Deployment

How to Deploy DeepSeek on Google Cloud or AWS

Chat Deep AI October 19, 2025 25 min read

Deploying DeepSeek models on cloud platforms can unlock powerful AI capabilities for advanced applications. DeepSeek Chat and DeepSeek Coder are state-of-the-art large language models from DeepSeek AI, designed for general conversational reasoning and code generation respectively. They are open-source (free for research and commercial use), with model checkpoints ranging from billions to hundreds of billions of parameters. This guide focuses on inference serving (not training) for both DeepSeek Chat and DeepSeek Coder, covering manual GPU-based deployment on AWS and Google Cloud, as well as fully managed serving via AWS Bedrock and Google Vertex AI.

We’ll provide step-by-step instructions, compare deployment options, and discuss cost, scalability, security, and best practices.

Overview of DeepSeek Chat vs. DeepSeek Coder

DeepSeek Chat is a general-purpose conversational LLM built with advanced reasoning in mind, capable of chain-of-thought “thinking mode” analysis for complex queries. It excels in multi-turn dialogue, creative writing, and multilingual understanding (supporting 100+ languages). DeepSeek Coder, on the other hand, is specialized for programming tasks. It was trained on 2 trillion tokens (87% code and 13% natural language) across dozens of languages, with context window up to 16K tokens for project-level code completion and infilling. Multiple model sizes are available (e.g. ~6.7B, 33B parameters) to suit different requirements. Both models’ weights are openly available (e.g. via Hugging Face) for self-hosting. In practice, DeepSeek Chat provides human-like conversational responses with strong reasoning, while DeepSeek Coder offers state-of-the-art performance on coding benchmarks. Next, we’ll explore how to serve these models on AWS and GCP.

Manual Deployment on AWS EC2 (GPU VM)

Deploying DeepSeek on an AWS EC2 instance with a GPU gives you full control over the environment and model configuration. This approach is ideal for developers who need custom setups or want to minimize ongoing costs for continuous heavy usage. Below are detailed steps:

Prerequisites: You’ll need an AWS account with permission to launch GPU instances, plus basic familiarity with Linux shell. It’s recommended to use an Ubuntu 20.04/22.04 AMI or AWS’s Deep Learning AMI (which comes with NVIDIA drivers and frameworks pre-installed). Ensure your AWS region has available GPU quotas.

Step 1: Launch a GPU EC2 Instance

Choose Instance Type: From the EC2 console, click “Launch Instance.” Select an Ubuntu 20.04 LTS (or AWS Deep Learning AMI) as the base image. For the instance type, pick a GPU-equipped instance like g4dn.xlarge, g5.xlarge, or higher depending on model size (e.g. A10G or V100/A100 GPUs). For example, a g4dn.xlarge (NVIDIA T4 with 16 GB VRAM) can handle a ~7B model, whereas a larger model (30B+) may require an A100 40/80GB or multiple GPUs.
Storage and Networking: Allocate sufficient EBS storage (at least 50–100 GB) to accommodate model files and libraries. Configure a Security Group to allow SSH (port 22) and any port you’ll use for your API (e.g. port 5000 or 8000). Attach or create an SSH key pair for access.
Launch and Connect: Launch the instance and connect via SSH using the key. For example: ssh -i YourKey.pem ubuntu@<EC2_PUBLIC_IP>.

Step 2: Set Up GPU Drivers and Environment

If you chose the AWS Deep Learning AMI, NVIDIA drivers and CUDA should already be installed. Otherwise, you must install them manually:

Install NVIDIA CUDA drivers (if needed): Update packages and use NVIDIA’s repo to install drivers (on Ubuntu). For example:

sudo apt update && sudo apt install -y build-essential wget 
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-repo-ubuntu2004_11.8.0-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004_11.8.0-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub
sudo apt update && sudo apt install -y cuda

Reboot or load NVIDIA modules to enable the GPU.

Install Python and ML Libraries: Ensure Python 3 is available (sudo apt install -y python3-pip). Upgrade pip and install key libraries: PyTorch (with CUDA support) and Hugging Face Transformers. For example:

pip3 install --upgrade pip  
pip3 install torch transformers accelerate

This will get the essentials for model inference. Optionally install FastAPI and Uvicorn if you plan to serve an API (pip3 install fastapi uvicorn).

