What is Model Serving?
Model serving is the infrastructure and process of deploying trained AI models in production environments so they can receive requests and return predictions or outputs reliably, efficiently, and at scale. It encompasses the technical systems needed to make AI models available to applications and end users.
What Is Model Serving?
Model serving is the bridge between a trained AI model and the real world. It is the infrastructure that takes a model that works in a research environment and makes it available as a reliable, scalable service that applications can call upon. Without model serving, an AI model is like a brilliant employee who is never connected to a phone or email -- capable but unreachable.
When you use any AI-powered feature -- a chatbot responding to customers, an image recognition system scanning products, or a recommendation engine suggesting items -- there is a model serving system behind the scenes handling your request, routing it to the right model, managing computational resources, and delivering the result back to you.
Why Model Serving Matters for Business
Model serving is where AI transitions from experiment to business value. Many organizations successfully build or customize AI models but struggle to deploy them reliably for production use. The gap between "it works in testing" and "it works for 10,000 users simultaneously" is precisely what model serving addresses.
Key challenges model serving solves:
- Reliability: Ensuring the AI is available when users need it, with minimal downtime
- Scalability: Handling varying loads, from quiet periods to traffic spikes, without degradation
- Latency: Delivering responses fast enough that users have a good experience
- Cost efficiency: Using computational resources wisely rather than over-provisioning expensive GPU infrastructure
- Version management: Updating models without disrupting service to users
Model Serving Approaches
Managed AI APIs (Simplest) Using services like OpenAI, Anthropic, Google Vertex AI, or AWS Bedrock. The provider handles all model serving infrastructure. You simply make API calls and pay per usage.
- Best for: Most SMBs, teams without dedicated ML infrastructure expertise
- Trade-offs: Less control, data leaves your infrastructure, ongoing per-request costs
Serverless Inference Cloud platforms like AWS Lambda, Google Cloud Functions, or specialized services like AWS SageMaker Serverless Inference handle scaling automatically. Resources are provisioned only when requests arrive.
- Best for: Workloads with variable or unpredictable traffic patterns
- Trade-offs: Cold start latency (first request may be slower), limited GPU support
Dedicated Inference Servers Running model serving software (like vLLM, TensorRT, or Triton Inference Server) on dedicated GPU instances. Provides full control over performance and configuration.
- Best for: High-volume applications, latency-sensitive workloads, organizations with ML operations capabilities
- Trade-offs: Requires technical expertise, infrastructure management, capacity planning
Edge Deployment Running models on local devices or on-premises servers using frameworks like ONNX Runtime, TensorFlow Lite, or llama.cpp.
- Best for: Offline scenarios, extreme latency requirements, data sovereignty compliance
- Trade-offs: Model size limitations, harder to update, device-specific optimization needed
Key Model Serving Concepts
Load Balancing: Distributing requests across multiple model instances to prevent any single server from becoming overwhelmed. Essential for production applications with unpredictable traffic.
Auto-Scaling: Automatically adding or removing model instances based on demand. This ensures you have enough capacity during peak times without paying for idle resources during quiet periods.
Model Versioning: Managing multiple versions of a model in production, enabling A/B testing, gradual rollouts, and quick rollbacks if a new version underperforms.
Monitoring and Observability: Tracking model performance, response times, error rates, and output quality in real time. This is critical for maintaining service quality and catching issues before they affect users.
Practical Guidance for Southeast Asian Businesses
For most SMBs in ASEAN, the model serving decision is straightforward:
If you are using AI through APIs (OpenAI, Anthropic, Google), the provider handles model serving for you. Your responsibility is to build reliable application code that calls these APIs with appropriate error handling, retry logic, and caching.
If you are self-hosting models, consider these platforms:
- Ollama: The simplest option for running open-source models locally. Good for internal tools and prototyping.
- vLLM: High-performance serving for large language models, suitable for production deployments.
- AWS SageMaker / Google Vertex AI: Managed infrastructure that simplifies deployment while giving you more control than pure API services.
Key questions to evaluate your model serving needs:
- How many concurrent users will access the AI? (Determines scaling requirements)
- What response time is acceptable? (Determines infrastructure tier)
- Must data stay within specific geographic boundaries? (Determines hosting location)
- What is your budget for infrastructure and technical staff? (Determines managed vs. self-hosted)
For companies just beginning their AI journey, start with managed APIs and only invest in custom model serving infrastructure when you have clear requirements that managed services cannot meet.
Model serving is the operational foundation that determines whether your AI investments actually deliver value to users and customers. Poor model serving leads to slow, unreliable, or unavailable AI features that frustrate users and waste investment. Getting it right ensures your AI applications are fast, reliable, and cost-effective at scale.
- Start with managed AI APIs for the simplest deployment experience and only invest in custom model serving infrastructure when you have specific requirements around data privacy, latency, or cost that managed services cannot meet
- Plan for traffic variability by implementing auto-scaling and caching strategies -- AI workloads can spike unpredictably, and over-provisioning wastes money while under-provisioning degrades user experience
- Implement monitoring from day one to track response times, error rates, and costs, allowing you to identify issues before users are affected and optimize spending as usage patterns become clear
Frequently Asked Questions
Do we need to worry about model serving if we use OpenAI or similar APIs?
Not directly. When you use managed AI APIs, the provider handles all model serving infrastructure. However, you still need to build your application code to interact with these APIs reliably -- implementing proper error handling, retry logic, rate limiting, and caching. You should also monitor your API usage and costs. Think of it as the provider managing the restaurant kitchen while you still need to manage the front of house.
When should a business consider self-hosting AI models?
Self-hosting becomes worth considering in three main scenarios: when data privacy regulations require processing within your own infrastructure, when you have high enough volume that dedicated infrastructure is cheaper than per-request API pricing (typically above 100,000 requests per month), or when latency requirements demand the model be physically close to your users. Most SMBs start with APIs and graduate to self-hosting only when one of these triggers is clearly met.
More Questions
Costs vary dramatically based on approach. Managed APIs charge per request or per token, typically USD 100-5,000 per month for SMB workloads. Self-hosted serving requires GPU instances that cost USD 500-5,000 per month per server depending on the GPU type. Serverless options fall between these ranges. The right approach depends on your volume, latency needs, and data requirements. For most SMBs processing under 50,000 requests per month, managed APIs are the most cost-effective option.
Need help implementing Model Serving?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how model serving fits into your AI roadmap.