Building AI applications in 2020 required deep expertise and significant infrastructure investment. By 2025, sophisticated tooling has democratized AI development—though choosing the right platforms requires understanding a complex landscape.

The Platform Categories

AI development platforms segment into distinct categories:

Foundation model APIs: OpenAI, Anthropic, Google, Cohere providing access to large language models and vision systems. The simplest starting point for many applications.

Cloud ML platforms: AWS SageMaker, Google Vertex AI, Azure ML offering comprehensive ML infrastructure for training and deployment.

Open-source frameworks: PyTorch, TensorFlow, JAX for those building from scratch or needing maximum control.

Application development: LangChain, LlamaIndex, Haystack providing higher-level abstractions for LLM applications.

No-code/low-code: Platforms enabling AI application development without traditional coding.

MLOps infrastructure: Tools for model deployment, monitoring, versioning, and lifecycle management.

Each category serves different needs, skills, and use cases.

What’s Changed

The 2024-2025 evolution:

Foundation model capabilities: Models have become dramatically more capable, changing what’s possible at the application layer.

Retrieval augmented generation (RAG): Became the standard pattern for knowledge-grounded applications.

Agentic patterns: Tools supporting autonomous AI agents that take actions, not just generate text.

Multimodal by default: Vision, audio, and text integrated rather than separate capabilities.

Fine-tuning accessibility: Custom model training became easier and cheaper.

Deployment simplification: Getting models into production became less specialized.

Choosing Platforms

Decision factors for organizations:

Use case complexity: Simple applications can use higher-level tools; complex ones may need lower-level control.

Team capabilities: What skills do you have? What can you realistically develop?

Scale requirements: Prototypes have different needs than production systems at scale.

Data sensitivity: Some use cases require on-premise deployment or specific cloud regions.

Cost structure: Different platforms have different pricing models—API calls, compute time, seats.

Vendor dependencies: How much lock-in are you accepting? What’s the exit path?

The Build vs. Buy Question

Organizations increasingly face strategic choices:

Build on foundation models: Use commercial APIs for core AI capabilities. Fastest to start but ongoing costs and dependencies.

Fine-tune existing models: Customize foundation models for specific domains. Balance of control and development speed.

Train custom models: Build from scratch for maximum control. Highest investment but potential competitive advantage.

Use application platforms: Leverage pre-built AI applications. Lowest development effort but least differentiation.

The right answer depends on strategic importance of AI, available resources, and timeline requirements.

Integration Realities

AI platforms must integrate with existing systems:

Data infrastructure: Where does training data live? How does it flow to AI systems?

Application backends: How do AI capabilities connect to business logic?

User interfaces: How do users interact with AI features?

Monitoring and observability: How do you know if AI systems are working correctly?

Security and compliance: How does AI fit into existing security and governance frameworks?

Integration complexity often exceeds model development complexity. Teams that work with Team 400 frequently cite integration as their primary challenge.

Cost Considerations

AI development costs have multiple components:

Platform fees: API calls, compute, storage, licensing.

Development time: Engineering effort to build and maintain systems.

Data preparation: Often underestimated effort to prepare training and testing data.

Operations: Ongoing monitoring, maintenance, and improvement.

Opportunity cost: What else could teams be building?

The total cost often exceeds initial platform fee estimates by 2-5x when full lifecycle is considered.

My Recommendations

For organizations building AI capabilities:

Start with APIs: Use foundation model APIs initially. Faster iteration, lower risk.

Invest in data: Your data is your competitive advantage. Platform choice matters less than data quality.

Build integration capability: Strong engineering practices for connecting AI to business systems.

Plan for change: The platform landscape is evolving rapidly. Avoid over-commitment to any single vendor.

Measure carefully: Track development velocity, system performance, and costs across platforms.

What’s Coming

Platform evolution ahead:

Consolidation: Too many platforms today; expect acquisitions and failures.

Capability convergence: Features move from premium to commodity rapidly.

Enterprise focus: Platforms adding governance, security, and compliance features.

Specialization: Vertical-specific platforms for healthcare, finance, legal, etc.

Open source pressure: Strong open alternatives keeping commercial platforms competitive.

The AI development platform market will look different in two years than it does today.

---

Tracking the evolution of AI development tooling and platforms.