LLM Cost Optimization: Practical Strategies to Reduce Your AI Spend

By the end of this guide, you will have a practical playbook for cutting your LLM costs by 40-70% without sacrificing output quality. You will implement tiered model selection, optimize prompts for token efficiency, build external caching with Redis, batch requests for throughput, and set up cost tracking that shows exactly where your money goes.

Every strategy uses NeuroLink’s multi-provider interface, so you can apply these patterns across OpenAI, Anthropic, Google, and any other provider you use.

Understanding Your LLM Cost Structure

Before optimizing, you need to understand where your money goes. LLM costs typically break down into several components.

Token-Based Pricing

Pricing Disclaimer: Figures below are approximate and based on publicly available provider pricing as of January 2026. Always verify current pricing at:

• OpenAI Pricing
• Anthropic Pricing
• Google AI Pricing
• AWS Bedrock Pricing

Pricing shown reflects estimates at time of writing. LLM pricing changes frequently – verify current rates at your provider’s pricing page before making cost projections.

Most LLM providers charge based on tokens—roughly 4 characters or 0.75 words per token. Costs are typically split between:

• Input tokens: The prompts and context you send to the model
• Output tokens: The responses generated by the model (usually 2-4x more expensive than input)

For example, GPT-4 Turbo charges approximately $10 per million input tokens and $30 per million output tokens. Claude Sonnet 4 sits at $3 per million input and $15 per million output. These differences matter significantly at scale.

Cost-Saving Tip: Consider GPT-4o as a more cost-effective alternative to GPT-4 Turbo. At $2.50 per million input tokens and $10 per million output tokens, GPT-4o offers similar capabilities at roughly 70% lower cost.

Hidden Cost Factors

Beyond raw token costs, consider:

• Retry overhead: Failed requests that consume tokens before erroring
• Verbose responses: Models that generate more text than necessary
• Context bloat: Sending unnecessary information in prompts
• Inefficient request patterns: Making many small requests instead of consolidated ones

Calculating Your True Cost Per Query

To optimize effectively, calculate your actual cost per meaningful business outcome:

True Cost = (Input Tokens x Input Rate + Output Tokens x Output Rate) x (1 + Retry Rate) / Success Rate

This formula accounts for retries and failures, giving you a realistic baseline for optimization efforts.

Strategy 1: Intelligent Model Selection

Not every request requires your most powerful—and expensive—model. Selecting the right model for each task can slash costs by 40-70% while maintaining quality where it matters.

The Tiered Approach

Tier 1 - Economy Models (Simple Tasks)

Use lightweight models like GPT-4o Mini, Claude Haiku 3.5, or Gemini Flash for:

• Simple classifications
• Data extraction from structured text
• Basic summarization
• Formatting and cleanup tasks

Cost: $0.15-1.00 per million input tokens

Tier 2 - Standard Models (Moderate Complexity)

Deploy mid-tier models like Claude Sonnet 4 or GPT-4o for:

• Content generation
• Code assistance
• Complex question answering
• Multi-step reasoning

Cost: $2.50-5 per million input tokens

Note: GPT-4 Turbo ($10/M input, $30/M output) is significantly more expensive than GPT-4o ($2.50/M input, $10/M output) while offering similar performance for most tasks. We recommend GPT-4o for standard workloads.

Tier 3 - Premium Models (Maximum Capability)

Reserve top-tier models like Claude Opus 4.5 (claude-opus-4-5-20251101) for:

• Critical business decisions
• Complex analysis requiring nuanced understanding
• Creative tasks demanding highest quality
• Tasks where errors carry significant consequences

Cost: Claude Opus 4.5: $5/M input, $25/M output (as of January 2026)

Note: Model names and IDs in code examples reflect versions available at time of writing. Model availability, naming conventions, and pricing change frequently. Always verify current model IDs with your provider’s documentation before deploying to production.

