DeepSeek API on Atlas Cloud: models, pricing, and quick start

You want to call DeepSeek from your own code, and you would rather not manage a separate DeepSeek account, a separate bill, and a separate SDK on top of whatever else your product already talks to. Atlas Cloud serves the DeepSeek V4 family through a single OpenAI-compatible endpoint, so a DeepSeek call looks exactly like an OpenAI call with a different base URL. This page lists the DeepSeek models you can reach on Atlas Cloud, what each one costs per token, and a quick start you can paste into a terminal.

At a glance

Prices shown are the Atlas Cloud reference rates for the V4 family; every model shows its live price in the Playground next to its Run button, so confirm the current number there before you build a budget around it.

Which DeepSeek model to use

The DeepSeek lineup on Atlas Cloud splits cleanly by job, and picking the right one is mostly about matching token cost to task value.

• DeepSeek V4 Pro is the default for anything that matters: coding, tool calls, multi-step reasoning, and agent loops. It pairs efficient sparse attention with strong reasoning and a one-million-token context window, and at $1.68 per million input tokens it stays affordable even under steady load.

• DeepSeek V4 Flash is the workhorse for volume. At $0.14 input and $0.28 output it costs roughly a tenth of Pro on input, which makes it the right pick for summarization, classification, context compression, and any high-frequency call where top-tier reasoning is not the point.

• DeepSeek V3.2 and the V3.1 line remain available when you want a cost-sensitive general chat model or a stable prior-generation baseline to compare against. They are useful as fallback targets more than as primary picks once V4 is available.

A practical pattern is to run V4 Pro as your primary model and route cheap, high-volume work to V4 Flash, so you pay premium rates only for the calls that earn them.

The million-token context window on both V4 models is worth understanding rather than ignoring. It changes what you can pass in a single call: entire codebases, long document sets, or a full conversation history that would have needed chunking on a 128K model. That capacity does not make every call more expensive, since you pay for the tokens you actually send, but it removes the engineering overhead of splitting and re-stitching inputs. For an application that reasons over large inputs, the context window is often a bigger practical win than a marginal price difference between models.

Quick start

Atlas Cloud is fully OpenAI-compatible, so if you already use the OpenAI SDK the entire change is the base URL and the key.

First, get a key. Sign in at console.atlascloud.ai, open API Keys, and create one. Access is pay-as-you-go, so there is no deposit and no minimum, you pay per request.

Export the key as an environment variable rather than pasting it into source. This keeps it out of your code and out of version control, and it matches how the OpenAI SDK already looks for credentials, so most existing tooling picks it up with no extra change. On a server, set it once in the shell profile or the process environment; in a container, pass it as a runtime secret rather than baking it into the image.

Then confirm the exact model IDs from the live endpoint instead of trusting a hard-coded string:

textCopy
1curl https://api.atlascloud.ai/v1/models \
2  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

Now make a chat call. In Python with the OpenAI SDK:

textCopy
1from openai import OpenAI
2
3client = OpenAI(
4    api_key="your-atlas-cloud-key",
5    base_url="https://api.atlascloud.ai/v1",
6)
7
8resp = client.chat.completions.create(
9    model="deepseek-ai/deepseek-v4-pro",
10    messages=[{"role": "user", "content": "Explain sparse attention in one paragraph."}],
11)
12print(resp.choices[0].message.content)

The same request as a raw curl call, if you would rather not touch an SDK:

textCopy
1curl https://api.atlascloud.ai/v1/chat/completions \
2  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
3  -H "Content-Type: application/json" \
4  -d '{
5    "model": "deepseek-ai/deepseek-v4-pro",
6    "messages": [{"role": "user", "content": "Hello from Atlas Cloud"}]
7  }'

If the call returns a completion, you are done. An authentication error means the key is wrong or not exported; an unknown-model error almost always means the deepseek-ai/ prefix is missing or the ID is stale, which is why the /v1/models check comes first.

Why route DeepSeek through Atlas Cloud

Calling DeepSeek directly from its own API is perfectly fine when DeepSeek is the only model you will ever use. The reason to go through Atlas Cloud is consolidation: one OpenAI-compatible key and one billing account cover DeepSeek plus 300+ other models across text, image, and video. A product that starts on DeepSeek V4 for chat can later add an image model or a video model without a second vendor, a second SDK, or a second invoice.

Atlas Cloud also lists SOC II certification and HIPAA compliance on the same account, with encryption at rest and in transit, which matters once DeepSeek is serving real user traffic rather than a prototype. OpenRouter is another OpenAI-compatible way to reach DeepSeek and carries a broad model catalog of its own; the Atlas Cloud difference is the unified full-modal catalog and the listed compliance rows on one key.

The operational details matter once traffic is real. Atlas Cloud exposes per-model and per-application TPM and RPM monitoring on its enterprise tier, plus custom rate limits, so a spike in one part of your product does not silently starve another. Pairing that with a fallback model, DeepSeek V4 Flash behind V4 Pro, for example, gives you a service that degrades gracefully under load instead of returning errors to users.

Where to confirm the current numbers

Model IDs and prices move as new DeepSeek versions ship. Two sources stay current: the live https://api.atlascloud.ai/v1/models endpoint for exact IDs, and atlascloud.ai/models for per-token prices shown next to each model. Check both before you wire a specific ID or rate into production code, and the DeepSeek integration will keep working as the lineup evolves.