AI/LLM Infrastructure Landscape

Executive Overview - Market Categories

AI Applications for Developers
Complete suite of AI-powered developer tools spanning the entire software development lifecycle. From code generation and testing to deployment and monitoring, these applications augment developer capabilities with autonomous agents and intelligent assistants that integrate directly into existing workflows. The fastest-growing segment with over 21 distinct categories addressing every aspect of modern software development.
2
Agent Infrastructure
Orchestration frameworks, execution environments, and tooling that enable autonomous agents to interact with the world and complete complex multi-step tasks. Includes browser automation, code sandboxes, and low/no-code platforms for rapid agent development.
3
Retrieval & Data Layer
RAG infrastructure, vector databases, search APIs, and memory systems that enable contextual AI with access to proprietary and real-time data. The knowledge foundation for intelligent applications and long-term agent memory.
4
LLM Infrastructure
Critical platform services for evaluation, routing, and gateway management ensuring reliable LLM operations at scale.
5
Foundation Models
The core AI models that power the entire ecosystem - from large language models to vision, audio, and multimodal systems developed by leading AI labs.
6
Core Model & Serving
Infrastructure for model inference, fine-tuning capabilities, and local deployment. The computational backbone for running AI at scale.
7
Custom Models
Specialized AI models optimized for specific tasks like OCR and code editing, going beyond general-purpose LLMs.
8
RL Environments & Data
Reinforcement learning environments, data labeling, and benchmarking platforms that enable AI training and evaluation at scale.

AI/LLM Infrastructure Landscape

Category Breakdown - 23 Segments

AI Applications for Developers
1
IDE Codegen
Editors that generate/edit code with repo context
2
CLI Codegen
Background tasks that plan, run, and open PRs
3
AI QA/Testing
E2E + visual + API tests run by agents
4
AI SRE/Observability
Alert handling, RCA, remediation
5
Code Review
PR reviewers with suggestions
6
Documentation
AI-native docs + automation
7
Support Assistants
Code/doc-aware Q&A
8
Site Builders
AI-powered website and app builders
9
AI SDK
SDK infrastructure for AI apps
Agent Infrastructure
9Orchestrators
Glue for tools, retrieval, and models
10Low/No-code
Fast iteration for PMs/teams
11Tool-use/MCP
Standardize tool access and auth
12Browser Execution
Safe web automation environments
13Code Runtime
Sandboxes for AI-generated code
Retrieval & Data Layer
14RAG Infra
Transforms docs/APIs into retrievable context
15Vector DBs
Core substrate for similarity search
16Search APIs
Live/web knowledge for agents
17Memory Layers
Persistent long-term memory systems
LLM Infrastructure
18Evals & QA
Test/compare prompts and models
19Routers/Gateways
Policy, fallback, analytics across providers
Foundation Models
21LLM Models
Core language models from leading AI labs
22Vision/Image
Image generation and understanding models
23Audio/Voice
Speech synthesis and audio generation
Core Model & Serving
24LLM Inference
Optimized text model serving infrastructure
25Multimodal Inference
Vision and multimodal model serving
26Fine-tuning
Customize models for specific tasks
27Local LLMs
Run models on your own hardware
Custom Models
28OCR
Specialized document processing
29Code Editing
Purpose-built for code modifications
RL Environments / Data
30Data Labelling
Human-in-the-loop data annotation
31RL Training Envs
Simulated environments for agent training
32Benchmarks
Model evaluation and comparison platforms

AI/LLM Infrastructure Landscape

Company Mapping - Market Players

AI Applications for Developers
1IDE Codegen
3AI QA/Testing
6Documentation
7Support
8Site Builders
9AI SDK
Agent Infrastructure
11Low/No-code
12Tool-use/MCP
14Code Runtime
Retrieval & Data Layer
Foundation Models
Core Model & Serving
Custom Models
28OCR
29Code Editing
RL Environments / Data
30Data Labelling
31RL Training Envs
Mechanize Fleet Preference Model