Implementing Model Selection with NeuroLink

With NeuroLink, switching between models and providers is straightforward:

import { NeuroLink } from '@juspay/neurolink';

const neurolink = new NeuroLink();

// Economy tier - simple classification task
async function classifyTicket(ticketText: string): Promise<string> {
  const result = await neurolink.generate({
    input: { text: `Classify this support ticket into one of: billing, technical, general.\n\nTicket: ${ticketText}` },
    provider: 'openai',
    model: 'gpt-4o-mini',  // ~$0.15/M input tokens
  });
  return result.content;
}

// Standard tier - content generation
async function generateResponse(context: string, query: string): Promise<string> {
  const result = await neurolink.generate({
    input: { text: `Context: ${context}\n\nQuestion: ${query}\n\nProvide a helpful response.` },
    provider: 'anthropic',
    model: 'claude-sonnet-4-5-20250929',  // ~$3/M input tokens
  });
  return result.content;
}

// Premium tier - complex analysis
async function analyzeContract(contractText: string): Promise<string> {
  const result = await neurolink.generate({
    input: { text: `Analyze this contract for risks and key terms:\n\n${contractText}` },
    provider: 'anthropic',
    model: 'claude-opus-4-5-20251101',  // ~$5/M input tokens
  });
  return result.content;
}

Building a Simple Router

You can build routing logic based on your task requirements:

import { NeuroLink } from '@juspay/neurolink';

const neurolink = new NeuroLink();

type TaskComplexity = 'simple' | 'moderate' | 'complex';

interface ModelConfig {
  provider: string;
  model: string;
  costPerMillionTokens: number;
}

const MODEL_TIERS: Record<TaskComplexity, ModelConfig> = {
  simple: {
    provider: 'openai',
    model: 'gpt-4o-mini',
    costPerMillionTokens: 0.15
  },
  moderate: {
    provider: 'anthropic',
    model: 'claude-sonnet-4-5-20250929',
    costPerMillionTokens: 3
  },
  complex: {
    provider: 'anthropic',
    model: 'claude-opus-4-5-20251101',
    costPerMillionTokens: 5
  }
};

async function routedGenerate(
  prompt: string,
  complexity: TaskComplexity
): Promise<string> {
  const config = MODEL_TIERS[complexity];

  const result = await neurolink.generate({
    input: { text: prompt },
    provider: config.provider,
    model: config.model
  });

  return result.content;
}

// Usage
const classification = await routedGenerate(
  'Classify: "My order hasn\'t arrived"',
  'simple'
);

const analysis = await routedGenerate(
  'Analyze the market trends in this report...',
  'complex'
);

Real-World Impact

Organizations implementing tiered model selection typically see:

• Before: 100% GPT-4 usage
• After: 60% economy, 30% standard, 10% premium
• Savings: 50-70% with minimal quality impact on appropriate tasks

Strategy 2: Prompt Optimization

The words you choose directly impact your costs. Optimizing prompts can reduce token consumption by 30-50%.

Minimize Input Tokens

Remove Redundant Context

Before (verbose):

You are a helpful assistant that specializes in customer support for our
e-commerce platform. You should always be polite and professional. When
answering questions, provide accurate information based on our policies.
Our company values customer satisfaction above all else. Please help the
following customer with their inquiry:

[500 tokens of context]
[Customer question]

After (optimized):

E-commerce support assistant. Answer based on provided policy context.

[200 tokens of essential context]
[Customer question]

Token reduction: ~40%

Use Concise System Prompts

// Cost-optimized system prompt
const systemPrompt = `Role: Support agent
Tone: Professional, helpful
Format: Bullet points for lists
Constraints: Max 3 sentences per point`;

Control Output Length

Explicit Length Instructions

import { NeuroLink } from '@juspay/neurolink';

const neurolink = new NeuroLink();

async function summarizeArticle(article: string): Promise<string> {
  const result = await neurolink.generate({
    input: {
      text: `Summarize this article in exactly 3 bullet points, each under 20 words.

