This topic surveys the practical landscape of AI image generation in 2025, focusing on platform choices, MCP (Model Context Protocol) integrations, and how provider free‑tier terms affect real projects. Adoption has shifted toward multi‑provider workflows and MCP servers that standardize access to diverse image models and editing tools, making it easier to swap backends for cost, capability, or compliance reasons. Key MCP servers include: HuggingFace Spaces (mcp‑hfspace) for running Spaces models and interfacing with Claude Desktop; Azure OpenAI DALL‑E 3 MCP for text‑to‑image generation on Azure; OpenAI GPT Image MCP for generation and editing via GPT image APIs; Replicate Flux–based Image Generation MCP; Grok‑MCP for xAI’s Grok models and image analysis; Fal MCP Server for Fal.ai models (FLUX, Stable Diffusion, MusicGen); and Nanana for Google Gemini Nano Banana–powered text‑to‑image and image‑to‑image workflows. Why this matters now: by late 2025, high‑quality image and image‑editing models are widely available from hosted cloud APIs and lighter local runtimes. Free‑tier policies remain diverse—varying in request caps, rate limits, watermarking, data retention, and commercial‑use terms—so teams must consider not just model quality but usage economics and license constraints. MCP layers help manage those differences by providing a single integration surface, enabling fallback to open models (e.g., Stable Diffusion variants) when commercial quotas are exceeded and simplifying experimentation across providers. Important considerations include API latency, moderation and safety features, commercial licensing, and the operational cost of hybrid or on‑prem deployments. This overview helps engineers and product teams compare capabilities and plan for cost, compliance, and continuity in image generation pipelines.