DeepSeek training, 1-on-1.Reasoning and code, low cost.
A DeepSeek expert opens your use cases with you and works through what matters: picking the right model, prompting DeepSeek-R1 for reasoning and coding, shipping on the API at low cost, and self-hosting for privacy. We start from your real tasks and your budget, not theory.
★★★★★ 4.7/5 · 300+ pros trained · France Num certified
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GeminiWe deploy DeepSeek in client stacks, not just in theory.
Most DeepSeek trainings are tutorials recorded by people who opened the model the night before. At Hack'celeration it's the opposite: routing reasoning to R1, shipping on the API, cutting token bills, self-hosting open-weight models for clients who can't send data out, that's our daily agency work. Everything we teach you, we practice on live stacks. We know the traps (R1 burning tokens on a task V3 handles, the data-residency surprise) because we've already solved them.
- We deploy DeepSeek in client stacks every week, not just in theory
- 1-on-1 format: the trainer adapts to your level, from prompt newcomer to seasoned dev
- We tell you when DeepSeek isn't the right call (sometimes GPT-4 or Claude earns its price)
- We start from your real use cases and your budget, not a dummy example
Four pillars so DeepSeek does real work at low cost.
DeepSeek used badly means the wrong model on every task, R1 burning tokens for nothing, and data sent where it shouldn't go. Most of the trouble comes from the choices around the model, not the model itself. We pick up your real use cases and work through the four pillars together.
- The DeepSeek models
From V3 to R1, and when to use each
DeepSeek ships several open-source models: V3 for general chat and fast tasks, R1 for step-by-step reasoning, and coder variants for dev work. We map each one to a real job so you stop defaulting to the biggest model. R1 shines on math, logic and multi-step problems but it spends more tokens thinking, so we show you when that trade is worth it and when V3 is plenty.
Choose my model - Reasoning and coding prompts
Prompt R1 for thinking, not just answering
R1 is a reasoning model: it works best when you let it think out loud, not when you over-constrain it like a chat model. We build your prompts for the tasks that matter (debugging, refactoring, data analysis, multi-step logic) and show you how to read the reasoning trace to catch where it goes wrong. You stop copy-pasting generic prompts and start getting answers you can trust.
See the prompts - The API and cost
Ship on the API, for a fraction of the price
DeepSeek's API is one of the cheapest credible options on the market, and it's OpenAI-compatible so it drops into your existing code. We set up your keys, pick the right model per call, and cut your bill with context caching, prompt trimming and routing cheap tasks to V3. You get the same job done for a fraction of what GPT-4 or Claude would cost.
See the API setup - Self-host and privacy
Run it on your own infra, your data stays home
The models are open-weight, so you can run DeepSeek on your own servers and keep your data off any third-party API. That matters if you're worried about sending prompts to servers in China or you have compliance constraints. We walk through self-hosting (Ollama, vLLM, a GPU box), the hardware reality, and how to wire DeepSeek into your stack alongside your other tools.
Plan my setup
Meet our trainers, leave with a plan.
Drop your email. We get back to you to connect you with a Hack'celeration-certified trainer: we look at your use cases, spot where DeepSeek can cut your AI cost without a quality drop, and tell you where to start. No commitment, even if you don't take the training.
- A diagnosis of your tasks and your current AI budget
- The first tasks to move to DeepSeek, in priority order
- The right 1-on-1 format for your level and your stack
- An honest take: DeepSeek or a premium model for your case
Your DeepSeek program, step by step.
Five steps, no skipping. Each one on your real use cases, with a clear deliverable. From the first session we map your tasks and your budget. By the end, you run DeepSeek across your work without us.
- Step 1 · Use cases and budget audit
We map your real tasks and your AI budget
First session, we look at what you actually need from a model: reasoning, coding, summarization, classification, chat. We check what you're paying today (or what GPT-4 and Claude would cost) and where DeepSeek can take over without a quality drop. We're honest about where it can't. You leave with a clear list of tasks to move to DeepSeek, in priority order, and an estimate of what you'd save. No theory, your real cases.
- Step 2 · First prompts and workflows
We get R1 and V3 working on your actual tasks
We pick the right model per task and build your first prompts. For reasoning and coding we use R1 and read its thinking trace together; for fast or simple jobs we route to V3 to save tokens. We set up reusable prompt templates for your recurring tasks, so you stop rewriting the same thing. By the end, you have working prompts on your own cases, not a generic example, and you know which model to reach for.
- Step 3 · API integration
We wire DeepSeek into your code and cut the bill
We get you shipping on the API. DeepSeek is OpenAI-compatible, so we plug it into your existing code or your no-code tools (Make, n8n) with minimal changes. We set up keys, error handling and rate limits, then attack cost: context caching, trimming prompts, routing cheap calls to V3 and reasoning calls to R1. You practice on your own integration, not a sandbox. You finish with DeepSeek live in your stack and a bill you control.
- Step 4 · Self-host and privacy
We self-host DeepSeek if your data can't leave
If you can't send prompts to a third-party API (sensitive data, compliance, the China data-residency question), we go self-hosted. We walk through running the open-weight models with Ollama or vLLM, the real hardware you'd need, and the quality trade-offs of smaller distilled versions. We wire it into your stack so it sits next to your other tools. This step is optional: if a hosted API is fine for you, we skip it and spend the time elsewhere.