${article}`
    },
    provider: 'openai',
    model: 'gpt-4o-mini',
    maxTokens: 150  // Limit output tokens
  });
  return result.content;
}

Use Explicit Limits in Prompts

Instead of relying on stop sequences, include explicit instructions in your prompts:

const result = await neurolink.generate({
  input: {
    text: 'List exactly 3 reasons (no more):\n1.'
  },
  provider: 'openai',
  model: 'gpt-4o-mini',
  maxTokens: 200  // Hard limit as backup
});

Prompt Templates for Efficiency

Create reusable, token-efficient templates:

interface PromptTemplate {
  name: string;
  template: string;
  estimatedInputTokens: number;
}

const TEMPLATES: Record<string, PromptTemplate> = {
  classify: {
    name: 'classification',
    template: 'Classify into [{categories}]: {text}\nCategory:',
    estimatedInputTokens: 20
  },
  summarize: {
    name: 'summarization',
    template: 'Summarize in {maxWords} words: {text}',
    estimatedInputTokens: 15
  },
  extract: {
    name: 'extraction',
    template: 'Extract {fields} from: {text}\nJSON:',
    estimatedInputTokens: 15
  }
};

function buildPrompt(
  templateName: string,
  variables: Record<string, string>
): string {
  const template = TEMPLATES[templateName];
  if (!template) throw new Error(`Unknown template: ${templateName}`);

  let prompt = template.template;
  for (const [key, value] of Object.entries(variables)) {
    prompt = prompt.replace(`{${key}}`, value);
  }
  return prompt;
}

// Usage - minimal tokens
const classifyPrompt = buildPrompt('classify', {
  categories: 'billing, technical, general',
  text: 'My payment failed'
});
// Result: "Classify into [billing, technical, general]: My payment failed\nCategory:"

Measuring Prompt Efficiency

Track tokens per prompt to identify optimization opportunities:

function estimateTokens(text: string): number {
  // Rough estimation: ~4 characters per token
  return Math.ceil(text.length / 4);
}

function analyzePromptEfficiency(
  prompt: string,
  response: string,
  taskType: string
): void {
  const inputTokens = estimateTokens(prompt);
  const outputTokens = estimateTokens(response);

  console.log(`Task: ${taskType}`);
  console.log(`Input tokens: ${inputTokens}`);
  console.log(`Output tokens: ${outputTokens}`);
  console.log(`Ratio: ${(outputTokens / inputTokens).toFixed(2)}`);
}

Strategy 3: External Caching with Redis

Why pay to generate the same response twice? Implementing caching at the application level dramatically reduces redundant API calls.

⚠️ Note: This is application-level code you must implement. NeuroLink provides the SDK, not built-in caching. You’ll build caching logic yourself using Redis or similar external services as shown in the patterns below.

Simple Response Caching

import { NeuroLink } from '@juspay/neurolink';
import Redis from 'ioredis';
import crypto from 'crypto';

const neurolink = new NeuroLink();
const redis = new Redis(process.env.REDIS_URL);

const CACHE_TTL = 86400; // 24 hours in seconds

function generateCacheKey(prompt: string, model: string): string {
  const hash = crypto.createHash('sha256')
    .update(`${model}:${prompt}`)
    .digest('hex');
  return `llm:cache:${hash}`;
}

async function cachedGenerate(
  prompt: string,
  provider: string,
  model: string
): Promise<{ text: string; cached: boolean }> {
  const cacheKey = generateCacheKey(prompt, model);

  // Check cache first
  const cached = await redis.get(cacheKey);
  if (cached) {
    return { text: cached, cached: true };
  }

  // Cache miss - call LLM
  const result = await neurolink.generate({
    input: { text: prompt },
    provider,
    model
  });

  // Store in cache
  await redis.setex(cacheKey, CACHE_TTL, result.content);

  return { text: result.content, cached: false };
}

// Usage
const response = await cachedGenerate(
  'What is the capital of France?',
  'openai',
  'gpt-4o-mini'
);

console.log(`Response: ${response.text}`);
console.log(`From cache: ${response.cached}`);

Implementing Similarity-Based Caching

For more sophisticated caching that handles paraphrased queries, you can use embeddings:

import { NeuroLink } from '@juspay/neurolink';
import Redis from 'ioredis';

const neurolink = new NeuroLink();
const redis = new Redis(process.env.REDIS_URL);

interface CachedResponse {
  prompt: string;
  response: string;
  embedding: number[];
  timestamp: number;
}

