매니지드 AI 서비스
오버헤드 없이 AI를 운영하세요
프로덕션 AI 시스템 운영에는 전문 MLOps 인재, GPU 인프라 전문성, 24/7 운영 감시가 필요합니다. Our managed AI services handle all of it, so your team stays focused on building business value instead of babysitting infrastructure.
AI 운영 비용 40-60% 절감 - 스타터 플랜 월 $3K부터
APAC 전역의 기업에 서비스 제공 • Vietnam • Singapore • South Korea • Japan • Hong Kong
기업이 AI 운영에 어려움을 겪는 이유
According to Gartner, 85% of AI projects never make it to production. The primary reason is not the models themselves, but the operational complexity of running them reliably in production.
THE IN-HOUSE BURDEN
일반적인 사내 MLOps 팀에는 3-5명의 전문 엔지니어가 필요합니다 at $180-250K each, plus GPU infrastructure costs of $50-200K/month, plus tooling licenses exceeding $30K/year.
SERAPHIM MANAGED AI
하나의 예측 가능한 월비용이 모든 MLOps 운영을 포함합니다, GPU management, monitoring, scaling, and 24/7 support. Your team focuses on model development and business outcomes.
즉시 AI 운영 평가 받기
Our consultant analyzes your current AI operations and recommends optimization strategies in real-time.
Ghost 상담Managed AI Service Capabilities
Comprehensive operational coverage for every layer of your AI stack, from data pipelines to production inference.
Model Hosting & Inference
We deploy and manage your trained models across optimized GPU clusters (NVIDIA A100, H100, L40S) with auto-scaling that responds to demand in seconds. Support for TensorRT, vLLM, Triton Inference Server, and custom serving frameworks. Latency targets as low as 50ms for real-time applications.
MLOps Pipeline Management
End-to-end pipeline orchestration using Kubeflow, MLflow, Airflow, or your preferred stack. Automated model versioning, A/B testing infrastructure, canary deployments, and rollback capabilities. Every pipeline run is logged, reproducible, and auditable for compliance.
Model Monitoring & Drift Detection
Continuous monitoring for data drift, concept drift, and performance degradation. Real-time alerting when model accuracy drops below configurable thresholds. According to industry research, unmonitored models lose 10-25% accuracy within 6 months. We prevent that.
Data Pipeline Orchestration
Manage your training and inference data pipelines from ingestion through transformation to feature stores. Support for batch and streaming architectures, Apache Spark, Kafka, and cloud-native services. Data quality checks and lineage tracking built into every pipeline.
AI Cost Optimization
Most organizations waste 40-70% of their GPU spend on idle or underutilized resources. We implement spot/preemptible instance strategies, right-size GPU allocations, schedule training jobs during off-peak hours, and consolidate inference workloads to maximize utilization.
24/7 AI Ops Support
Round-the-clock operational support from engineers who specialize in ML infrastructure. Proactive incident detection, automated remediation for common failures, and escalation paths for critical issues. Monthly operations reviews with optimization recommendations.
In-House vs. Managed AI Operations
| FACTOR | IN-HOUSE | SERAPHIM MANAGED |
|---|---|---|
| Annual team cost (3-5 MLOps engineers) | $540K - $1.25M | $36K - $180K |
| Time to operational readiness | 3-6 months (hiring) | 1-2 weeks |
| GPU utilization rate | 20-35% typical | 75%+ optimized |
| 24/7 on-call coverage | Difficult to maintain | Included in all tiers |
| Model drift detection | Build from scratch | Pre-built, continuously active |
| Scalability during demand spikes | Capacity planning required | Auto-scale in seconds |
| Vendor and key-person risk | High (single-person dependencies) | Low (team-based, documented) |
Managed AI Service Tiers
Transparent pricing that scales with your AI operations. All tiers include monitoring, alerting, and dedicated support.
