Modular

Modular is the right choice when you need to run inference across NVIDIA, AMD, and Apple Silicon without rewriting code. The unified stack from kernel to cloud is genuinely unique—most vendors tie you to one GPU family. The performance claims (2x over vLLM, 70% cost savings) hold up in published case studies, especially for large MoE models like DeepSeek and MiniMax. We'd reach for this when deploying AI agents or multi-modal pipelines that need sub-500ms time-to-first-token and the flexibility to switch hardware. Where it bites: Modular is not a zero-ops solution. You'll need GPU expertise to tune custom kernels in Mojo, and the self-hosted tier requires managing your own infrastructure. The pricing can get complex—shared endpoints are per-token, dedicated are per-minute, and BYOC is sold by contract. Smaller teams may find Ollama or vLLM simpler for lightweight projects. Acquisition by Qualcomm (announced June 2026) adds credibility and resources, but also risks shifting focus away from AMD/Intel support or open-source commitments. For now, the platform remains vendor-neutral; we'll watch how that evolves. Compared to alternatives: vLLM is simpler and free but only runs on NVIDIA. Together AI offers managed inference but lacks hardware portability. Modular's advantage is clearest for enterprises with mixed GPU fleets who want to avoid lock-in. Startups with single-GPU stacks should look elsewhere. Real-world caveats: The model library is rich but not exhaustive—some niche architectures may need custom porting. The OpenAI-compatible API is convenient but may lag behind the latest OpenAI features. Mojo is still in beta; expect sharp edges for kernel development.