Note: If using an AWS Deep Learning AMI, these steps are simplified – most drivers and frameworks are already present. You can simply create a Python virtual environment and ensure the Transformers library is up-to-date.

Step 3: Download DeepSeek Model Weights

With the environment ready, obtain the DeepSeek model weights. The official DeepSeek models are hosted on Hugging Face Hub, which makes downloading easy:

Choose the model variant: For DeepSeek Chat, you might use the latest available checkpoint (e.g. deepseek-ai/DeepSeek-R1-Chat or DeepSeek-V2-Chat). For DeepSeek Coder, you can choose an instruct-tuned code model like deepseek-ai/deepseek-coder-6.7b-instruct or the larger 33b if you have a powerful GPU. Ensure the model size fits in your GPU memory (a 6.7B model requires ~16 GB GPU RAM in BF16, while a 33B model may need ~65+ GB or multi-GPU).

Download via Transformers: Hugging Face Transformers can automatically download the model. In a Python session or script, you can load the model which will trigger a download to the local cache (be patient, these files are tens of GB). For example:

from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "deepseek-ai/deepseek-coder-6.7b-instruct"  # or another DeepSeek model ID
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, 
                                            torch_dtype="auto").cuda()

This will download the tokenizer and model weights to /home/ubuntu/.cache/huggingface by default. We set trust_remote_code=True because DeepSeek may use custom model code on Hugging Face. We also load the model to GPU (.cuda()), using an appropriate torch_dtype (float16/BF16 for efficiency).

Alternative: If you prefer, you can use the Hugging Face CLI or Git LFS to pull the model files directly. For example: huggingface-cli install deepseek-ai/deepseek-coder-6.7b-instruct. Then use the local path in from_pretrained.

Tip: DeepSeek model weights (especially chat models like V2) can be very large (the DeepSeek-V2 Chat model is 236B parameters, MoE-based, requiring 8×80GB GPUs for full load). For single-GPU deployment, stick to smaller variants or community-distilled versions (e.g. an 8B distilled DeepSeek-R1 model). You can also consider quantizing the model (4-bit or 8-bit) using libraries like bitsandbytes to reduce memory usage, at some cost to speed/accuracy.

Step 4: Run Inference Locally for Testing

After loading the model, test a simple inference to ensure everything is working:

prompt = "Hello, can you explain the theory of relativity in simple terms?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

This should print a generated answer from the DeepSeek model. DeepSeek Chat models support very large context windows (up to 128K tokens in some versions), but keep the prompt small for quick tests.

If you loaded a DeepSeek Coder model, you can also test it with a coding prompt. For instance, using the chat formatting as in the model card example: supply a conversation with a user message asking for code, then generate the assistant’s answer.

Step 5: Expose an API Endpoint (Serving Inference)

To use DeepSeek in a real application, you’ll want to serve it behind an API. A simple approach is to wrap the model in a web service:

FastAPI + Uvicorn: Create a small FastAPI server that listens for requests and returns model outputs. For example:

from fastapi import FastAPI
app = FastAPI()

@app.post("/generate")
async def generate(prompt: str, max_tokens: int = 200):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=max_tokens)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"response": result}

Save this as app.py and run with Uvicorn: uvicorn app:app --host 0.0.0.0 --port 5000. Ensure your EC2 security group allows port 5000. Now you have an HTTP endpoint (POST /generate) to get model inferences. This is a basic example – you should add authentication and request validation in production.

Hugging Face TGI: For more robust serving, consider using Hugging Face’s Text Generation Inference (TGI) server. DeepSeek models support TGI for optimized GPU utilization. You can run the official Docker image for TGI with the DeepSeek model ID, which provides a high-performance, production-ready REST API and gRPC endpoint out of the box.

Scaling on AWS: If expecting high load, you can run multiple EC2 instances and put them behind an Application Load Balancer, or use an Amazon EKS (Kubernetes) cluster to manage replica pods of this service. Make sure to implement caching for repeated prompts if applicable, as well as autoscaling policies to spin up more instances during peak demand.

Pros of Manual AWS Deployment: You have full control over the model and environment – you can fine-tune or customize the model if needed, choose specific hardware, and potentially reduce cost for sustained throughput. Your data stays within your controlled instances. For example, hosting a 7B model on a g4dn.xlarge (~$0.526/hour) can be cost-effective for continuous use.

