Free Tool

    LLM Inference Cost Estimator

    Estimate hosted API costs with separate standard and cached input pricing, output pricing and RAG retrieval costs.

    Understanding LLM Cost Components

    This calculator helps estimate real-world inference costs by factoring in optimizations and overhead

    Base Costs

    Requests/Day

    10K = small app • 100K = medium • 1M+ = enterprise

    Token Counts

    1.5K input (prompt + context) • 300 output (response)

    Model Pricing

    Enter the current standard, cached-input and output prices from your provider

    Smart Optimizations

    Prompt Caching

    Cached tokens use their own provider price rather than being treated as free

    Hosted vs self-hosted

    Quantization affects self-hosted capacity, not unrelated API or retrieval charges

    RAG Retrieval

    Vector search + embedding costs per query

    Real-World Factors

    Traffic Patterns

    Peak hours can be 3-5x average load

    Error Handling

    Retries and timeouts add 5-15% overhead

    Model Selection

    Smaller models for simple tasks can cut costs 10x

    💡 Pro Tips for Cost Optimization

    Batch Processing

    Group similar requests together

    Smart Routing

    Route simple queries to smaller models

    Response Streaming

    Improve perceived speed, reduce timeouts

    Usage Monitoring

    Track cost per user/feature

    Cost Inputs

    Hosted API estimate only. Use your provider's current prices. Self-hosted quantization changes infrastructure throughput and must be modelled separately; it does not discount retrieval or other API charges.

    Daily cost: $0.00
    Monthly cost (30d): $0.00

    Reduce Token Costs

    Optimize your token usage for maximum efficiency

    Use shorter, more focused prompts
    Implement prompt caching for repeated queries
    Use smaller models where appropriate
    Batch requests when possible

    Optimize Performance

    Improve efficiency through technical optimization

    Evaluate quantization only within a separate self-hosted capacity model
    Use efficient serving frameworks (vLLM, TensorRT-LLM)
    Optimize RAG retrieval with vector caching
    Set up autoscaling for variable loads

    Monitor & Budget

    Track and control your LLM infrastructure costs

    Set up cost alerts and budgets
    Track cost per request/user/feature
    Monitor cache hit rates and adjust strategies
    Regular vendor pricing reviews