The DeepSeek agency.Frontier AI, far less cost.
DeepSeek runs open weights at a fraction of frontier cost, but aimed at the wrong tasks the saving never lands. We route it where it wins, self-host it when your data must stay put, and wire it in with cost controls.
★★★★★Verified Trustpilot reviews · AI, automation & growth agency
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GeminiA DeepSeek agency cuts your AI bill, without losing control.
Anyone can sign up for the API. Routing it well, integrating it cleanly, self-hosting where your data needs it, and keeping the cost predictable is a different job. Here are the four things we own.
- Model fit & routing
DeepSeek where it wins, frontier models where it counts
Sending every call to a frontier model is how you burn budget on tasks that don't need it. We map your workload and use DeepSeek where it's strong (high-volume work, and R1 for reasoning at a fraction of frontier cost), then route the hardest calls to a frontier model. You pay for top-tier quality only on the small slice that actually needs it, and slash the cost of the rest.
See a typical setup - API integration
Wired into your product, stack and back-office
DeepSeek's API is OpenAI-compatible, so it's close to a drop-in swap, but a clean integration is still its own job. We wire it into your product, your internal tools and your automations, handle retries, fallbacks and streaming, and set the routing layer so you can switch models without rewriting your app. The point is DeepSeek working inside your systems, not a script someone ran once.
See the integrations - Self-hosting & data residency
Open weights on your infra, sensitive data stays put
DeepSeek ships its models with open weights, so you can self-host instead of sending data to a public API. That matters: DeepSeek is a Chinese provider, so for sensitive or regulated data we recommend running the open weights on your own infra (EU or on-prem) so nothing leaves your perimeter. We'll be honest about when self-hosting beats the public API for you, and when it doesn't.
See the method - RAG, agents & ops
Assistants grounded on your data, with controls
A cheap model is only useful if it answers from your reality. We build RAG pipelines and agents grounded on your own data, with monitoring and cost controls so usage stays predictable and you can see exactly where tokens go. We're an automation and AI agency first, so this plugs into your tools and workflows instead of becoming another silo nobody maintains.
See AI enablement
We put DeepSeek to work like a cost lever, not a science project.
Most DeepSeek rollouts go one of two ways: every call still hits a frontier model so the saving never lands, or sensitive data goes to a public API nobody vetted. So we treat it like infrastructure: workload mapped, calls routed to the right model, self-hosted where your data needs it, and watched with cost controls so the savings are real and predictable.
- Audit · map your AI workload and where cost or data residency actually hurts
- Route · DeepSeek where it wins, frontier models for the hardest calls
- Build · API integration, RAG and agents, self-hosted when your data needs it
- Operate · monitoring and cost controls so usage stays predictable as you scale
We're model-agnostic, on purpose.
We don't sell a partner tier. We use DeepSeek for cost and throughput, frontier models for the hardest 5%, and we route between them, so we set DeepSeek up the way it actually pays off: right model per task, self-hosted where your data needs it, monitored. And we'll tell you honestly when its public API is the wrong call for your data and you should self-host instead.
- We're model-agnostic: DeepSeek for cost and throughput, frontier for the hardest 5%, and we route between them instead of betting your stack on one vendor.
- Honest on data: DeepSeek is a Chinese provider, so for sensitive data we'll tell you to self-host the open weights rather than hit the public API.
- You leave autonomous: the routing layer and pipelines live in your stack, so your team owns the setup and the cost controls without us.
- No partner badge. We're judged on whether your AI bill drops while quality holds, not on a vendor tier we get to display.
DeepSeek at the core, your stack and controls around it.
We configure the parts that turn cheap tokens into reliable, governed AI, then connect them to how your team works. Here's what a real rollout covers.
- Setup
OpenAI-compatible API integration
We wire DeepSeek's OpenAI-compatible API into your product, back-office and automations, with retries, fallbacks and streaming, so it's close to a drop-in swap done properly.
- Setup
Model routing (DeepSeek + frontier)
We set up a routing layer that sends each call to the right model: DeepSeek for cost and throughput, R1 for reasoning, a frontier model for the hardest few percent.
- Setup
Self-hosting / on-prem deployment
We run DeepSeek's open weights on your own infra (EU or on-prem) so sensitive data never leaves your perimeter, sized to your traffic, with honest advice on when it's worth it.
- Setup
RAG on your data
We build retrieval pipelines so DeepSeek answers from your real documents and systems, not generic guesses, with the grounding and evaluation that keep answers trustworthy.
- Setup
Cost & usage monitoring
We add monitoring and cost controls so you see exactly where tokens go, catch runaway usage, and keep the bill predictable as you scale across teams.
- Setup
Automation (n8n / Make + DeepSeek)
We plug DeepSeek into n8n or Make so it powers real automations: triage, drafting, classification, enrichment, inside the workflows your team already runs.
We map your AI cost, you leave with a plan.
Before quoting anything, we take 60 minutes to look at your AI workload, where you're spending, and what data you're sending. You leave with an honest read on what DeepSeek cuts, what to route where, and whether you should self-host for your data. Zero pitch, just a frank take on your AI bill.
- An honest read on where DeepSeek cuts your cost
- What to route to DeepSeek and what to keep on a frontier model
- Whether your data needs self-hosting or the API is fine
- The integration, RAG and agents worth building
How we run a DeepSeek rollout.
Five steps, in order. We don't move workloads onto DeepSeek before we've checked the data, we don't ship a setup with no cost controls, and your team owns it at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · AI cost audit
Map your workload and where cost actually hurts
We look at your AI workload: what each call does, the volume, and how much you're spending where. We flag the tasks paying frontier prices for work DeepSeek handles fine, the reasoning that needs R1, and the small slice that genuinely needs a frontier model. We also flag anything with sensitive or regulated data, because that changes whether the public API is even an option.
- Step 2 · Routing & integration
Route to the right model and wire DeepSeek in
We set up the routing layer (DeepSeek for cost and throughput, R1 for reasoning, frontier for the hardest few percent) and wire DeepSeek's OpenAI-compatible API into your product and tools, with retries, fallbacks and streaming. Because it's close to a drop-in swap, you can change models later without rewriting your app, so you're never locked to one vendor.
- Step 3 · Self-host where it matters
Run the open weights when your data needs it
For sensitive or regulated data we deploy DeepSeek's open weights on your own infra (EU or on-prem) so nothing leaves your perimeter, sized to your real traffic. We're honest about the trade-off: self-hosting is right for data residency and scale, the public API is fine for low-stakes work. We tell you which fits which workload instead of pushing one to look impressive.
- Step 4 · RAG & agents
Ground it on your data and put it to work
We build RAG pipelines so DeepSeek answers from your real documents and systems, and agents for the repetitive work that eats time: triage, drafting, classification, enrichment. Everything plugs into your tools through n8n, Make or direct integrations, with the grounding and evaluation that keep answers trustworthy rather than confidently wrong.
- Step 5 · Monitor & hand over
Set cost controls, then hand it over
We add monitoring and cost controls so you see exactly where tokens go, catch runaway usage, and keep the bill predictable as you scale. The routing layer, pipelines and dashboards live in your stack, so your team owns it. If you want to go deeper, we cover model routing and self-hosting end to end so you can tune it yourself as your workload grows.
We're judged on the bill that drops.
No partner badge to display, so we lead with what matters: feedback from the teams whose DeepSeek setup we ran, and whether the savings held while quality stayed put. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The routing layer and pipelines live in your stack, owned by your team
- Sensitive data self-hosted, never sent to a public API
- Cost controls wired in so the savings are real and predictable
- Trustpilot reviews come from the teams we set it up for
The questions we get asked on repeat.
What does a DeepSeek agency actually do?
A DeepSeek agency puts DeepSeek's open-weight models to work to cut your AI cost while keeping control. We map your workload, set up model routing (DeepSeek where it wins, a frontier model for the hardest calls), wire its OpenAI-compatible API into your product and tools, build RAG and agents on your data, and self-host the open weights when your data needs to stay put. The point is a lower, predictable AI bill with quality intact, not a tool nobody maintains.How much does a DeepSeek rollout cost?
It depends on scope: a simple API integration is nothing like setting up routing, RAG, agents and a self-hosted deployment. We don't throw out a flat package. We start with a free 60-minute audit to find where DeepSeek actually cuts your cost, then quote a fixed scope. DeepSeek's own pay-per-token API you pay to DeepSeek directly (it's much cheaper than frontier models); we set up the usage and controls so the bill stays predictable.Is DeepSeek safe for sensitive or regulated data?
Through the public API, not for everything, and we'll say so plainly. DeepSeek is a Chinese provider, so for sensitive or regulated data we recommend self-hosting the open weights on your own infra (EU or on-prem) so nothing leaves your perimeter. The public API is fine for low-stakes work where cost and speed matter more than residency. We tell you which path fits which workload instead of pushing one because it's easier to sell.DeepSeek or a frontier model: which should we use?
Both, and that's the point. We're model-agnostic. DeepSeek is strong on cost and throughput, and R1 is a chain-of-thought reasoning model that competes with frontier reasoning at a fraction of the cost. We route high-volume and reasoning work to DeepSeek, and keep a frontier model for the hardest few percent where its edge is worth the price. You pay top-tier rates only where they actually move the needle.Can you integrate DeepSeek into our existing stack?
Yes, and it's easier than most expect. DeepSeek's API is OpenAI-compatible, so it's close to a drop-in swap, but a clean integration still needs routing, retries, fallbacks and streaming done right. We wire it into your product, your back-office and your automations through n8n, Make or direct integrations, behind a routing layer so you can switch models later without rewriting your app. You're never locked to one vendor.What is R1 and when is it worth using?
R1 is DeepSeek's chain-of-thought reasoning model: it thinks step by step before answering, which makes it strong on harder, multi-step problems, and it competes with frontier reasoning models at a much lower cost. It's worth it when a task genuinely needs reasoning (analysis, multi-step logic, tricky extraction) rather than a quick answer. We route reasoning-heavy work to R1 and keep the general, high-volume calls on V3, so you're not overpaying either way.Should we self-host DeepSeek or use the API?
It depends on your data and your scale, and we'll be honest about it. Self-hosting the open weights on your own infra (EU or on-prem) is the right call for sensitive or regulated data and for high, steady volume. The public pay-per-token API is fine for low-stakes work and spiky traffic, with no infra to run. We size both options against your real workload and tell you where the line is, instead of defaulting to whichever is simpler for us.How long does a DeepSeek rollout take?
For a scoped setup (audit, routing, API integration), count 2 to 4 weeks: audit and routing first, then a clean integration and cost controls. Adding RAG, agents and a self-hosted deployment runs longer. We split it into batches so you get a cheaper, predictable bill fast, rather than waiting on a big rollout before anything moves. Each batch ships with its monitoring so you can see the savings as they land.
Stop overpaying for AI. Route it right.
A 60-minute audit, your AI workload mapped, a plan with the routing, self-hosting and cost controls baked in. If your team can run it in-house after setup, we'll hand you the playbook. If we're the right fit, we handle it.