Cons: You are responsible for maintaining the server (updates, security patches), scaling it, and handling failures. Inference speed may be lower compared to specialized serving infrastructure, especially for very large models that require multi-GPU or model parallelism.

Manual Deployment on Google Cloud (GCP Compute Engine)

Deploying DeepSeek on Google Cloud’s Compute Engine is similar to AWS EC2. GCP allows you to launch VM instances with GPUs and set up the environment for model serving. Here’s how to do it:

Prerequisites: A Google Cloud project with billing enabled, and quotas for GPUs in your desired region (enable the Compute Engine API and request a GPU quota if not already available). Install gcloud CLI for convenience, or use the GCP Console UI.

Step 1: Launch a GPU VM on Compute Engine

Create the VM: In the Google Cloud Console, go to Compute Engine > VM Instances and click “Create Instance.” Choose a machine type and attach a GPU. For example, you might use an n1-standard-8 with 1×NVIDIA T4 (gpuType: T4) or a more powerful A100 (on a2-highgpu-1g for 1×A100). Be sure to disable Secure Boot (required for NVIDIA driver installation) when using GPUs.
Use a Deep Learning VM Image: For the boot disk, select Deep Learning VM (DLVM) image for PyTorch. Google offers these images with GPU drivers and ML libraries pre-installed. For example, pick “Common frameworks: PyTorch 1.x with CUDA 11.* (Ubuntu 20.04)”. This saves setup time – the DLVM comes with NVIDIA drivers, CUDA, Python, PyTorch, etc., ready to go.
Configure instance details: Add at least 100 GB of disk space (to hold models). Open a firewall port (e.g. 5000) in the Networking settings for your VM if you plan to serve an API externally, similar to AWS steps. Tag the instance and apply a firewall rule allowing traffic on that port.
Launch and Connect: Create the instance. Once running, connect via SSH (using the Cloud Console SSH or the gcloud CLI: gcloud compute ssh your-instance-name). You should see a greeting that confirms GPU drivers are installed.

Step 2: Environment Setup on GCP VM

If you used the Deep Learning VM, most dependencies are present. Still, verify that you have the latest transformers library and that the GPU is accessible:

Check GPU: Run nvidia-smi to ensure the NVIDIA driver is working and the GPU is visible.

Python environment: The DLVM has a base Anaconda environment. You can use that or create a new virtual env. Install/upgrade Hugging Face Transformers and any other packages (FastAPI, etc.) as

needed:

pip install --upgrade transformers accelerate fastapi uvicorn

(PyTorch should already be installed on the DLVM. If not, install a CUDA-enabled PyTorch.)

Download model weights: The process is the same as on AWS. Use the Hugging Face Hub to download the model. Ensure your VM has internet access to fetch the weights. You might choose a smaller DeepSeek model unless your GCP GPU has large memory. For instance, on a T4 (16 GB), use a 6.7B model or a quantized larger model. On an A100 (40 GB), you could try the 33B model in 8-bit mode. Run the Python snippet to load the model as shown earlier. The Hugging Face from_pretrained call will handle the download and caching.

Step 3: Running Inference and Serving on GCP

Test the model locally in the same way as AWS to verify it’s generating responses. Then set up an API endpoint:

FastAPI on Compute Engine: Similar FastAPI code can be used. Make sure to allow the traffic through Google Cloud’s firewall (either by configuring the VM’s network tag with a firewall rule or opening it for all if testing). Use Uvicorn to run the server on 0.0.0.0. You might also consider using Cloud Run or Google Kubernetes Engine if you want a more managed way to serve containerized models, but note that Cloud Run has limited GPU support (currently only CPU or limited GPU in specific regions in preview). For straightforward GPU usage, running directly on the VM is simplest.
Scaling on GCP: For higher availability, you can use Compute Engine’s Managed Instance Groups to replicate your VM and put them behind a load balancer. Alternatively, integrate with Vertex AI endpoints by deploying a custom model to a Vertex Prediction service (which manages scaling for you, but this is similar to AWS SageMaker – beyond our scope here).

Manual GCP Deployment Pros: Like AWS, you get full control. Google’s GPU VMs are performant and the Deep Learning VM images ease the setup with pre-configured drivers. You can keep all data within your project and customize the environment or even fine-tune models if needed.