// Generate embedding for similarity comparison
// Note: NeuroLink's generate() is for text generation, not embeddings.
// Use your preferred embedding service directly (e.g., OpenAI's embeddings API).
async function getEmbedding(text: string): Promise<number[]> {
  // Example using OpenAI embeddings API directly
  const response = await fetch('https://api.openai.com/v1/embeddings', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      input: text,
      model: 'text-embedding-3-small',
    })
  });
  const data = await response.json();
  return data.data[0]?.embedding || [];
}

function cosineSimilarity(a: number[], b: number[]): number {
  if (a.length !== b.length) return 0;

  let dotProduct = 0;
  let normA = 0;
  let normB = 0;

  for (let i = 0; i < a.length; i++) {
    dotProduct += a[i] * b[i];
    normA += a[i] * a[i];
    normB += b[i] * b[i];
  }

  return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
}

async function semanticCachedGenerate(
  prompt: string,
  provider: string,
  model: string,
  similarityThreshold: number = 0.92
): Promise<{ text: string; cached: boolean; similarity?: number }> {
  // Get embedding for the new prompt
  const promptEmbedding = await getEmbedding(prompt);

  // Check cached responses
  // Production: use SCAN instead of KEYS to avoid blocking Redis on large keyspaces
  const cachedKeys = await redis.keys('llm:semantic:*');

  for (const key of cachedKeys) {
    const cachedJson = await redis.get(key);
    if (!cachedJson) continue;

    const cached: CachedResponse = JSON.parse(cachedJson);
    const similarity = cosineSimilarity(promptEmbedding, cached.embedding);

    if (similarity >= similarityThreshold) {
      return {
        text: cached.response,
        cached: true,
        similarity
      };
    }
  }

  // Cache miss - call LLM
  const result = await neurolink.generate({
    input: { text: prompt },
    provider,
    model
  });

  // Store with embedding
  const cacheEntry: CachedResponse = {
    prompt,
    response: result.content,
    embedding: promptEmbedding,
    timestamp: Date.now()
  };

  const cacheKey = `llm:semantic:${Date.now()}`;
  await redis.setex(cacheKey, 86400, JSON.stringify(cacheEntry));

  return { text: result.content, cached: false };
}

Cache Strategy by Use Case

Different applications need different caching strategies:

interface CacheConfig {
  ttlSeconds: number;
  similarityThreshold: number;
  maxEntries: number;
}

const CACHE_CONFIGS: Record<string, CacheConfig> = {
  // FAQ/Support - stable answers, long cache
  support: {
    ttlSeconds: 604800,  // 7 days
    similarityThreshold: 0.88,
    maxEntries: 10000
  },

  // Content generation - fresh content, short cache
  content: {
    ttlSeconds: 3600,  // 1 hour
    similarityThreshold: 0.95,
    maxEntries: 1000
  },

  // Code assistance - moderate caching
  code: {
    ttlSeconds: 86400,  // 24 hours
    similarityThreshold: 0.92,
    maxEntries: 5000
  }
};

Monitoring Cache Performance

Track cache effectiveness:

interface CacheMetrics {
  hits: number;
  misses: number;
  totalRequests: number;
  estimatedSavings: number;
}

class CacheMonitor {
  private metrics: CacheMetrics = {
    hits: 0,
    misses: 0,
    totalRequests: 0,
    estimatedSavings: 0
  };

  private costPerRequest: number;

  constructor(avgCostPerRequest: number = 0.001) {
    this.costPerRequest = avgCostPerRequest;
  }

  recordHit(): void {
    this.metrics.hits++;
    this.metrics.totalRequests++;
    this.metrics.estimatedSavings += this.costPerRequest;
  }

  recordMiss(): void {
    this.metrics.misses++;
    this.metrics.totalRequests++;
  }

  getHitRate(): number {
    if (this.metrics.totalRequests === 0) return 0;
    return this.metrics.hits / this.metrics.totalRequests;
  }

  getReport(): string {
    return `
Cache Performance Report
========================
Total Requests: ${this.metrics.totalRequests}
Cache Hits: ${this.metrics.hits}
Cache Misses: ${this.metrics.misses}
Hit Rate: ${(this.getHitRate() * 100).toFixed(1)}%
Estimated Savings: $${this.metrics.estimatedSavings.toFixed(2)}
    `.trim();
  }
}

Strategy 4: Request Batching Patterns

Processing requests individually incurs overhead. Batching similar requests reduces both costs and latency.

⚠️ Note: This is application-level code you must implement. NeuroLink provides the SDK, but batching logic must be built by your application. The patterns below show how to implement request batching yourself.

