Models catching up on benchmarks
Open releases from major labs and collectives now compete on reasoning, coding, and multilingual tasks. Distilled variants trade a small quality gap for dramatic savings in VRAM and throughput—ideal for high-volume internal tools.
The tooling maturity curve
Hugging Face, vLLM, Ollama, and MLX lowered the bar to run models locally. LoRA and QLoRA fine-tuning lets teams adapt base models with modest GPU budgets. RAG pipelines integrate cleanly with open embedding models and vector databases.
When to choose open source
- Strict data residency or air-gapped environments
- Heavy customization on proprietary terminology and workflows
- Predictable unit economics at scale
When APIs still win
Cutting-edge multimodal features, minimal ops overhead, and burst capacity without capital expense keep hosted APIs attractive for many products. Hybrid strategies—open models for bulk tasks, APIs for peak capability—are increasingly common.