Cons: You manage the infrastructure. If the instance crashes, you need to intervene. Scaling up and down is manual unless you script it. Costs run as long as the VM is up (even if idle), so for spiky workloads you must remember to shut down instances to save money.

Managed Deployment on AWS Bedrock

AWS Bedrock is a fully managed service that provides access to foundation models via API without needing to deploy your own servers. Amazon has integrated DeepSeek models into Bedrock, allowing you to use them as a service. This is ideal for enterprise scenarios or quick integration when you don’t want to handle infrastructure. There are two ways to leverage Bedrock for DeepSeek:

Using AWS-Hosted DeepSeek Models (Bedrock MaaS)

AWS offers certain DeepSeek model versions as managed APIs. For example, DeepSeek-R1 (an earlier chat model) was the first to launch on Bedrock, and more recently DeepSeek-V3.1 is available with improved reasoning and “thinking mode” capabilities. These models are hosted by AWS – you simply make API calls and are billed per token.

Getting Started: Make sure Bedrock is enabled for your AWS account (it became generally available in 2025). No provisioning is needed; you will use AWS’s endpoints. Key steps:

Access via AWS Console: You can test the DeepSeek model in the Bedrock Playground. Go to the Amazon Bedrock console, open the Playground for Text generation. Select the model – choose the “DeepSeek” category and pick a model like DeepSeek-V3.1. You can then input prompts in the UI and even toggle “Model reasoning mode” on/off to see chain-of-thought reasoning before final answers. This mode is unique to DeepSeek, providing explainable step-by-step solutions.

API Integration: For production use, call Bedrock via AWS SDK/CLI. Bedrock supports both an InvokeModel API (single prompt-response calls) and a Converse API (for multi-turn chat sessions) for models that support it. Using the AWS CLI, an example invocation might look

like:

aws bedrock invoke-model \
  --model-id deepseek-v3_1 \
  --body '{"inputText": "Explain the significance of the number 42."}'

(The exact model-id or ARN can be found in AWS’s documentation or the console; DeepSeek models might be identified by names like DeepSeek-R1-0528 or DeepSeek-v3.1.)

Bedrock handles scaling: The model is serverless from the user perspective – AWS will allocate the necessary GPU resources behind the scenes. You just pay per request (see Cost section below).

Security & Monitoring: By using Bedrock, you inherit AWS’s enterprise-grade security features for this servic. You can use IAM to control access, and Bedrock Guardrails to filter sensitive or unwanted content automatically. This is great for production where moderation and compliance are needed.

Using DeepSeek on Bedrock is extremely simple – no setup beyond AWS config. However, you are limited to the model versions AWS provides. Currently those include general models (Chat-style) like R1 and V3.1, which do support code generation within their capabilities, but not a separate “Coder” model. The integrated DeepSeek-V3.1 is quite capable at coding tasks too, having excelled in code benchmarks per AWS’s announcement, so it may suffice for many coding use cases.

Importing Custom DeepSeek Models into Bedrock

AWS Bedrock also introduced custom model import, which lets you bring your own model weights (for supported architectures) and have Bedrock host them in the same serverless manner. This is a powerful option if you want to deploy, say, the DeepSeek Coder 33B model (which AWS doesn’t natively offer as of now) or a distilled version of DeepSeek Chat with your own fine-tuning.

High-Level Workflow:

Prepare model artifacts: Download the model weights (as in the manual steps) – for example, a DeepSeek-R1 distilled 8B model from Hugging Face. Make sure you have all required files (model binaries, tokenizer, config, etc.).
Upload to S3: Put the model files in an S3 bucket in your account. Use an efficient method (the files can be large, use aws s3 cp or the console).
Import via Bedrock Console or API: In the Bedrock console, use “Import Model” and provide the S3 path and an IAM role that grants Bedrock access to that bucket. Bedrock will then load your custom model and make it available as an endpoint, just like the built-in ones. (Alternatively, you can call the CreateModel API with boto3 to achieve the same programmatically.)

Once imported, you can invoke your custom DeepSeek model through Bedrock’s API. Bedrock handles provisioning GPUs for it on-demand. Note: imported models currently run with On-Demand mode (pay per minute of usage per model copy), so costs can accumulate if the model stays loaded for long periods. Be sure to reference AWS’s documentation for custom model pricing and supported architectures.

