TL;DR: Moving ML from prototype to production needs the right tools, team, and infrastructure. This guide covers MLOps platforms, costs, and hiring in 2026.
Picking the wrong ML platform can drain $50,000+ annually through hidden costs and migration problems. We have seen this happen to several startups we work with. One client spent three months rebuilding their pipeline after choosing a tool that did not scale.
In this guide, you will learn how to build ML infrastructure that works. We cover the top MLOps tools, cloud platform costs, and how to hire the right team. This is based on our experience helping startups ship AI products across Asia.
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Quick Overview: ML Infrastructure Components in 2026
| Component | Open Source Option | Managed Option | Monthly Cost Range |
|---|---|---|---|
| Experiment Tracking | MLflow | Weights & Biases | Free to $200/user |
| Pipeline Orchestration | Kubeflow, Airflow | AWS SageMaker Pipelines | $5,000-15,000 |
| Feature Store | Feast | Tecton | Free to custom pricing |
| Model Serving | Seldon Core | Vertex AI Endpoints | $500-5,000 |
| Monitoring | Evidently AI | Arize, Fiddler | Free to $1,000+ |
The MLOps Landscape Has Changed
Over the last two years, the industry shifted from pilot projects to enterprise-grade ML systems. Three big changes happened. Feature stores became standard infrastructure. Experiment tracking expanded to cover GenAI. And specialized LLM tools entered mainstream MLOps stacks.
According to KPMG, global VC investment in AI reached $120 billion in Q3 2025. This was the fourth consecutive quarter above $100 billion. Companies are spending real money on ML infrastructure now.
What We See with Our Clients
We placed an ML engineer at a fintech startup last year. Their first task was migrating from notebook experiments to proper pipelines. It took two months. The founder told us they wished they had started with better infrastructure from day one.
Most startups we work with follow a similar pattern. They start with Jupyter notebooks and basic scripts. Then they hit scaling problems. Then they scramble to add proper tooling. You can avoid this by planning your infrastructure early.
Choosing Your MLOps Platform
You have three main options. Open source tools you manage yourself. Cloud provider platforms like SageMaker or Vertex AI. Or commercial MLOps platforms. Each has trade-offs.
Open Source Tools
MLflow is the most popular choice. It handles experiment tracking and model registry. It is free and works with any infrastructure. But it is not a pipeline orchestrator. You need to pair it with Airflow or Kubeflow for workflows.
Kubeflow is the gold standard for Kubernetes-native ML. It is powerful but complex. You need DevOps expertise to run it. Budget for 1-2 full-time Kubernetes administrators for enterprise deployments. Training costs average $2,000-5,000 per team member.
ZenML offers a modular setup that works with multiple stacks. It integrates with Kubernetes, SageMaker, Vertex AI, and Airflow. Good option if you want flexibility without the Kubeflow complexity.
Cloud Platform Comparison
| Platform | Market Share | Best For | Savings Options |
|---|---|---|---|
| AWS SageMaker | 34% | Fine-grained control, elastic scaling | Up to 64% with savings plans |
| Azure ML | 29% | Microsoft stack, regulated industries | 42% with 1-year reservations |
| GCP Vertex AI | 22% | Research, warehouse-native ML | Sustained use discounts after 25% utilization |
AWS leads with 34% market share. Their Inferentia3 chips cut inference costs by 58% with 3-year commitments. Azure dominates regulated industries with confidential computing. GCP punches above its weight in research with TPU v5p clusters.
One thing to watch with Vertex AI. It does not support scaling to zero. This means higher costs for low-usage deployments. We had a client get surprised by a $3,000 bill for an endpoint that barely had traffic.
Commercial Platforms
Weights & Biases excels at experiment tracking. Researchers love it. Team plans cost $100-200 per user monthly. Budget 20-30% extra for storage overages.
Databricks uses pay-as-you-go pricing based on compute usage. They measure in Databricks Units (DBUs). Good for teams already using their lakehouse platform.
TrueFoundry is cloud-agnostic and runs on Kubernetes. It handles both MLOps and LLMOps. Good choice if you want to avoid vendor lock-in.
Feature Stores: The New Standard
Feature stores became essential infrastructure in 2025-2026. They solve the problem of keeping features consistent between training and inference. Two main options dominate the market.
| Aspect | Feast | Tecton |
|---|---|---|
| Pricing | Free (open source) | Enterprise pricing |
| Setup | Self-deployed | Fully managed |
| Real-time Support | Standard online/offline | Sub-second freshness |
| Governance | Basic | Full lineage tracking |
| Best For | Quick implementation, fraud detection | Dynamic pricing, real-time personalization |
Feast is open source and flexible. You can plug in existing tools like Spark, Kafka, and Redis. No vendor lock-in. Good for teams that want control.
Tecton is built by the creators of Uber’s Michelangelo. Companies like PayPal, Atlassian, and DoorDash use it. The key advantage is real-time streaming. Features can be available for inference in seconds.
From Prototype to Production: A Practical Path
Here is the approach we recommend based on working with dozens of startups.
Stage 1: Early Prototype
Keep it simple. Use Jupyter notebooks for exploration. Track experiments with MLflow. Store data in your existing database. Do not over-engineer at this stage.
Cost: Nearly free. MLflow is open source. Cloud compute costs are minimal for small experiments.
