Post-Training is Back: How to Own Your Intelligence Like a Pro

2026-04-13 · Source: Scaling the Enterprise · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, long

Summary

The article argues that post-training open-source models are now superior to renting frontier intelligence due to cost, control, and capability. It highlights Baseten's \$1.5 billion Series F in June 2026, valuing it at \$13 billion, based on the thesis that post-trained open models offer the best combination of capability, cost, and control. Examples like Cursor's Composer 2.5 matching Claude Opus 4.7 on coding benchmarks at one-tenth the cost, Harvey's legal models achieving 11x cheaper performance than Opus alone, Ramp's expense categorization, Genspark's +12% quality and +33% tool-call volume, and Vercel's 93%+ error-free generation rate at 40x inference speed of GPT-4o-mini demonstrate this shift. The article also outlines an 8-step guide for enterprises to build their own intelligence, from data auditing to continuous learning, and previews an ecosystem of infrastructure providers like Fireworks AI, Baseten, and Modal.

Key takeaway

For AI Architects and MLOps Engineers evaluating model deployment strategies, the shift towards post-training open-source models presents a compelling alternative to costly frontier APIs. You should audit your enterprise's data assets and define private evaluation rubrics to build custom models that offer better accuracy, lower latency, and significant cost savings. Prioritize continuous learning loops to maintain a competitive advantage and own your intelligence on your terms.

Key insights

Post-training open-source models now offer superior capability, cost, and control compared to renting frontier intelligence APIs.

Principles

• Own intelligence for competitive advantage.
• Model and agent harness must co-develop.
• Private evaluation rubrics are critical.

Method

An 8-step process: audit data, select open-source foundation, build private eval rubrics, design agent harness, post-train iteratively (SFT, iterative SFT, RL), evaluate relentlessly, deploy on suitable infrastructure, and build continuous learning loops.

In practice

• Use 7-27B parameter models for domain-specific tasks.
• Filter agent traces to rubric-passing examples for SFT.
• Deploy on platforms like Fireworks AI, Baseten, or Modal.

Topics

• Post-Training LLMs
• Open-Source Models
• Enterprise AI Strategy
• AI Inference Platforms
• Model Fine-tuning
• Agentic Workloads

Best for: Director of AI/ML, AI Architect, MLOps Engineer

Related on AIssential

• The Market Map: Owning Your Intelligence — The market for enterprise AI ownership via post-training and custom models is rapidly maturing with a robust vendor ecosystem.
• Complex-IF and Beyond: Expert Rubrics for RLVR — Expert-authored rubrics significantly enhance both LLM evaluation and training for complex, agentic tasks.
• The Architecture Behind Open-Source LLMs — MoE architectures and diverse training strategies drive rapid progress in open-weight LLMs, optimizing cost and performance.
• not much happened today — The AI landscape is rapidly evolving towards specialized, agentic systems, with open-weight models gaining significant ground against proprietary solutions.
• Meta is back in the LLM game after a year-long break — Meta's Muse Spark release signals a renewed, resource-intensive push into top-tier LLMs, despite past failures and ongoing post-training challenges.
• Harvey Trains Open Source Models To Encode Law Firm Workflows — Post-training open-source LLMs on proprietary workflows and client data offers superior, secure, and customized legal AI automation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Scaling the Enterprise.