Stop Guessing Your
LLM API Token Bills.
The real-time token cost calculator, model price comparison engine, and spend-attribution proxy for engineering teams shipping AI in production.
Token Cost & Model Spend Calculator
Adjust your monthly request volume, token usage, and caching rates to instantly project your infrastructure costs across top providers.
Batch Processing (50% Off)
Simulate OpenAI / Anthropic batch queue pricing for asynchronous tasks.
2026 Live Model Pricing Matrix
Granular breakdown of per-million token rates, prompt cache read discounts, batch pricing, and context windows across major providers.
| Model | Provider | Context | Input / 1M | Output / 1M | Cached In / 1M | Batch Off | Ratio vs Ref | Action |
|---|
5 Strategies to Cut LLM Spend by 70%
Production architectural patterns used by high-volume engineering teams to eliminate token waste before requests hit the model.
Static Prompt Caching
Frontier models offer up to 90% discounts on cached input tokens (Anthropic at $0.30/M vs $3.00/M). Structure your message payloads so that static system instructions, tool definitions, and documentation are placed strictly at the beginning of the context.
Cascading Model Fallback
Over 75% of user queries don't require full frontier reasoning. Route incoming requests to lightweight models (Gemini 2.0 Flash or Claude 3.5 Haiku) first. If confidence or validation tests fail, seamlessly cascade upward to Claude 3.5 Sonnet or o3-mini.
Constrained JSON Schema
Unconstrained generation wastes hundreds of completion tokens on verbose conversational prose ("Certainly! Here is your JSON..."). Enforce native structured JSON schema mode to guarantee output token economy and eliminate post-processing hallucinations.
Semantic Vector Deduplication
Store embedding vectors of incoming prompts in an edge vector store (Redis or Momento). If a user query shares >0.96 cosine similarity with an existing answer in your cache, serve the response with zero model invocation cost and sub-10ms latency.
Strict Max Token Caps
Agentic loops can enter runaway retry spirals, burning through millions of tokens in minutes. Enforce strict per-session token budgets and clamp max_tokens dynamically based on the intended output length.
LLMSpend Gateway
Automate all 5 optimization rules transparently at the network layer. Plug into the LLMSpend edge proxy with one line of code to get token attribution, budget caps, and automatic model failover with zero manual refactoring.
One Line of Code. Total Cost Governance.
LLMSpend Gateway is a drop-in reverse proxy that attributes token burn per customer, enforces hard monthly budget caps, and hedges model outages.
Per-Tenant Token Attribution
Pass customer IDs or repository tags via request headers to know exactly which client tier or feature branch is driving your API bill.
Automated Hard Budget Caps
Set max monthly spend caps per client. LLMSpend automatically rejects rogue agent loops before receiving an unexpected $10,000 bill.
Multi-Provider Outage Fallback
When OpenAI experiences a 500 or 503 outage, LLMSpend instantaneously reroutes your prompt to Anthropic Claude 3.5 Sonnet or DeepSeek with zero downtime.
Take Control of Your LLM Spend
Join 400+ engineering teams currently piloting the LLMSpend Gateway. Get immediate access to the live dashboard, cost alerts, and SDK.