Pros of Bedrock: No infrastructure to manage – truly serverless usage of DeepSeek. You get auto-scaling, high availability, and fast integration with other AWS services. For example, you can pipe Bedrock outputs directly into AWS Lambda or workflow automation. Also, AWS’s close partnership with DeepSeek ensures optimized performance (Bedrock claims certain models run faster on AWS’s infrastructure than elsewhere). Moreover, data is not used to retrain models and stays within your AWS environment with robust privacy (important for enterprises).

Cons: The cost per token can be higher than running your own instance if you have high volume (since you pay a premium for convenience). Also, you have less flexibility – you cannot customize the model’s responses beyond what the model provides (though you can do prompt engineering). Fine-tuning the model weights is not available on Bedrock for DeepSeek (aside from techniques like distillation outside Bedrock). Lastly, availability of models is at AWS’s discretion (e.g. if a new DeepSeek version comes out, you wait for AWS to add it, or import it yourself). Weigh these factors based on your use case.

Managed Deployment on Google Cloud Vertex AI

Google Cloud’s Vertex AI offers a similar Model-as-a-Service capability via its Model Garden. DeepSeek models are available as fully managed, serverless APIs on Vertex AI. This means you can call DeepSeek through Vertex AI without provisioning any servers, and Google handles scaling and serving.

Available DeepSeek Models on Vertex: As of 2025, Google provides at least DeepSeek R1 (0528) and DeepSeek-V3.1 through Vertex AI. These are the same model family as discussed, offered under Google’s partnership terms (DeepSeek is a third-party model on GCP). Each model comes with a model card in the Vertex Model Garden that you can reference for details and terms.

Using DeepSeek via Vertex API:

Vertex AI UI: You can find DeepSeek models in the Vertex AI Model Garden on the Google Cloud Console. For example, search for DeepSeek-V3.1 and open its page. There, you may have an option to try it out with a prompt in the UI and see the result. This is great for quick testing.

API Request: To integrate into your apps, use Vertex’s predict endpoints. Vertex assigns a model name for the endpoint. According to Google’s docs, for instance use the model name "projects/*/locations/us-west2/models/deepseek-v3.1-maas" when making requests, or simply specify model="deepseek-v3.1-maas" if using their Python SDK. Similarly, DeepSeek R1 is invoked with "deepseek-r1-0528-maas". Using curl, a request might look like:

curl -X POST -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d '{"instances":[{"prompt":"<your prompt>"}]}' \
     https://us-west2-aiplatform.googleapis.com/v1/projects/<PROJECT>/locations/us-west2/publishers/google/models/deepseek-v3.1-maas:predict

(The exact endpoint and format may vary; Google provides client libraries that simplify this.)

Features: Vertex AI allows streaming responses via Server-Sent Events for chat models, which DeepSeek supports. Streaming can significantly improve perceived latency by sending tokens as they are generated. You can enable this in the API call if needed (e.g., in the SDK you might set stream=True).

Integration: Since Vertex AI is a Google service, it integrates with GCP’s ecosystem. You can secure the endpoint with AI Platform’s access control, monitor usage, and even set up** Vertex Pipelines** or Cloud Functions to call the model. Vertex also supports prompt tuning and other features for some models, though it’s unclear if prompt tuning (adapter fine-tuning) is enabled for DeepSeek yet.

Tool Use: DeepSeek-V3.1 has the concept of “thinking mode” and tool usage (the model can output reasoning), but in Vertex’s API you typically get the final answer. If the model supports functions or tools, you would see structured output (this might be an area to watch in Vertex updates).

Considerations: Google emphasizes responsible AI as well. For production, you should monitor outputs. Google provides Model Armor for Vertex AI – a system to filter or moderate prompts/responses for safety. It’s recommended to integrate such safeguards if deploying to end-users, similar to AWS’s guardrails.

Vertex AI Pros: Like Bedrock, no infra management – just an API call. It’s easily scalable (Google manages the load). The pay-as-you-go model means you can start without upfront costs and scale as needed. Vertex also has a user-friendly platform if you want to experiment or evaluate models side-by-side.