Simple Batch Processing

import { NeuroLink } from '@juspay/neurolink';

const neurolink = new NeuroLink();

interface BatchItem {
  id: string;
  prompt: string;
}

interface BatchResult {
  id: string;
  response: string;
}

async function processBatch(
  items: BatchItem[],
  provider: string,
  model: string
): Promise<BatchResult[]> {
  // Combine prompts into a single request
  const combinedPrompt = items
    .map((item, index) => `[${index + 1}] ${item.prompt}`)
    .join('\n\n');

  const systemPrompt = `Process each numbered item and respond with the same numbering format.`;

  const result = await neurolink.generate({
    input: {
      text: `${systemPrompt}\n\n${combinedPrompt}`
    },
    provider,
    model
  });

  // Parse responses (simplified - production code needs robust parsing)
  const responses = result.content.split(/\[\d+\]/).filter(Boolean);

  return items.map((item, index) => ({
    id: item.id,
    response: responses[index]?.trim() || ''
  }));
}

// Usage
const items: BatchItem[] = [
  { id: '1', prompt: 'Classify: "Payment issue"' },
  { id: '2', prompt: 'Classify: "Shipping delay"' },
  { id: '3', prompt: 'Classify: "Product question"' }
];

const results = await processBatch(items, 'openai', 'gpt-4o-mini');

Time-Window Batching

For real-time applications, collect requests within a time window:

import { NeuroLink } from '@juspay/neurolink';

const neurolink = new NeuroLink();

interface QueuedRequest {
  prompt: string;
  resolve: (result: string) => void;
  reject: (error: Error) => void;
}

class BatchQueue {
  private queue: QueuedRequest[] = [];
  private timer: NodeJS.Timeout | null = null;
  private readonly windowMs: number;
  private readonly maxBatchSize: number;
  private readonly provider: string;
  private readonly model: string;

  constructor(options: {
    windowMs?: number;
    maxBatchSize?: number;
    provider: string;
    model: string;
  }) {
    this.windowMs = options.windowMs || 100;
    this.maxBatchSize = options.maxBatchSize || 20;
    this.provider = options.provider;
    this.model = options.model;
  }

  async add(prompt: string): Promise<string> {
    return new Promise((resolve, reject) => {
      this.queue.push({ prompt, resolve, reject });

      // Process immediately if batch is full
      if (this.queue.length >= this.maxBatchSize) {
        this.flush();
      } else if (!this.timer) {
        // Start timer for window
        this.timer = setTimeout(() => this.flush(), this.windowMs);
      }
    });
  }

  private async flush(): Promise<void> {
    if (this.timer) {
      clearTimeout(this.timer);
      this.timer = null;
    }

    if (this.queue.length === 0) return;

    const batch = this.queue.splice(0, this.maxBatchSize);

    try {
      // Process batch
      const combinedPrompt = batch
        .map((req, i) => `[${i}] ${req.prompt}`)
        .join('\n');

      const result = await neurolink.generate({
        input: { text: `Respond to each numbered item:\n${combinedPrompt}` },
        provider: this.provider,
        model: this.model
      });

      // Parse and resolve
      const responses = this.parseResponses(result.content, batch.length);
      batch.forEach((req, i) => req.resolve(responses[i] || ''));

    } catch (error) {
      batch.forEach(req => req.reject(error as Error));
    }
  }

  private parseResponses(text: string, count: number): string[] {
    const responses: string[] = [];
    const parts = text.split(/\[\d+\]/);

    for (let i = 1; i <= count; i++) {
      responses.push(parts[i]?.trim() || '');
    }

    return responses;
  }
}

// Usage
const batcher = new BatchQueue({
  windowMs: 50,
  maxBatchSize: 10,
  provider: 'openai',
  model: 'gpt-4o-mini',
});

// These requests will be batched together
const [result1, result2, result3] = await Promise.all([
  batcher.add('Summarize: Article 1...'),
  batcher.add('Summarize: Article 2...'),
  batcher.add('Summarize: Article 3...')
]);

Batch Optimization Strategies

Homogeneous Batching: Group similar task types together for better results.

