LLMs From Scratch vs Surge AI
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | LLMs From Scratch | Surge AI |
|---|---|---|
| Primary Offering | Book/tutorial to build a GPT-like LLM in PyTorch | Human feedback platform for RLHF & evaluation |
| Price | ~$40 (Manning book) | Custom (contact sales) |
| Expert Labor | Not applicable | Curated workforce of doctors, lawyers, engineers |
| Hands-on Coding | Full code examples in PyTorch | No; uses SDK/API to collect data |
| Target User | Developers, researchers, students learning LLM internals | AI labs, safety teams, enterprise AI builders |
| Latest Benchmark | Not applicable | Antidote leaderboard (expert-graded), Riemann-bench (<10% frontier score) |
Surge AI and LLMs From Scratch serve fundamentally different needs. Pick Surge AI if you need expert human feedback for RLHF or rigorous model evaluation; it's a service, not a tutorial. Choose LLMs From Scratch if you want to understand and build an LLM yourself via hands-on PyTorch code. They are complementary—use Surge for data after you've built your model.
Learn to build a GPT-like LLM from scratch with PyTorch and Sebastian Raschka's hands-on book.
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Expert human feedback and benchmarks for frontier AI alignment, RLHF, and red teaming
Visit WebsiteWhat real users say: LLMs From Scratch vs Surge AI
Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.
LLMs From Scratch
49 mentions across 3 sources · 73% positive
Hacker News, GitHub, Lemmy
What users praise
- • Unmatched depth in explaining transformer internals with code and illustrations
- • Hands-on approach: build and train a GPT-like model from scratch on your laptop
- • Cryptography: clear, step-by-step construction of multi-head self-attention and transformer blocks
- • Strong focus on 'why' behind architecture, not just 'how' to use APIs
What frustrates them
- • High skill barrier: requires intermediate Python and deep learning knowledge
- • Time-consuming to work through fully—not a quick read
- • Some chapter code lacks reproducibility, breaking the 'follow-along' experience
- • Assumes PyTorch comfort; non-PyTorch users face extra friction
Researched Aug 14, 2026
Surge AI
47 mentions across 3 sources · 30% positive — critical
Hacker News, YouTube, Lemmy
What users praise
- • Expert workforce (doctors, lawyers, engineers) for nuanced feedback, widely respected.
- • Proprietary benchmarks like GDP.pdf and HANDBOOK.md are cited by major labs.
- • Strong backing from founder Edwin Chen, who scaled to $1BN+ revenue without funding.
- • Covers RLHF, red teaming, and multimodal labeling for frontier AI needs.
What frustrates them
- • Very few community reviews; most sentiment is from founders' promotion, not user experience.
- • Pricing is contact-only and likely expensive, excluding startups and individuals.
- • Learning curve is steep; requires advanced ML knowledge and enterprise context.
- • Not self-serve; buyers must engage sales, which slows evaluation.
Researched Aug 21, 2026
Who should pick which
- Frontier AI lab fine-tuning a new LLMPick: Surge AI
Needs expert human feedback for RLHF and advanced benchmarks like Antidote or Riemann-bench. Surge provides domain experts and rigorous evaluation.
- Deep learning engineer learning transformer internalsPick: LLMs From Scratch
LLMs From Scratch offers hands-on PyTorch code from tokenization to training, ideal for understanding LLMs bottom-up.
- AI safety team conducting red teaming with domain expertsPick: Surge AI
Surge’s curated workforce of doctors, lawyers, and engineers provides the nuanced adversarial testing needed for safety.
- Student building a small GPT as a capstone projectPick: LLMs From Scratch
The book’s step-by-step approach with code is perfect for a self-contained project; no need for costly human feedback.
- Enterprise building multimodal AI for PDF understandingPick: Surge AI
Surge’s GDP.pdf benchmark and expert labelers can handle complex, real-world document tasks that require domain knowledge.
Frequently Asked Questions
LLMs From Scratch vs Surge AI: which should you choose?
Surge AI and LLMs From Scratch serve fundamentally different needs. Pick Surge AI if you need expert human feedback for RLHF or rigorous model evaluation; it's a service, not a tutorial. Choose LLMs From Scratch if you want to understand and build an LLM yourself via hands-on PyTorch code. They are complementary—use Surge for data after you've built your model.
Can I use Surge AI to learn how to build an LLM?
No. Surge is a service for collecting expert human feedback; it does not teach model architecture or training code.
Does LLMs From Scratch include RLHF data collection?
It covers RLHF basics at a conceptual level, but does not provide a workforce for collecting human feedback – you'd need to simulate or use a platform like Surge.
Which tool is cheaper?
LLMs From Scratch is a one-time book cost (~$40). Surge AI requires contacting sales and is typically expensive, suited for funded teams.
Can I evaluate my model's performance with LLMs From Scratch?
The book includes basic evaluation and generation quality metrics, but not expert-graded benchmarks like Surge’s Antidote.
Does Surge AI provide any code or model architecture?
No, Surge provides an SDK/API to interact with its platform. It does not teach you how to build a model from scratch.
Is LLMs From Scratch suitable for beginners?
No, it assumes solid Python and deep learning fundamentals (PyTorch). Beginners may struggle.
Can I use Surge AI for simple sentiment analysis?
Not recommended – Surge is optimized for complex, reasoning-heavy tasks. Simpler tasks are better served by cheaper platforms.
Does Surge AI have a free tier?
No, pricing is custom and requires contacting sales. There is no free self-serve tier.
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Last reviewed: July 3, 2026