Cons: Costs can accumulate per request (discussed below). You are also subject to Google’s service terms for these models, which might impose certain usage restrictions. Additionally, if your application requires the model to be on a private network or on-prem, Vertex might not fit that need (though you could consider Anthos or GKE On-Prem with your own deployment in such cases). Lastly, like Bedrock, you can’t customize the base model weights on Vertex’s managed service – you get what the model is (though Vertex does allow custom model deployment on your own endpoints if needed).

Cost Considerations

When deciding between manual vs managed deployment, cost is a crucial factor:

AWS EC2 / GCP VM Costs (Manual): You pay for the compute instance by the second/hour. For example, an AWS g5.xlarge with one A10G GPU might cost around ~$0.80/hour in US regions, and a p4d.24xlarge (8×A100) can be ~$32/hour. GCP’s A100 instances (e.g. a2-highgpu-1g) are on the order of ~$2–$3/hour. If running 24/7, even a single GPU instance could be hundreds to thousands of dollars per month. However, you have the flexibility to shut down when not needed, and the cost is fixed regardless of how many requests you send (useful if you have high volume usage). Also consider storage and data egress costs if applicable.
Managed Service Costs (Bedrock & Vertex): These typically charge per token of input/output. For instance, Amazon Bedrock’s pricing for DeepSeek-R1 is about $0.00135 per 1k input tokens and $0.0054 per 1k output tokens. That means a prompt with 2,000 tokens and a 1,000-token answer costs roughly $0.0081. DeepSeek-V3.1 might be priced a bit higher given its size (those rates are an example; always check the latest pricing page). Google Vertex AI’s pricing is similar in spirit, perhaps on the order of fractions of a cent per 1000 characters (they often quote per character for generative models). For example, one source mentions Vertex Gen AI pricing around $0.00003 per 1K input characters and $0.00009 per 1K output characters for some models (specific rates for DeepSeek may differ). Managed services also may charge for idle usage in certain modes: AWS Bedrock custom model usage is billed by the minute of model loaded time, and Vertex endpoints charge an hourly rate if you deploy a model to a private endpoint node.
Which is cheaper? It depends on usage patterns. If you have a low-volume app (e.g. a few thousand tokens per day), using Bedrock/Vertex will be very cheap (pennies) compared to keeping a GPU server running (which might cost dollars even idling). Conversely, for extremely high volumes (say millions of tokens per day), a dedicated GPU might turn out cheaper per token. Example: 1 million output tokens on Bedrock DeepSeek might cost ~$5.4 (using the $0.0054/1k rate), whereas running a single high-end GPU for an hour ($2-$3) could generate a similar amount of tokens if fully utilized. At scale, self-hosting might save costs, but requires engineering effort to optimize GPU usage.
Hidden costs: Don’t forget data transfer costs – sending prompts and receiving answers over the network (especially if clients are not in the same region) could incur bandwidth fees on cloud platforms. With manual deployment, if you host in one region and serve globally, you might pay egress charges. With managed APIs, data egress is usually negligible for text, but if your prompt/response sizes are huge (remember DeepSeek can handle 100k+ context), those token costs and bandwidth can add up.
Cost management tips: Use autoscaling or serverless options to ensure you’re not paying for idle GPUs. Both AWS and GCP have ways to schedule instance uptime (e.g., shut down at night if not needed). For Bedrock/Vertex, set usage quotas or budget alerts. If you anticipate steady heavy use, AWS offers Provisioned Throughput discounts on Bedrock (with committed hourly rates), and GCP might have enterprise agreements for Vertex AI usage.

Scalability and Performance Tips

Serving large models requires careful planning to ensure low latency and good throughput:

