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How to architect AI
for your organization

Choosing the right platform is critical. We’ve made it easy: compare tools, frameworks, and platforms to make the best decision.

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How Lamatic Supports Financial Workflows

Direct Foundation Model APIs

You integrate directly with a model provider’s API and build orchestration logic yourself.

Raw model APIs offer maximum flexibility and fast iteration with minimal abstraction. But you must handle orchestration, routing, and memory yourself, and complexity grows quickly with multi-step workflows, RAG, fallback logic, and production-grade reliability.

Best for:

Prototypes
Lightweight AI featuresEarly experimentationSmall-scale use cases
Early experimentation
Small-scale use cases

Automation Platforms

No-code or low-code workflow automation platforms with AI integrations.

Automation platforms are quick to deploy with pre-built connectors and low engineering overhead. But they lack AI-native abstractions, struggle with long-term state, and become brittle when scaling to AI product features, stateful agents, or large data pipelines.

Best for:

Internal automation
Marketing workflows
AI triggers inside business processes
Non-technical teams

DIY AI Middleware

Developer frameworks that provide modular components for building AI pipelines and orchestration logic.

Custom AI stacks offer high flexibility, modular architecture, and fine-grained control with custom routing logic. But they come with heavy maintenance, version drift, infrastructure management, and DevOps overhead—often breaking down for small teams, fast go-to-market needs, or long-term scale.

Best for:

Engineering-driven teams
Custom AI infrastructure
Deep architectural control

Hyperscaler AI Platforms

Cloud-native AI services are integrated into enterprise cloud ecosystems.

Enterprise platforms provide strong governance, security frameworks, cloud-native integration, and dedicated support. But they introduce vendor lock-in, complex setup, higher costs, and require cloud expertise—making them less suited for startup agility, cross-cloud flexibility, or rapid experimentation.

Best for:

Enterprise IT environments
Security and compliance-heavy industries
Large-scale deployments

AI-Native Platform-as-a-Service

A managed AI orchestration layer designed specifically for building AI applications — without managing raw infrastructure.

AI infrastructure platforms offer built-in orchestration, model-agnostic workflows, and reduced infrastructure overhead through higher-level abstractions. They come with an opinionated architecture, less customization than DIY stacks, and reliance on the platform’s abstraction layer.

Best for: