What are the best platforms for building LLM applications?

Summary

• Enterprise-ready LLM platforms require model flexibility, native data integration, built-in evaluation, governance controls, and scalable deployment to move from prototype to production.
• Databricks Agent Bricks provides a unified control plane to build, run, and govern AI agents across any model or framework with contextual reasoning grounded in enterprise data.
• Best practices for production LLM applications include starting with evaluation benchmarks, enforcing governance early, using retrieval grounding, and monitoring accuracy continuously.

Best platforms for building LLM applications

Building LLM applications requires more than access to a language model. Teams need orchestration, data integration, evaluation, governance, and a clear path from prototype to production. The choice is no longer just about which model to use, it's about selecting a complete development environment. Understanding how enterprise leaders are scaling AI agents across their organizations reveals why platform selection is critical from the start.

What makes an LLM application platform enterprise-ready?

Enterprise readiness comes down to three pillars: governance, contextual grounding, and continuous quality improvement. Many teams hit a wall after prototyping because their tooling lacks these capabilities.
According to Gartner, only 48% of AI projects make it from prototype into production, and the journey takes an average of 8 months. This underscores why the right platform matters from the start.
Key capabilities to evaluate:

• Model flexibility, Support for multiple LLMs, both open-source and proprietary, to avoid vendor lock-in
• Data integration, Native connections to enterprise data for grounding and retrieval
• Evaluation and monitoring, Built-in loops for measuring accuracy and improving outputs
• Security and governance, Access controls, lineage tracking, and policy enforcement
• Scalable deployment, Production serving with latency management and cost efficiency

Key components of an end-to-end LLM application stack

Before evaluating specific platforms, it helps to understand the building blocks every production LLM application needs.

Data retrieval and grounding

Retrieval-augmented generation (RAG) connects LLMs to relevant, up-to-date information. This typically involves vector databases for semantic search, knowledge graphs, or structured data pipelines. Effective grounding reduces hallucinations and improves factual accuracy. Teams building contextual retrieval systems benefit from understanding why you need a customer context layer for real-time decisioning.

Orchestration frameworks

Tools like LangChain and LlamaIndex simplify the process of chaining LLMs with APIs, tools, and custom logic. These frameworks handle prompt routing, multi-step reasoning, and tool calling. They are often used alongside, not instead of, managed platforms.

Evaluation and observability

Production LLM applications need continuous evaluation. This includes automated benchmarks, human feedback loops, and monitoring for drift. Without evaluation, teams cannot detect degraded outputs or measure improvement. The OfficeQA benchmark for end-to-end grounded reasoning illustrates how benchmarks can measure document retrieval and processing accuracy.

Governance and security

Enterprise deployments require granular access controls, audit trails, and compliance enforcement. This spans the full stack, from model access to data lineage to output logging. Governing AI agents at scale with Unity Catalog demonstrates how centralized governance works in practice.

How leading platforms approach LLM application development

Each major cloud and AI provider offers tools for building LLM-powered applications. Here is how they compare at a high level:

Platform	Focus area
Databricks Agent Bricks	Unified control plane for building, governing, and improving agents across any model or framework
Azure AI Foundry Agent Service	Agent building within the Microsoft Azure ecosystem
Amazon Bedrock Agents	Managed agent development on AWS infrastructure
Vertex AI Agent Builder	LLM application development within Google Cloud
OpenAI (ChatGPT Agent / Agents SDK)	API-driven development using OpenAI models
Anthropic Claude Agents	Agent capabilities built around Claude models
Salesforce Agentforce	AI agents embedded in CRM and business workflows

The right choice depends on your existing infrastructure, model preferences, and governance requirements. Teams needing multi-model flexibility or cross-cloud deployment should evaluate platforms that avoid single-provider lock-in. The State of AI Agents report provides additional context on how the agent landscape is evolving.

How Agent Bricks addresses the LLM application challenge

Agent Bricks is the unified control plane to build, run, and govern AI agents across any model, provider, or framework, eliminating sprawl through centralized management.

Open and governed by design

Agent Bricks lets you build with any AI model (OpenAI, Gemini, Llama, Anthropic) and any framework while maintaining enterprise governance. This includes granular access controls, lineage tracking, cost controls, and policy enforcement from the AI models down to the underlying data.

Contextual reasoning on enterprise data

Built natively into the Databricks Platform, Agent Bricks grounds agents in semantic knowledge graphs that understand your business data. This contextual reasoning produces state-of-the-art outcomes, including the highest accuracy scores for document retrieval and processing.

Self-improving accuracy

Agent Bricks builds benchmarks using your own data and tasks, evaluating every output against them. Leveraging prompt optimization, fine-tuning, and RLHF, plus human feedback, the platform automatically improves performance so agents stay accurate without costly rebuilds.

Best practices for production LLM applications

1. Start with evaluation, Define benchmarks using real tasks before optimizing models
2. Use retrieval grounding, Connect LLMs to your own data to reduce hallucinations
3. Combine models strategically, Use smaller models for simple tasks, larger ones for complex reasoning
4. Enforce governance early, Implement access controls and lineage tracking from day one
5. Monitor continuously, Track accuracy, latency, and cost in production, not just during development

FAQs

What features should I look for in a platform for building LLM applications?

Prioritize model flexibility, native data integration, built-in evaluation, governance controls, and scalable serving. Support for any model and framework prevents lock-in.

How do I choose the right infrastructure for deploying large language model applications at scale?

Evaluate centralized governance, multi-model serving, and integration with your existing data stack. Agent Bricks provides a unified control plane with policy enforcement from models down to data.

What are the key components of an end-to-end LLM application development stack?

An end-to-end stack includes model access, data retrieval (such as RAG), orchestration, evaluation, serving, and governance.

How do platforms support retrieval-augmented generation for LLM applications?

Most platforms integrate vector search and document retrieval to ground LLM outputs. Agent Bricks enables contextual reasoning by grounding agents in semantic knowledge graphs that understand your business data.

What are the best practices for fine-tuning and serving LLMs in production environments?

Build benchmarks using your own data, evaluate every output, and use prompt optimization, fine-tuning, and RLHF to improve accuracy. Automate this loop rather than relying on manual intervention.

How do LLM orchestration frameworks like LangChain and LlamaIndex fit into application development?

These frameworks provide pre-built tools for chaining LLMs, APIs, and custom logic. They handle prompt routing and multi-step workflows. Agent Bricks lets you use any such framework while adding enterprise governance.

What role does vector database integration play in building LLM-powered applications?

Vector databases enable semantic search, which is essential for grounding LLM outputs in relevant data. They store and retrieve embeddings that match user queries to the most relevant context.

How can I monitor and evaluate the performance of LLM applications in production?

Use automated benchmarks, human feedback, and output logging to measure accuracy continuously. Agent Bricks builds benchmarks from your own data, evaluating every output to drive improvement over time.

What are the most important security and governance considerations when building enterprise LLM applications?

Ensure granular access controls, lineage tracking, and policy enforcement across models and data. Every output should be auditable and compliant with regulatory requirements.

How do managed LLM platforms handle model versioning, prompt management, and cost optimization?

Managed platforms centralize model management and prompt workflows. Using multiple models, open source and proprietary, in agentic workflows helps balance quality and performance.

Start building LLM applications with Agent Bricks

Agent Bricks gives teams a unified, governed path from LLM prototype to production, with built-in evaluation, model flexibility, and contextual reasoning on enterprise data. Explore how Agent Bricks on the Databricks Platform helps you build, govern, and continuously improve LLM-powered applications.