type TaskType = 'classification' | 'summarization' | 'extraction';

class TypedBatchQueue {
  private queues: Map<TaskType, BatchQueue> = new Map();

  constructor(private provider: string, private model: string) {
    const taskTypes: TaskType[] = ['classification', 'summarization', 'extraction'];
    taskTypes.forEach(type => {
      this.queues.set(type, new BatchQueue({
        windowMs: 100,
        maxBatchSize: 20,
        provider,
        model
      }));
    });
  }

  async process(taskType: TaskType, prompt: string): Promise<string> {
    const queue = this.queues.get(taskType);
    if (!queue) throw new Error(`Unknown task type: ${taskType}`);
    return queue.add(prompt);
  }
}

Strategy 5: Provider Failover for Cost Efficiency

Using multiple providers allows you to optimize for both cost and reliability:

import { NeuroLink } from '@juspay/neurolink';

const neurolink = new NeuroLink();

interface ProviderConfig {
  provider: string;
  model: string;
  costPerMillionTokens: number;
  priority: number;
}

const PROVIDERS: ProviderConfig[] = [
  { provider: 'anthropic', model: 'claude-sonnet-4-5-20250929', costPerMillionTokens: 3, priority: 1 },
  { provider: 'openai', model: 'gpt-4o', costPerMillionTokens: 2.5, priority: 2 },  // More cost-effective than GPT-4 Turbo
  { provider: 'openai', model: 'gpt-4o-mini', costPerMillionTokens: 0.15, priority: 3 },
  { provider: 'anthropic', model: 'claude-opus-4-5-20251101', costPerMillionTokens: 5, priority: 4 }  // For premium tasks
];

async function generateWithFallback(
  prompt: string,
  preferCheapest: boolean = false
): Promise<{ text: string; provider: string; model: string }> {

  // Sort by cost if preferring cheapest, otherwise by priority
  const sorted = [...PROVIDERS].sort((a, b) =>
    preferCheapest
      ? a.costPerMillionTokens - b.costPerMillionTokens
      : a.priority - b.priority
  );

  let lastError: Error | null = null;

  for (const config of sorted) {
    try {
      const result = await neurolink.generate({
        input: { text: prompt },
        provider: config.provider,
        model: config.model
      });

      return {
        text: result.content,
        provider: config.provider,
        model: config.model
      };
    } catch (error) {
      lastError = error as Error;
      console.warn(`Provider ${config.provider}/${config.model} failed, trying next...`);
    }
  }

  throw lastError || new Error('All providers failed');
}

// Usage - prefer cheapest option
const result = await generateWithFallback(
  'Classify this text...',
  true  // preferCheapest
);

Strategy 6: Cost Tracking and Monitoring

You cannot optimize what you do not measure. Implement cost tracking at the application level.

Simple Cost Tracker

interface UsageRecord {
  timestamp: Date;
  provider: string;
  model: string;
  inputTokens: number;
  outputTokens: number;
  cost: number;
  tags: Record<string, string>;
}

interface CostRates {
  inputPerMillion: number;
  outputPerMillion: number;
}

// Pricing as of January 2026 - verify current rates at provider sites
const MODEL_COSTS: Record<string, CostRates> = {
  'gpt-4-turbo': { inputPerMillion: 10, outputPerMillion: 30 },
  'gpt-4o': { inputPerMillion: 2.5, outputPerMillion: 10 },  // Recommended over GPT-4 Turbo
  'gpt-4o-mini': { inputPerMillion: 0.15, outputPerMillion: 0.6 },
  'claude-opus-4-5-20251101': { inputPerMillion: 5, outputPerMillion: 25 },  // Claude Opus 4.5 - Jan 2026
  'claude-sonnet-4-5-20250929': { inputPerMillion: 3, outputPerMillion: 15 },
  'claude-3-5-haiku-20241022': { inputPerMillion: 0.8, outputPerMillion: 4 }
};

class CostTracker {
  private records: UsageRecord[] = [];

  calculateCost(
    model: string,
    inputTokens: number,
    outputTokens: number
  ): number {
    const rates = MODEL_COSTS[model];
    if (!rates) return 0;

    const inputCost = (inputTokens / 1_000_000) * rates.inputPerMillion;
    const outputCost = (outputTokens / 1_000_000) * rates.outputPerMillion;