Vertical Scaling (bigger machines): DeepSeek models, especially the chat model with hundreds of billions of parameters (MoE), may require multiple GPUs or nodes. On your own infrastructure, you can use model parallelism libraries (like Hugging Face Accelerate or DeepSpeed) to split the model across GPUs. Ensure your software (Transformers, etc.) supports sharded loading. The DeepSeek team recommends using vLLM or optimized inference code for their 236B model, so leverage those when possible.
Horizontal Scaling (more replicas): For handling more concurrent requests, run multiple model instances. Each GPU can only handle so much token generation per second. You might dedicate one GPU per process and run N processes on N GPUs (or even load the model multiple times on a single GPU if memory allows to utilize spare compute). Using a distributed batch inference approach (where you batch multiple requests together) can dramatically improve throughput with GPUs – libraries like vLLM or TGI do this automatically.
Batching and Async: If you roll your own server, implement asynchronous processing and batch small requests together. For example, if 10 users ask something at the same time, you can concatenate their prompts into one batch and run a single forward pass (if using the same model settings). This achieves higher GPU utilization. The trade-off is added latency for each user, so tune batch sizes carefully.
Optimizations: Use half-precision (FP16 or BF16) for inference – this is standard and supported by DeepSeek models. You can also try 8-bit or 4-bit quantization for faster inference and lower memory, at some quality cost. Compiling the model with TorchScript or using ONNX Runtime or NVIDIA’s TensorRT can yield speed-ups if you have time to optimize.
Caching: Many applications see repeated queries. Implement a cache for recent prompts or results (with proper validity checks) to avoid recomputation. If using Bedrock or Vertex, you might cache at the application layer since each call costs money; ensure you’re not sending the exact same prompt repeatedly if it can be served from memory.
Autoscaling: For manual deployments, set up cloud auto-scaling triggers (CPU/GPU utilization or queue length) to launch additional instances when load spikes. For managed services, concurrency scaling is automatic up to limits – be mindful of quotas (e.g., Vertex might have a default QPS limit you need to request increases for, and Bedrock has per-region TPS quotas).
Testing: Benchmark your setup. Record the latency and throughput at various loads. For example, the Medium blog in our references measured costs and latencies after deploying on SageMaker and suggests running a benchmark script to gather stats. This can inform you how to adjust instance types or max tokens per request for optimal performance.

Security Best Practices

Whether you deploy manually or use a managed service, serving a powerful LLM like DeepSeek requires attention to security and responsible AI practices:

API Security: If you expose a REST endpoint on a VM (EC2/Compute Engine), do not leave it open to the internet without protection. At minimum, use an authentication mechanism (API keys, OAuth, etc.) so only authorized clients can query the model. Use HTTPS (you can put Nginx or a Cloud Load Balancer with TLS in front of your instance) to encrypt traffic.
Network isolation: Place your VM in a private subnet or behind a firewall if possible, especially for internal enterprise deployments. On AWS, Security Groups and possibly a PrivateLink or VPC Endpoint for Bedrock can ensure traffic doesn’t go over the public internet. On GCP, you could use VPC Service Controls with Vertex AI to restrict who can call the model API.
Data Privacy: DeepSeek models are hosted either in your own environment or by the cloud provider. If using Bedrock/Vertex, understand their data policies. Typically, inputs are not stored or used to retrain models, but you should not send highly sensitive data to any external service without encryption and necessary agreements. For self-hosted, ensure your storage (like model files on disk or any logs) is secure and not publicly accessible.
Resource access: When setting up cloud resources for custom deployments, use least-privilege IAM roles. For example, if your model needs to load from S3, the IAM role should only have access to that specific bucket path. If using SageMaker or custom Bedrock imports, lock down the bucket permissions to your service role.
Input Sanitization: Clients might send prompts that attempt to prompt the model to do undesirable things (prompt injection attacks). Consider filtering or sanitizing inputs. For instance, you might disallow obviously malicious instructions or extremely long inputs that could affect performance. Both AWS and GCP provide tools for this: AWS Bedrock has Guardrails which can automatically screen and redact sensitive info or block harmful content, and GCP’s Model Armor can perform a similar role for prompts/responses. Leverage these in managed services, or implement your own moderation layer when self-hosting (e.g., use an open-source content filter to post-process model outputs).
Monitoring and Logging: Keep an eye on your deployment. Enable logging of requests and responses (without logging sensitive data) to track usage patterns. Monitor for any abuse or anomalies – e.g., a sudden spike in requests could indicate a DoS or an unintended usage. On AWS, CloudWatch can track Bedrock API calls and errors; on GCP, Cloud Monitoring does similarly for Vertex. For manual servers, implement logging and possibly an alert if the process crashes or CPU goes high.
Patching and Updates: If self-hosting, regularly update the environment for security patches. The OS should be updated and unnecessary services disabled. If a new version of DeepSeek model comes out with improvements (or critical bug fixes), plan how to update your deployment (A/B testing new model versions if in production). For managed services, AWS/GCP will handle model updates on their side, but you should read update notices (for example, if they deprecate an older model or change the API version).

By following these practices, you can serve DeepSeek robustly and securely, whether on your own instances or via cloud APIs. Remember that with great power (an advanced AI model) comes great responsibility to use it ethically and safely.

Manual vs. Managed: Which Deployment to Choose?

Finally, let’s compare when each approach makes sense, and recommendations for different users:

Individual Developers / Small Teams: If you are experimenting or building a prototype, using managed services like Vertex AI or Bedrock can be the quickest path. There’s no setup, and costs can be negligible for light use (often under free trial quotas or just a few cents). For example, you could integrate DeepSeek’s API in your app in an afternoon. If you need a specific model capability not offered (e.g. the code-specialized DeepSeek Coder), you might opt to run a smaller model on a single cloud VM to get that functionality. But generally, for quick results, managed wins for solo devs.
Startups / Projects in Development: Startups need to balance cost and agility. In early stages, managed deployment is attractive – you get scalability without DevOps. As your usage grows, keep an eye on cost: it might reach a point where hosting a model on a dedicated instance (or fine-tuning a smaller model) is cheaper. A common pattern is to begin with Bedrock/Vertex for speed, then transition to self-hosting if the monthly bill for API usage becomes significant. Startups with ML expertise might jump to self-hosting sooner to allow more customization (e.g., integrate DeepSeek into their own stack, add domain-specific fine-tuning). Also consider hybrid: use managed for baseline features, and supplement with a self-hosted model for specialized tasks.
Enterprise and Production at Scale: Enterprises often prioritize security, reliability, and support. Managed services (AWS Bedrock, especially) are tailored for this: you get things like audit logs, IAM integration, and 24/7 support from the provider. If you need to comply with regulations (HIPAA, GDPR, etc.), using the cloud provider’s managed model service might simplify compliance since the providers often certify their services for certain standards. Enterprises also benefit from the tooling – e.g., Bedrock Guardrails for policy compliance, or Vertex’s data governance integration. On the other hand, some enterprises with large AI teams may choose to deploy models in their own VPC or on-prem for maximum control (especially if they have sensitive data that cannot go to third-party services). In that case, they might containerize DeepSeek and deploy on Kubernetes or even use AWS’s NVIDIA Inferentia/Trainium chips for cost efficiency. It really depends on the organization’s priorities. Generally, if an enterprise is already an AWS shop, Bedrock’s DeepSeek offering is a compelling option to get started quickly and scale safely.

In summary, manual vs managed is not an all-or-nothing choice. You can start managed, then move to manual as needed, or use managed for one part of your application and manual for another. DeepSeek’s open availability gives you this flexibility – unlike closed models, you’re not locked in to one platform. As one engineering blog noted, there’s a “spectrum of deployment strategies” ranging from easy Bedrock usage to fully custom EC2 setups, and you should choose the path that fits your team’s expertise and use-case requirements.

Conclusion

Deploying DeepSeek Chat and DeepSeek Coder on AWS or Google Cloud can be achieved in multiple ways, each with its own advantages. If you need maximum control or want to run the specialized code model, manual deployment on GPU VMs (EC2 or GCE) is the way to go – you download the open-source checkpoints and run them with frameworks like Transformers, exposing your own API. This gives flexibility to optimize and perhaps lower costs at scale, but requires managing the infrastructure. If convenience and fast time-to-market are top priorities, managed services like AWS Bedrock and Google Vertex AI offer DeepSeek models as a service – simply call an API and leverage powerful reasoning and coding capabilities in your applications without worrying about servers. Many organizations might even adopt a hybrid approach over time.

By following the detailed steps and best practices outlined above, you can confidently deploy DeepSeek for inference serving in a production-grade environment. Always keep in mind cost implications, scalability planning, and security measures as you integrate these advanced AI models into your products. DeepSeek’s impressive reasoning abilities (e.g. chain-of-thought explanations) and code generation skills can be a game-changer for developer productivity, analytics, and more – and now you have a roadmap to bring those capabilities to your preferred cloud platform, whether through your own GPU instances or fully managed AI services. Good luck with your DeepSeek deployments, and happy building!