- Step 5 · Autonomy
You run DeepSeek across your work without us
The number one goal: you become autonomous. By the end of the program, you know how to pick the right DeepSeek model, prompt R1 for reasoning and coding, ship on the API at low cost, and self-host if you need to. You no longer need an agency to run AI on a budget. And if you want to delegate a bigger build later, we also run a DeepSeek agency, but that's not the point here.
Why train 1-on-1 with us.
- 300+Pros already trained on AI
More than 300 people have gone through our trainings across France and Europe. Devs, founders, data and ops teams. Not vanity numbers: people who now run DeepSeek on real tasks and cut their AI bill, instead of paying premium prices for jobs a cheaper model handles.
- 4.7/5Rating across 334 verified reviews
Average rating of 4.7 out of 5, across 334 reviews. We won't pretend DeepSeek beats every model at everything: R1 is slower and the data-residency question is real. But the 1-on-1 format makes the difference in knowing exactly when DeepSeek is the smart call.
- 1:1A dedicated expert, not a class of 100
You're not a number in a webinar. A trainer opens your real use cases, looks at your stack and your budget, and works through your actual tasks. We schedule sessions around your availability, replays included.
A working agency, recognized by the French State.
Hack'celeration is certified Activateur France Numérique and holds the AI Ambassador title, both granted by France Num to organizations that genuinely drive the digital transformation of companies. On the ground, we deploy DeepSeek in client stacks every week: more than 300 pros trained and a 4.7/5 rating across 334 verified reviews, left by the people who took our programs, not just by the buyer.
- Certified Activateur France Numérique
- AI Ambassador (France Num)
- 300+ pros trained across France and Europe
- 4.7/5 across 334 verified reviews
The questions we get the most.
What is a 1-on-1 DeepSeek training?
An individual program with a DeepSeek expert, not a class of 100 people. We open your real use cases, look at your stack and your budget, and work through your actual tasks: choosing the right model, prompting R1 for reasoning and coding, shipping on the API, self-hosting if you need it. You ask your questions live, the trainer adapts the pace to your level. We schedule sessions around your availability, and you leave with concrete actions every time. That's the difference between watching a tutorial and actually running DeepSeek in your work.How much does the DeepSeek training cost?
There is no single price. We connect you with a trainer certified by Hack'celeration, matched to your need and your level. It varies from one trainer to another, based on their profile and the format that fits your project.DeepSeek or ChatGPT and Claude: which should I use?
It depends on the task. DeepSeek is open-source and far cheaper, and R1 holds its own on reasoning, math and coding against models that cost several times more. ChatGPT and Claude still lead on some nuanced writing, very long context, and the polish of their ecosystem (tools, vision, integrations). The smart move is rarely one model for everything: we help you route reasoning and bulk tasks to DeepSeek to cut cost, and keep a premium model for the jobs that genuinely need it. We're honest about where each one wins.What is DeepSeek-R1 and when should I use it?
R1 is DeepSeek's reasoning model: instead of answering straight away, it thinks through the problem step by step before responding. That makes it strong on math, logic, debugging and multi-step tasks where a normal chat model rushes and slips. The trade-off is that R1 spends more tokens and time on that thinking, so it's overkill for simple chat or quick lookups. In the training we show you how to prompt R1 to think well, read its reasoning trace to catch errors, and use the faster V3 model when you don't need the deep reasoning.Is DeepSeek safe to use with my data and the China concern?
Fair question, and the answer depends on how you run it. Using DeepSeek's hosted API means your prompts go to its servers, which raises data-residency and privacy concerns for some companies, especially around China. The key point: the models are open-weight, so you can self-host them on your own infrastructure and your data never leaves. In the training we lay out both options honestly, the trade-offs of each, and help you pick based on your real compliance and sensitivity, not on hype or fear.Can I self-host DeepSeek, and what do I need?
Yes, that's one of DeepSeek's big advantages: the models are open-weight, so you can run them on your own infrastructure. The full models need serious GPU memory, but smaller distilled versions run on a single decent GPU or even a strong laptop with Ollama, with some quality trade-off. We walk through the realistic options (Ollama for local, vLLM for serving, a GPU box or cloud instance for scale), what hardware each needs, and how to wire it into your stack. We're straight about what's worth self-hosting and what's better left on the API.How cheap is the DeepSeek API really?
Very cheap compared to the big names, often several times less per token than GPT-4 or Claude for comparable tasks, which is why it's drawing so much attention. But the real bill depends on usage: R1 spends more tokens because it reasons out loud, so a reasoning-heavy workload costs more than the headline price suggests. In the training we set up cost controls (context caching, prompt trimming, routing simple calls to the cheaper V3 model) so you get the savings in practice, not just on paper.Do I need to be technical to take the DeepSeek training?
Not for everything. Using DeepSeek through a chat interface or a no-code tool like Make or n8n needs no code, and we can start there. To ship on the API or self-host, some technical comfort helps, but the 1-on-1 format starts from your exact level: prompt newcomer, we go step by step; seasoned dev, we jump straight to the API, cost optimization and self-hosting. You learn exactly the layer you need, no more.Is the training online or in person?
100% online, over video, 1-on-1. You join the sessions from anywhere, we share your screen and your real use cases live. Sessions are recorded if you want to revisit them. The individual format means real interaction: you're not a number in a webinar of 100, the trainer answers your questions about your stack and your budget. That's what makes learning concrete on a tool that moves as fast as DeepSeek.
Your AI bill deserves to drop. Meet your trainer.
Drop your email. An expert who ships DeepSeek daily looks at your use cases and shows you where it cuts cost without losing quality. No commitment, even if you don't take the training.