Running DeepSeek as a service, Foundation Model Platforms

DeepSeek Agency Implementation, Building Productized AI Services

Learn how to architect client delivery workflows around DeepSeek's 1M-token context window and thinking modes, integrate the API into existing agency stacks via OpenAI-compatible endpoints, and structure token-based pricing into retainer and project models. This course covers model selection (Flash vs. Pro), cost optimization through context caching, and automation patterns for content, code, and support workflows.

Open the decision record for DeepSeek

What does running DeepSeek for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Vendor cost is usage-based per input/output token with off-peak rates at half of peak rates; the lowest published plan price is Not published. Budget must be derived from expected token volume and the agency's own labor rate.
Time to first value
Not published
Payback
Not modeled
Guided implementation
8 hours

Is DeepSeek worth running as a client service?

DeepSeek is a raw model API with OpenAI/Anthropic-compatible endpoints, 1M-token context, tool calls, JSON output, and Flash vision; it is viable for agencies with engineering capacity to embed it in client products. Vendor cost is usage-based per token with no published plan price, and it provides no white-label, multi-client dashboard, or agency partner layer, so client price, labor, overhead, and volume must be supplied to model ROI.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • DeepSeek prepaid account balance (usage-based, billed per input/output token)
  • API key issued at api.deepseek.com for OpenAI-format requests
  • API key or base URL for Anthropic-format requests at api.deepseek.com/anthropic
  • Client-side data source whose text/images fit within the 1M-token context window
  • Tool/function schemas for any external function the model must invoke

People and inputs

  • Developer(s) able to integrate an OpenAI- or Anthropic-compatible API
  • Process for designing prompts, JSON output schemas, and tool call payloads
  • Token usage and billing reconciliation routine against the prepaid balance
  • Test harness covering 1M-token context, 384K output, FIM completion, and Flash vision

Included with the course

7 working documents for delivering this service.

  • DeepSeek Model Selection Matrix for Client Projectsworksheet
  • Token Budget Calculator: Flash vs. Pro Pricing Scenariostemplate
  • API Integration Checklist: OpenAI-Compatible Endpoint Setupchecklist
  • Context Caching Strategy Guide for Document-Heavy Workflowsguide
  • Thinking Mode vs. Non-Thinking: Decision Tree for Client Deliverablessop
  • Retainer Pricing Template: Monthly Token Allocation and Overage Handlingtemplate
  • Tool Calling Implementation Playbook for Agency Automationsop

Listed by name. These documents are not yet published as individual downloads.