LLM cost planning
Engineering and finance teams use OmniKit to turn prompt packs into monthly dollar envelopes — before locking a vendor or spinning up GPUs. Prices follow a versioned list catalog; negotiated contracts may differ.
- 01
Baseline monthly token cost
Paste real system and user prompts, set RPS, and compare DeepSeek, OpenAI, Claude, Gemini, and vLLM on the same workload.
Open → - 02
Read a vendor head-to-head
Open a keyword-focused compare brief (for example DeepSeek vs GPT-4o mini) with an embedded mini calculator, then jump back to the full tool.
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Model RAG and retrieval spend
If retrieval dominates chat volume, estimate embedding + generation separately so the chatbot line item is not hiding vector costs.
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Find savings levers
Project prompt-cache savings, batch vs realtime discounts, and GPU/vLLM break-even before you commit to infra.
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Check rate-limit headroom
Map RPS and tokens to RPM/TPM caps so growth plans do not stall on throttle errors.
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Methodology notes
OmniKit estimates use published per-million list rates and local tokenization where possible. Cache write fees, batch multipliers, and self-hosted utilization assumptions are editable inputs — set them to match your provider docs. For quality, always A/B on your domain before switching 100% of traffic.
Start at the LLM Cost Calculator or browse all cost comparisons.