What Is AI Compute Capacity Planning? Budget Your Coding Agent Infrastructure

Capacity Planning: Not Just for Infrastructure Teams

AI compute capacity planning is the process of forecasting how much computational resources your team will need to run AI coding agents, and budgeting accordingly. Traditional capacity planning focused on servers and databases. Modern capacity planning must account for a new, variable-cost resource: LLM inference tokens.

For teams using API-based models (Claude, GPT, Gemini), capacity planning means predicting monthly token consumption and budget. For teams running self-hosted models, it means calculating GPU requirements. Either way, the goal is the same: ensure your team has enough AI compute to work productively without overspending or hitting unexpected limits.

The Three Variables of AI Coding Capacity

Every AI coding capacity plan revolves around three variables:

• Developers (D): Number of team members using AI coding tools daily
• Tasks per developer per day (T): How many AI-assisted tasks each developer completes (typically 15-50 for active users)
• Tokens per task (K): Average input + output tokens per task (ranges from 5K for simple completions to 200K+ for complex agent workflows)

Monthly token demand = D × T × K × 22 working days. For a team of 10 developers, each doing 30 tasks/day at 50K tokens/task average: 10 × 30 × 50,000 × 22 = 330 million tokens/month.

Budgeting by Model Tier

Using the 330M tokens/month example (60% input, 40% output split), here is what that costs across model tiers:

The 66x cost difference between DeepSeek Flash and Claude Opus highlights why capacity planning cannot be model-agnostic. Your choice of model is the single largest lever on your AI compute budget.

Capacity Planning for Self-Hosted Models

If your team runs open-source models on owned or rented GPUs, capacity planning shifts to hardware:

• Model size: A 70B parameter model needs ~35GB VRAM (4-bit quantized). One A100 (80GB) or two A6000 (48GB each) handles this.
• Concurrent users: Each concurrent inference request needs KV-cache memory. Budget 2-4GB per concurrent user for a 70B model.
• Throughput target: A single A100 serves roughly 30-50 tokens/second for a 70B model. For 10 concurrent developers, you need 2-3 GPUs.
• Cloud GPU cost: A100 instances cost $2-4/hour on AWS/GCP. Three instances running business hours (10h/day, 22 days) = $1,320-$2,640/month.

Compare this to the API cost table above. Self-hosted 70B models deliver quality between Sonnet and Opus at a cost comparable to Gemini Pro API — but require engineering effort to maintain.

Building Your Capacity Plan: Step by Step

A practical capacity planning process for AI coding teams:

• Week 1-2: Measure baseline. Track actual token usage per developer using provider dashboards or gateway logs. Most teams overestimate by 2-3x until they measure.
• Week 3: Identify patterns. Which tasks consume the most tokens? Which developers use the most? Are there spikes around deadlines?
• Week 4: Set budgets. Based on measured data, set per-developer monthly caps with 20% buffer. Implement alerting at 80% threshold.
• Monthly: Review and adjust. Token usage grows as teams become more proficient with AI tools. Plan for 10-20% monthly growth in the first year.

Common Capacity Planning Mistakes

Avoid these errors that lead to budget overruns or artificial constraints:

• Planning from theoretical maximums: "Each developer could use 1M tokens/day" leads to absurd budgets. Use measured actuals with 20% buffer.
• Ignoring prompt caching: If 60% of your tokens are repeated system prompts, caching reduces effective cost by 50%+ without reducing capacity.
• Single-model planning: Using Opus for everything is like using a sports car for grocery runs. Route simple tasks to cheaper models to stretch your budget 3-5x.
• Setting caps too low: Overly restrictive budgets cause developers to ration AI usage, defeating the productivity purpose. The ROI threshold is typically 3-5x: if $1 of AI saves $3-5 of developer time, keep spending.

Use our AI Cost Estimator to model your team's capacity needs across different project types and model configurations, then set appropriate monthly budgets.