,786,789,792],{},[54,784,785],{},"Application compatibility (containerized workloads ideal)",[54,787,788],{},"Performance profile (Graviton3 excels at web, databases, caching)",[54,790,791],{},"Cost-performance ratio (price\u002Fperformance advantage)",[54,793,794],{},"Migration effort vs savings (ROI calculation)",[13,796,797],{},[27,798,720],{},[323,800,803],{"className":801,"code":802,"language":328},[326],"Current: 10x m5.2xlarge ($1,752\u002Fmonth each)\nTotal: $17,520\u002Fmonth | $210,240\u002Fyear\n\nRecommended: 10x m6g.2xlarge (Graviton3, ARM-based)\nCost: $10,512\u002Fmonth | $126,144\u002Fyear\n\nMonthly Savings: $7,008 (40% reduction)\nAnnual Savings: $84,096\nPerformance: Same or better for most workloads\nRisk Level: MEDIUM (requires ARM compatibility validation)\nMigration Notes: Test in staging first, monitor CPU metrics\nRollback: Switch back to x86 instances if issues arise\n",[330,804,802],{"__ignoreMap":332},[31,806],{},[38,808,810],{"id":809},"_4-storage-optimization-analysis","4. Storage Optimization Analysis",[13,812,813,815],{},[27,814,695],{},"\nOptimizes EBS volume types (gp2→gp3) and adjusts IOPS\u002Fthroughput for actual needs.",[13,817,818],{},[27,819,701],{},[51,821,822,825,828,831],{},[54,823,824],{},"IOPS usage patterns (provisioned vs actual utilization)",[54,826,827],{},"Throughput requirements (sustained vs burst)",[54,829,830],{},"Burst vs sustained performance needs",[54,832,833],{},"Cost per IOPS across volume types (gp3 is 20% cheaper)",[13,835,836],{},[27,837,720],{},[323,839,842],{"className":840,"code":841,"language":328},[326],"Current: 50x gp2 volumes (500 GB each, 1500 IOPS baseline)\nCost: $50\u002Fmonth each | $2,500\u002Fmonth total\n\nRecommended: 50x gp3 volumes (500 GB, 3000 IOPS baseline)\nCost: $40\u002Fmonth each | $2,000\u002Fmonth total\n\nMonthly Savings: $500 (20% reduction)\nAnnual Savings: $6,000\nPerformance: Same or better (gp3 has 2x baseline IOPS)\nRisk Level: LOW (in-place modification supported, no downtime)\nImplementation: Modify volumes during maintenance window\n",[330,843,841],{"__ignoreMap":332},[31,845],{},[17,847,849],{"id":848},"continuous-learning-improvement","Continuous Learning & Improvement",[38,851,853],{"id":852},"feedback-loop","Feedback Loop",[13,855,856],{},"Jetscale's AI improves over time by learning from outcomes:",[13,858,859],{},[27,860,861],{},"Tracking Outcomes:",[51,863,864,870,876,882],{},[54,865,866,869],{},[27,867,868],{},"Actual savings vs projected",": 98% accuracy within ±5%",[54,871,872,875],{},[27,873,874],{},"Performance impact post-deployment",": Monitoring for degradation",[54,877,878,881],{},[27,879,880],{},"Rollback frequency and reasons",": Learning from issues",[54,883,884,887],{},[27,885,886],{},"Customer acceptance rates",": Understanding preferences",[13,889,890],{},[27,891,892],{},"Refining Models:",[51,894,895,898,901,904],{},[54,896,897],{},"Adjusting safety margins based on real-world outcomes",[54,899,900],{},"Improving workload classification accuracy",[54,902,903],{},"Better prediction of seasonal patterns and growth",[54,905,906],{},"Enhanced risk scoring based on deployment results",[13,908,909],{},[27,910,911],{},"Incorporating Feedback:",[51,913,914,917,920,923],{},[54,915,916],{},"Learn from rejected recommendations (too aggressive? wrong timing?)",[54,918,919],{},"Adjust for organization-specific preferences (conservative vs aggressive)",[54,921,922],{},"Respect manual overrides and exceptions (don't re-suggest)",[54,924,925],{},"Personalize risk tolerance per team\u002Fenvironment",[31,927],{},[17,929,931],{"id":930},"transparency-control","Transparency & Control",[38,933,935],{"id":934},"understanding-every-recommendation","Understanding Every Recommendation",[13,937,938],{},[27,939,940],{},"Every recommendation includes:",[51,942,943,949,955,961],{},[54,944,945,948],{},[27,946,947],{},"Why this change is suggested",": Specific usage patterns and inefficiencies",[54,950,951,954],{},[27,952,953],{},"What data supports it",": Historical metrics and charts",[54,956,957,960],{},[27,958,959],{},"What risks exist",": Honest assessment of potential issues",[54,962,963,966],{},[27,964,965],{},"How to roll back if needed",": Step-by-step instructions",[13,968,969],{},[27,970,971],{},"You always have control:",[51,973,974,977,980,983],{},[54,975,976],{},"Accept, reject, or defer any recommendation",[54,978,979],{},"Provide feedback on accuracy and outcomes",[54,981,982],{},"Set custom policies (e.g., \"never touch production databases\")",[54,984,985],{},"Adjust risk tolerance levels (conservative to aggressive)",[38,987,989],{"id":988},"safety-guarantees","Safety Guarantees",[13,991,992],{},[27,993,994],{},"Jetscale's AI never recommends changes that:",[51,996,997,1000,1003,1006],{},[54,998,999],{},"Eliminate high-availability configurations (multi-AZ, replicas)",[54,1001,1002],{},"Risk SLA violations or performance degradation",[54,1004,1005],{},"Require migrations without sufficient validation data",[54,1007,1008],{},"Can't be easily rolled back if issues occur",[13,1010,1011],{},[27,1012,1013],{},"Red flags that prevent recommendations:",[51,1015,1016,1019,1022,1025],{},[54,1017,1018],{},"Insufficient usage data (minimal historical metrics)",[54,1020,1021],{},"High variability in workload patterns (unpredictable)",[54,1023,1024],{},"Recent instance size changes (let it stabilize first)",[54,1026,1027],{},"Critical production resources without backup plan",[31,1029],{},[31,1031],{},[17,1033,1035],{"id":1034},"related-documentation","Related Documentation",[51,1037,1038,1046,1053,1060,1067],{},[54,1039,1040,1045],{},[1041,1042,1044],"a",{"href":1043},"\u002Fdocs\u002Fhow-it-works","How Jetscale Works"," - Overall platform workflow",[54,1047,1048,1052],{},[1041,1049,1051],{"href":1050},"\u002Fdocs\u002Faws-setup","AWS Account Setup"," - Connect your AWS account",[54,1054,1055,1059],{},[1041,1056,1058],{"href":1057},"\u002Fdocs\u002Fazure-setup","Azure Account Setup"," - Connect your Azure account",[54,1061,1062,1066],{},[1041,1063,1065],{"href":1064},"\u002Fdocs\u002Fservices","Supported Services"," - What Jetscale optimizes",[54,1068,1069,1073],{},[1041,1070,1072],{"href":1071},"\u002Fdocs\u002Ffaq","FAQ"," - Common questions answered",[31,1075],{},[13,1077,1078],{},[27,1079,1080],{},"Questions about AI safety or validation?",[51,1082,1083,1090],{},[54,1084,1085,1086],{},"Email: ",[1041,1087,1089],{"href":1088},"mailto:support@jetscale.ai","support@jetscale.ai",[54,1091,1092],{},[1041,1093,1097],{"href":1094,"rel":1095},"https:\u002F\u002Fjetscale.ai\u002Fdemo",[1096],"nofollow","Schedule Technical Deep Dive",[31,1099],{},[13,1101,1102],{},[1103,1104,1105],"em",{},"Last Updated: January 29, 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