Local model costs include compute, model serving, knowledge processing, operations, and quality acceptance. Define the business task, quality threshold, response-time target, and peak demand before comparing total costs over the same period. Hardware purchase price is one part of that budget.
This method applies to Ollama and other local inference services connected to FastGPT. Capabilities, memory requirements, and throughput depend on the model, quantization, context, concurrency, and runtime version. Measure them on the intended hardware.
Build a complete cost table
| Cost item | Data to collect | Calculation approach |
|---|---|---|
| Compute and storage | Purchase and depreciation or rental, model storage, and business data | Normalize to the same monthly or annual period |
| Power and facilities | Power draw, operating hours, electricity price, and hosting | Use actual operating conditions |
| Knowledge processing | OCR, embedding, reranking, and rebuilding frequency | Track service usage or local resource consumption |
| Software and operations | Licensing, deployment, monitoring, on-call work, upgrades, and recovery | Combine direct fees and labor |
| Quality and rework | Data preparation, evaluation sets, human review, and retries | Convert observed work into cost |
| Peak capacity and resilience | Spare capacity, failover, and parallel environments | Include these in the operating budget |
Monthly total cost is the sum of the monthly amounts. Cost per accepted task is monthly total cost divided by completed tasks that pass quality acceptance. When that count is zero, report total cost and failure causes while resolving usability.
Verify integration first
Test authentication, model identifiers, request formats, and streaming through the actual network path. Add required tool calls, image inputs, or other capabilities. Check both the connection from FastGPT to the inference service and the complete user conversation to locate network, gateway, and model-response problems.
Ollama's OpenAI-compatible interface has a documented support scope. Its documentation uses a Modelfile num_ctx setting for context length. FastGPT's maxContext is an application-side budget, so check both configurations separately.
Context defaults depend on the Ollama version and runtime environment. Explicitly set and record context length, output budget, and concurrency during cost measurements. Retain the model name, version, and quantization so results can be reproduced.
Measure representative load
Include short questions, long-document questions, multi-turn conversations, and required tool scenarios. Record input and output length, time to first output, complete response time, failures, memory or VRAM use, and device utilization. Measure bulk knowledge ingestion separately so contention with online requests remains visible.
Begin with one request and increase concurrency gradually while observing latency, queues, and errors. Size capacity for business peaks and record idle costs during quieter periods. Test long contexts and cold starts separately because they affect resource use and waiting time.
Compare at equivalent quality
Evaluate local and hosted candidates with the same questions, prompts, and acceptance criteria. Include answer correctness, citation quality, tool results, and human-handoff rate when deciding whether a task is accepted. Use the same period and task volume for cost comparisons, and identify processing that still depends on external services.
Model low, medium, and high scenarios for task volume, device utilization, context length, and human-review rate. Identify the factors that most affect accepted-task cost, then decide whether to optimize the model, reduce unnecessary context, adjust capacity, or change the integration.
Include operational work in the launch decision
Assign owners for model upgrades, image updates, storage growth, credential rotation, and recovery. Retain a working previous model configuration and repeat the fixed question set before switching. After launch, update the budget using observed accepted-task volume so procurement and scaling decisions remain tied to measured outcomes.
Related guides
- FastGPT Model Gateway Architecture and Routing Guide
- 3-Year Total Cost of Ownership for Self-Hosted Enterprise AI Knowledge Bases