AI Launchpad
For teams with 1-3 production models
- Up to 3 model endpoints
- Basic model monitoring & alerting
- Shared GPU inference cluster
- Weekly performance reports
- 99.5% uptime SLA
- Business-hours support (4hr response)
- Monthly optimization review
AI Growth
For scaling AI across the organization
- Up to 15 model endpoints
- Advanced drift detection & retraining triggers
- Dedicated GPU allocation
- Full MLOps pipeline management
- Data pipeline orchestration
- 99.9% uptime SLA
- 24/7 support (1hr response, 15min critical)
- Bi-weekly optimization reviews
AI Command
Enterprise-scale AI operations
- Unlimited model endpoints
- Dedicated GPU clusters (A100/H100)
- Custom MLOps toolchain
- On-premise or air-gapped options
- 자동화된 재학습 파이프라인
- 99.99% uptime SLA
- Dedicated support engineer (5min critical)
- Executive QBR reporting
- Compliance documentation (SOC2, ISO 27001)
Real-World Managed AI Outcomes
Fraud Detection Pipeline for SE Asian Fintech
A Singapore-based fintech running 7 fraud detection models needed to reduce inference latency from 800ms to under 100ms while handling 50K transactions per second during peak hours. Our managed service optimized their model serving with TensorRT, implemented auto-scaling GPU clusters, and set up real-time drift monitoring. Result: 94ms average latency, 99.97% uptime over 12 months, and $340K annual savings versus their previous in-house setup.
Manufacturing Quality AI
A Vietnamese electronics manufacturer deployed computer vision models for defect detection across 12 production lines. We managed the entire inference pipeline, including edge-to-cloud data sync, model updates during maintenance windows, and automated retraining when defect patterns shifted. Model accuracy stayed above 98.5% consistently.
SaaS NLP Platform
A Korean SaaS company serving enterprise customers needed to manage 23 fine-tuned language models across multiple regions. Our team consolidated their GPU spend from $180K/month to $72K/month through intelligent scheduling, spot instance strategies, and model quantization, without any degradation in inference quality.
Common Questions About 매니지드 AI 서비스
What are managed AI services?
+Managed AI services provide end-to-end operational support for your AI and machine learning systems. This includes model hosting and inference, MLOps pipeline management, model monitoring for performance drift, data pipeline orchestration, GPU cluster management, and 24/7 support. Instead of building and maintaining an internal AI ops team, you outsource the operational burden to specialists while retaining full ownership of your models and data.
How much can managed AI services reduce our operational costs?
+Most organizations reduce AI operational costs by 40-60% when switching to managed AI services. The savings come from optimized GPU utilization (typically improving from 30% to 75%+ utilization), elimination of idle compute costs, automated scaling, and not needing to hire specialized MLOps engineers at $180-250K per year. A Gartner study found that 85% of AI projects fail to reach production, often due to operational complexity that managed services solve.
What is model drift and why does it matter?
+Model drift occurs when an AI model's performance degrades over time because the real-world data it encounters diverges from its training data. Without monitoring, model accuracy can drop 10-25% within 6 months. Our managed AI services include continuous drift detection that alerts you when model accuracy falls below thresholds and can trigger automated retraining pipelines to maintain performance.
Do we retain ownership of our AI models and data?
+Yes, absolutely. You retain 100% ownership of all models, training data, and inference data. Our managed AI services operate on your infrastructure (cloud VPC, on-premise, or hybrid). We manage the operations, but all intellectual property remains yours. You can export or migrate at any time with no lock-in.
What SLAs do you offer for managed AI services?
+We offer tiered SLAs: Starter tier includes 99.5% uptime with 4-hour response time, Growth tier includes 99.9% uptime with 1-hour response time and 15-minute critical incident response, and Enterprise tier includes 99.99% uptime with dedicated support engineers and 5-minute critical incident response. All tiers include real-time monitoring dashboards and monthly performance reports.
Stop Wasting GPU Spend on Idle Infrastructure
The average organization wastes 40-70% of its AI compute budget on underutilized resources. Our managed AI services optimize every dollar while keeping your models running at peak performance. Get a free assessment of your current AI operations and see exactly where you can save.