Foundation models are good at general tasks but lack your company’s data, processes and context. See 5 reasons why, and when you need to fine-tune a model

AI inference is the step where a trained model produces results from new data. Learn how inference works, how it differs from training, and how to deploy it.

Compare the top AI inference platforms in 2026: Together, Fireworks, Groq, Bedrock and GreenNode MaaS on latency, data control and pricing for APAC teams

Kimi K2 has 1T total and 32B active parameters. See model size, VRAM needs, K2.6 vs K2.7 Code, prompt format, benchmarks, and how to run Kimi via API or self-host.

ArtCraft is 7 open-source Adobe alternatives, 77% co-authored by Claude Opus 5.5. How did AI agents build it, and what can businesses learn?

Compare 3 ways to deploy AI: calling a direct model API (OpenAI/Anthropic), using MaaS, or self-hosting on GPUs, across cost, latency, data control, and ops effort.

Jev responds in 70–500ms at a fraction of LLM cost. See how it differs from LLMs, what real benchmarks show, and when to use it.

AI costs are hard to predict as you scale. GreenNode MaaS tracks tokens and cost in real time, helping businesses budget, control, and optimize AI spend transparently.

Test and compare multiple AI models before production - no code required. The GreenNode MaaS Playground helps you evaluate, tune prompts, and pick the right model faster.