DeepSeek integration services
We integrate DeepSeek's hosted API and deploy selected model weights for product teams with repeated reasoning, extraction and backlog workflows. We measure task quality and total operating cost, then define deployment and escalation rules.
01Capabilities
DeepSeek workflows for volume, local inference and model tiers
We connect DeepSeek to your product through AI integration work. The workload can use a reviewed hosted endpoint or a locally deployed checkpoint.
- Volume
Sorting and extraction across catalogues
We integrate sorting, triage and extraction for e-commerce product catalogues and backlogs. Workload evaluation compares accepted outputs and total cost per completed task, including retries and escalations.
- Boundary
A selected checkpoint on your infrastructure
With downloadable weights, we select the exact checkpoint and serving setup for your account or region. We review licence terms, test capacity and audit outbound data and telemetry.
- Tiering
Measured handover between models
A tested routing rule decides which work stays with DeepSeek and which goes to another provider. We measure handovers and monitor changes in the escalation rate.
- Overnight
Batch processing with throughput and quality targets
We build queued processing for archives, old tickets and document piles. The test covers throughput, total unit cost, required response times and output quality.
02Fit
Choosing a hosted API or self-hosted DeepSeek deployment
We select the hosting route against your data classification and task results, then confirm who operates and supports it.
| Your situation | What we recommend |
|---|---|
| High request volume with a defined quality and cost target | Evaluate the hosted APIEvaluate the endpoint with representative inputs and measure cost per accepted task, including an existing connector where it fits the required controls. |
| Inference must run within your own account and region | Evaluate a local checkpointReview the exact DeepSeek checkpoint, licence, hardware and serving plan. The surrounding deployment questions also apply to Llama. |
| Customer, health or financial data is in scope for hosted inference | Review endpoint policyReview the hosted service's policy against your data classification. Compare customer-controlled weights with Claude or Gemini, and obtain approval for the chosen data path. |
| Procurement requires a named supplier and defined support | Define supportConfirm the selected host's support terms, service responsibilities and escalation route. Compare providers on those requirements alongside task quality and total cost. |
| A narrow label set with representative labelled examples | Compare a classifierExisting rules or a small classifier may cover a bounded label set. Use PyTorch for a task-specific training project, and compare total cost against the hosted model. |
| A fixed graphics-card budget limits serving capacity | Test hardware fitTest memory use, quantization, throughput and answer quality at the planned load. Include suitable Qwen checkpoints in the comparison rather than assuming a model name settles capacity. |
Scope
We own the workload acceptance tests, deployment-location recommendation, measured handover rule and alerts when escalation rates change. The written deployment decision states where inference runs and how that matches the data classification. unicrew has built software since 2012 with 100+ senior in-house engineers across six countries and an ISO 27001:2022 certified information-security management system.
03Delivery
Evaluating DeepSeek quality, deployment and total cost
A model enters the workflow against agreed acceptance criteria and a documented hosting decision.
- Define accepted outputs for the taskWe build a test set from your real data with the answers you would accept. It includes difficult cases and explicit quality criteria before model and cost comparisons.
- Compare the exact model configurationsWe use the same prompts, inputs and scoring method for each selected endpoint or checkpoint. The evaluation records failures, retry and reasoning usage, review effort and total cost.
- Select the hosting and serving planWe match public, internal or regulated data to a reviewed endpoint or deployment. For model weights, the plan covers exact licence terms, serving capacity and outbound data or telemetry.
- Test the handover rule and monitor changesWe implement a measured escalation rule and alert when the handover rate moves. A change triggers investigation against output quality and the workload. Our AI QA and evaluation practice tests model, routing and deployment changes.
04Stack
What a tiered pipeline is made of
The selected checkpoint and handover rule determine the serving and packaging decisions around the model.
05Questions
Asked by the person who signs the security form
The review separates the hosted service from the exact model weights and deployment you intend to operate.
Yes. Our engineers integrate DeepSeek endpoints, deploy selected checkpoints and build the evaluated routing around them.
An architect outside the delivery team reviews the design. Our AI QA and evaluation practice evaluates the model's outputs and routing decisions against agreed tasks.
For a delivery team coordinated by unicrew, choose managed teams.
The answer depends on the exact service, data classification and deployment; hosted access and customer-controlled weights require different reviews.
DeepSeek's hosted-service privacy policy describes processing and storage of personal information in the People's Republic of China. Your reviewers need to assess the chosen service and policy against your data requirements.
We review the outbound data path and telemetry of a self-hosted deployment; downloadable weights alone do not establish isolation.
Yes, where the configured model meets the task's acceptance and operating requirements.
We test against Claude or the OpenAI API on the same task set. Familiar API formats do not establish full feature equivalence. The comparison covers:
- Accepted outputs and tool behaviour.
- Rate limits, retries and error handling.
- Total task cost and the measured handover rule.
The result can be a replacement or a tiered pipeline.
Yes, when the exact checkpoint's licence and serving requirements fit your deployment.
R1 code and weights use the MIT licence. Distilled models disclose Qwen or Llama base terms; V3 distinguishes code and model licences.
We review the checkpoint and base-model terms, then test hardware capacity, quantization and answer quality. The hosted API is a separate service and must be evaluated in its own configuration.
Retries, error review, escalations and local serving can raise the cost per accepted task.
We compare:
- Model usage, including reasoning and retries.
- Hardware, serving and operational support.
- Reviewing errors and handling escalated work.
We test the handover rule and monitor escalation rates alongside quality. A change in that rate is a signal to investigate.
We quote DeepSeek integration and deployment work against the selected endpoint or checkpoint, serving scope and evaluation needs.
- Time and materials: billed hourly, quoted per project.
- Fixed price: outcome based, quoted per project.
- Team extension: billed monthly per team member.
Most engagements start within two to four weeks.
Measure DeepSeek quality and cost before deployment
Bring the monthly volume, data classification and outputs the task must produce. We will recommend a deployment option, the evaluation to run and the handover rule to test.