Stage 2: First Production Model
Add proper pipelines. Use Airflow or Prefect for orchestration. Set up a model registry in MLflow. Deploy with a simple REST API. Add basic monitoring.
Cost: $1,000-3,000/month for compute and storage. Most of this is cloud infrastructure.
Stage 3: Scaling Up
Now you need real infrastructure. Consider Kubeflow if you have Kubernetes expertise. Or use a managed platform like SageMaker. Add a feature store. Implement proper CI/CD for models.
Cost: $5,000-15,000/month depending on scale. Factor in DevOps time for maintenance.
Stage 4: Enterprise Scale
Full MLOps stack. Multiple models in production. Automated retraining. Advanced monitoring for drift and performance. Multi-region deployment. Governance and compliance.
Cost: $20,000-100,000+/month. At this point, you need dedicated ML platform engineers.
Building Your ML Team
Infrastructure is only part of the equation. You need people who can build and maintain it. Here is what we see in the market.
Key Roles for ML Infrastructure
- ML Engineer: Builds models and pipelines. Bridges data science and engineering.
- MLOps Engineer: Specializes in infrastructure. Manages deployment and monitoring.
- Data Engineer: Handles data pipelines and feature engineering.
- Platform Engineer: Maintains Kubernetes and cloud infrastructure.
For early-stage startups, one strong ML engineer can cover multiple roles. As you scale, you need specialists.
Salary Comparison by Region
According to Second Talent’s Asia Tech Salary Index, there are significant differences across regions.
| Region | ML Engineer (Annual) | Cost vs Silicon Valley |
|---|---|---|
| Silicon Valley | $180,000-250,000 | Baseline |
| Singapore | $80,000-150,000 | 40-50% lower |
| Vietnam | $20,000-35,000 | 60-80% lower |
| Indonesia | $25,000-45,000 | 55-75% lower |
| Philippines | $18,000-30,000 | 65-85% lower |
Vietnam, Philippines, and Indonesia maintained 18-21% salary growth in 2025. But they still offer 60-70% cost savings versus US rates. We helped a SaaS startup hire three ML engineers in Vietnam for the cost of one in San Francisco.
Common Mistakes to Avoid
We see the same mistakes repeated across clients. Here are the top ones.
1. Starting Too Complex
Do not deploy Kubeflow for your first model. Start simple and add complexity as needed. One startup we know spent two months setting up infrastructure before training a single model. They ran out of runway before shipping anything.
2. Ignoring Costs
Cloud ML costs can spike fast. Set up billing alerts. Use spot instances for training. Right-size your inference endpoints. One client reduced their monthly bill from $8,000 to $2,500 by switching to spot instances and optimizing their serving infrastructure.
3. Skipping Monitoring
Models degrade over time. Data drift happens. Without monitoring, you will not know until users complain. Add basic monitoring from day one. Track prediction distributions and model performance metrics.
4. Building Everything Custom
You do not need to build your own feature store or experiment tracker. Use existing tools. Your competitive advantage is in your models and data. Not in reinventing infrastructure.
GenAI and LLMOps: New Considerations
The rise of GenAI adds new infrastructure needs. According to McKinsey, companies are rapidly adopting LLMs for various applications. This requires updated tooling.
What is Different for LLMs
- Vector stores: You need databases like Pinecone, Weaviate, or pgvector for RAG applications.
- Prompt management: Track and version prompts like you track code.
- Hallucination monitoring: New tools detect when models make things up.
- Cost tracking: API calls to OpenAI or Anthropic add up quickly.
Tools like LangChain and LlamaIndex help build LLM applications. But you still need the underlying MLOps infrastructure.
Our Recommendations by Company Stage
| Stage | Recommended Stack | Team Size | Monthly Budget |
|---|---|---|---|
| Pre-seed | MLflow + Cloud notebooks | 1 ML engineer | $500-1,000 |
| Seed | MLflow + Airflow + Basic monitoring | 1-2 ML engineers | $2,000-5,000 |
| Series A | Cloud platform (SageMaker/Vertex) + Feature store | 3-5 ML/MLOps engineers | $10,000-20,000 |
| Series B+ | Full MLOps platform + Custom tooling | Dedicated platform team | $30,000+ |
These are rough guidelines. Your actual needs depend on your product and scale. A company serving 1 million predictions per day has different needs than one serving 1,000.
Making the Right Platform Choice
If you are already invested in AWS, Azure, or Google Cloud, start with their ML platform. The integration benefits outweigh the differences between platforms.
If you want to avoid lock-in, use open source tools. MLflow for tracking. Kubeflow or Airflow for pipelines. Feast for features. This takes more work but gives you flexibility.
If you have budget but limited DevOps expertise, use managed platforms. They cost more but save engineering time. Time is usually more expensive than cloud bills for early-stage startups.
Conclusion
Building ML infrastructure in 2026 is easier than ever. Good tools exist at every price point. The key is matching your infrastructure to your stage and team capabilities.
Start simple. Use proven tools. Add complexity only when you need it. And invest in the right people. Good engineers make any stack work. Bad infrastructure choices can be fixed. But wasted time cannot be recovered.
We work with startups across Asia building AI products. The ones that succeed focus on shipping models quickly. They iterate on infrastructure as they learn. They hire talented people at sustainable costs. That combination beats having the fanciest tooling every time.
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