5 Things to Clarify Before Deploying AI Inference Infrastructure
When integrating AI into your operations or products, you don't need to finalize GPU specs or model choices from the start. Begin by clarifying what you want to achieve, your expected scale, quality requirements, data handling, and cost and operational scope. This guide walks through five key points to think through before your first consultation.
You're welcome to reach out even if not all items are decided. The examples in this guide are illustrative and do not represent completed deployments or recommended configurations.
Define your use case
Start by identifying who will use AI and for which tasks. Being specific about the input and expected output — whether answering questions, summarizing documents, or extracting information — makes it easier to think through the processing requirements and application integration.
Things to clarify
- Who will use it and for which tasks
- What information goes in and what output is expected
- What friction exists in the current workflow
Example to share in a consultation
We're exploring a system that references internal manuals to answer employee questions. Currently, each department handles inquiries individually.
Estimate scale and processing timing
Beyond the number of users, the volume of concurrent processing and peak usage periods are also useful inputs for planning. The requirements differ between real-time conversational use and batch document processing with a fixed deadline.
Things to clarify
- Number of users and expected concurrent usage
- Number of documents and typical length per document
- Processing frequency, peak hours, and target completion times
Example to share in a consultation
We expect around 50 employees to use the system. We don't yet know the concurrent usage, but we're assuming weekday business hours.
Note
User count alone is not enough to determine GPU capacity or TPM requirements. Model choice and input/output conditions also need to be considered together.
Determine quality and response speed requirements
Think through what kind of output would actually be usable in your workflow, using real input examples. Clarifying how outputs will be reviewed and how long users can wait helps define the evaluation criteria for testing.
Things to clarify
- Expected output format and content quality
- Impact of errors and how they will be reviewed
- Acceptable response latency
Example to share in a consultation
Outputs will be reviewed by a team member before use. We'd like to start by evaluating response quality and latency using real inquiry examples.
Note
AI outputs may contain errors. Depending on your use case, consider human review steps or application-level controls.
Clarify data handling and system integration
Clarify what information will be passed to the AI, who can access it, and where the results will be used. Document retrieval, access control, OCR, and connections to existing systems need to be scoped separately from the inference infrastructure itself.
Things to clarify
- Whether confidential or personal data is involved
- Requirements for storage location, logging, and retention periods
- Access permissions and how to connect with existing applications
Example to share in a consultation
We'll be working with internal-only documents. We want to maintain per-department access controls, so we'd like to start by discussing how to integrate the search layer with the inference infrastructure.
Note
For initial consultations, please share an overview of your data types and requirements rather than sending confidential documents or actual data.
Outline cost and operational responsibilities
Think beyond the initial setup to include the usage period, ongoing costs, and who will handle operations. Distinguish between infrastructure maintenance and the management of your application and business data — and clarify what you'll handle internally versus what you'd like to discuss.
Things to clarify
- Target start date and expected usage period
- Budget range and plans for future scaling
- Scope of maintenance, incident response, and data management
Example to share in a consultation
We're planning for ongoing use. We'll manage the application internally and would like to discuss the infrastructure operations. We'll review costs once the configuration and scope are clearer.
Note
Token Factory is planned as a monthly fixed-fee service for ongoing use. Pricing, availability, and scope of support are confirmed individually.
Pre-consultation planning memo
Fill in what you know. Write 'TBD' for anything undecided. Use this to organize your thoughts before reaching out.
- Business goal or task to address:
- Who will use it / estimated number of users:
- Content to process / volume / frequency:
- Required output quality / acceptable response time:
- Data involved and systems to connect:
- Target start date / expected usage period:
- Budget range:
- What your team can handle internally:
- What is still undecided:
Reach out even if the details aren't finalized.
We'll work through the requirements and next steps together, starting from your goals and challenges.