Building AI applications in 2024 looks nothing like building AI applications in 2022. The tooling has matured dramatically, lowering barriers and improving developer productivity.

Understanding the current landscape helps developers make better choices about their AI stack.

The Tool Categories

AI development tools segment into distinct categories:

Foundation model access: APIs and SDKs for accessing GPT-4, Claude, Gemini, open-source models. The basic building block.

Orchestration frameworks: LangChain, LlamaIndex, Semantic Kernel, and others for building applications that use models effectively.

Vector databases: Pinecone, Weaviate, Chroma, Qdrant for storing and retrieving embeddings. Essential for RAG applications.

Evaluation tools: LangSmith, Weights & Biases, custom frameworks for testing and evaluating AI outputs.

Deployment infrastructure: Managed services, serverless options, and infrastructure for running AI in production.

Development environments: IDEs, notebooks, and environments optimized for AI development.

Observability: Tools for monitoring, debugging, and understanding AI application behavior.

Orchestration Framework Evolution

Orchestration frameworks have matured significantly:

LangChain: The dominant framework. Comprehensive but complex. Improving developer experience but still has a learning curve. Strong ecosystem of integrations.

LlamaIndex: Focused on data ingestion and retrieval. Excellent for RAG applications. Simpler than LangChain for appropriate use cases.

Semantic Kernel: Microsoft’s entry. Strong Azure integration. Growing but smaller ecosystem than LangChain.

Custom frameworks: Many teams build their own lightweight orchestration. Simpler but requires more development.

The pattern emerging: frameworks for complex applications, direct API calls for simple ones. Framework overhead doesn’t always justify itself.

Vector Database Consolidation

The vector database market is consolidating:

Managed services dominating: Pinecone, Weaviate Cloud, and managed offerings gaining share. Operational simplicity outweighs cost.

Feature convergence: Core functionality is commoditizing. Differentiation through performance, ecosystem, and advanced features.

Hybrid approaches: Traditional databases adding vector capabilities. PostgreSQL with pgvector serves many use cases.

Cost considerations: Vector database costs can surprise. Understanding pricing models matters.

For most applications, choice matters less than it did. Focus on operational simplicity and appropriate pricing.

Evaluation Is the Hard Problem

AI evaluation tools remain the most immature category:

No consensus approach: Different teams use different methodologies. No standard practice has emerged.

Automated evaluation limitations: LLM-as-judge approaches help but don’t solve the problem. Human evaluation remains necessary for many applications.

Benchmark limitations: Academic benchmarks don’t predict production performance. Domain-specific evaluation needed.

Tooling gaps: Tools for building evaluation suites, tracking performance over time, and comparing approaches are improving but still limited.

This is where teams should invest. Evaluation capability separates successful AI applications from disappointments.

Production Deployment Patterns

Several deployment patterns are emerging:

Serverless model access: Using managed API services with serverless compute for application logic. Simplest option, scales automatically.

Dedicated inference: Running models on dedicated infrastructure. Better for high-volume, latency-sensitive applications.

Hybrid approaches: Managed APIs for some models, self-hosted for others. Balances simplicity and control.

Edge deployment: Running models closer to users for latency reduction. Increasingly viable as model efficiency improves.

The right pattern depends on volume, latency requirements, cost sensitivity, and regulatory constraints.

Observability Challenges

Monitoring AI applications poses unique challenges:

Output quality measurement: Traditional metrics don’t capture whether AI outputs are good. New approaches needed.

Cost tracking: AI API costs can be substantial. Granular cost attribution matters.

Debugging difficulty: Understanding why an AI produced specific output is harder than debugging traditional software.

Performance baseline: What’s “normal” for an AI application? Establishing baselines takes time.

Tools like LangSmith, Helicone, and custom solutions help, but the space is still developing.

Developer Experience Gaps

Despite progress, significant gaps remain:

Testing AI applications: How do you write tests for non-deterministic systems? Practices are still emerging.

Local development: Running sophisticated AI applications locally for development remains challenging.

Documentation consistency: Rapid evolution means documentation often lags. Outdated examples are common.

Learning resources: Quality learning resources haven’t kept pace with tool evolution.

Teams should budget extra time for these gaps when estimating AI development projects.

Making Technology Choices

For teams selecting AI development tools:

Start simple: Use managed services and high-level frameworks initially. Add complexity only when justified.

Preserve flexibility: Avoid deep lock-in to specific tools. The landscape is still shifting.

Invest in evaluation: Build evaluation capability early. It’s the foundation for improvement.

Focus on operations: Production operations often harder than initial development. Plan for it.

Follow the community: The most-used tools have better support, more examples, and faster fixes.

Looking Forward

The AI development tool landscape will continue maturing:

Consolidation coming: Too many players in some categories. Expect acquisitions and failures.

Platform integration: Cloud providers will absorb more tooling into their platforms.

Developer experience focus: Improving usability will differentiate surviving tools.

Evaluation evolution: Better evaluation approaches and tools will emerge.

Standardization: Common patterns and interfaces will emerge, reducing switching costs.

The chaos of early AI development is giving way to more mature, stable practices. This is good for developers and organizations building AI applications.

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Tracking the evolution of AI development tools and practices.