    return inputCost + outputCost;
  }

  record(
    provider: string,
    model: string,
    inputTokens: number,
    outputTokens: number,
    tags: Record<string, string> = {}
  ): void {
    const cost = this.calculateCost(model, inputTokens, outputTokens);

    this.records.push({
      timestamp: new Date(),
      provider,
      model,
      inputTokens,
      outputTokens,
      cost,
      tags
    });
  }

  getTotalCost(since?: Date): number {
    return this.records
      .filter(r => !since || r.timestamp >= since)
      .reduce((sum, r) => sum + r.cost, 0);
  }

  getCostByModel(): Record<string, number> {
    const costs: Record<string, number> = {};

    for (const record of this.records) {
      costs[record.model] = (costs[record.model] || 0) + record.cost;
    }

    return costs;
  }

  getCostByTag(tagKey: string): Record<string, number> {
    const costs: Record<string, number> = {};

    for (const record of this.records) {
      const tagValue = record.tags[tagKey] || 'untagged';
      costs[tagValue] = (costs[tagValue] || 0) + record.cost;
    }

    return costs;
  }

  getReport(): string {
    const total = this.getTotalCost();
    const byModel = this.getCostByModel();

    let report = `
Cost Report
===========
Total Cost: $${total.toFixed(4)}
Total Requests: ${this.records.length}

By Model:
`;

    for (const [model, cost] of Object.entries(byModel)) {
      report += `  ${model}: $${cost.toFixed(4)}\n`;
    }

    return report;
  }
}

// Usage with NeuroLink
import { NeuroLink } from '@juspay/neurolink';

const neurolink = new NeuroLink();
const tracker = new CostTracker();

async function trackedGenerate(
  prompt: string,
  provider: string,
  model: string,
  tags: Record<string, string> = {}
): Promise<string> {
  const inputTokens = Math.ceil(prompt.length / 4);  // Rough estimate

  const result = await neurolink.generate({
    input: { text: prompt },
    provider,
    model
  });

  const outputTokens = Math.ceil(result.content.length / 4);  // Rough estimate

  tracker.record(provider, model, inputTokens, outputTokens, tags);

  return result.content;
}

// Track with tags for attribution
await trackedGenerate(
  'Summarize this article...',
  'openai',
  'gpt-4o-mini',
  { team: 'marketing', feature: 'content-gen' }
);

console.log(tracker.getReport());

Setting Budget Alerts

class BudgetMonitor {
  private dailyBudget: number;
  private monthlyBudget: number;
  private tracker: CostTracker;
  private alertCallback: (message: string) => void;

  constructor(options: {
    dailyBudget: number;
    monthlyBudget: number;
    tracker: CostTracker;
    onAlert: (message: string) => void;
  }) {
    this.dailyBudget = options.dailyBudget;
    this.monthlyBudget = options.monthlyBudget;
    this.tracker = options.tracker;
    this.alertCallback = options.onAlert;
  }

  check(): void {
    const now = new Date();
    const startOfDay = new Date(now.getFullYear(), now.getMonth(), now.getDate());
    const startOfMonth = new Date(now.getFullYear(), now.getMonth(), 1);

    const dailyCost = this.tracker.getTotalCost(startOfDay);
    const monthlyCost = this.tracker.getTotalCost(startOfMonth);

    const dailyPercent = (dailyCost / this.dailyBudget) * 100;
    const monthlyPercent = (monthlyCost / this.monthlyBudget) * 100;

    if (dailyPercent >= 90) {
      this.alertCallback(`CRITICAL: Daily budget at ${dailyPercent.toFixed(1)}%`);
    } else if (dailyPercent >= 75) {
      this.alertCallback(`WARNING: Daily budget at ${dailyPercent.toFixed(1)}%`);
    }

    if (monthlyPercent >= 90) {
      this.alertCallback(`CRITICAL: Monthly budget at ${monthlyPercent.toFixed(1)}%`);
    } else if (monthlyPercent >= 75) {
      this.alertCallback(`WARNING: Monthly budget at ${monthlyPercent.toFixed(1)}%`);
    }
  }
}

Implementing Your Cost Optimization Strategy

Phase 1: Measure (Week 1-2)

1. Implement cost tracking with the patterns shown above
2. Tag all requests with attribution data (team, feature, use case)
3. Establish baseline costs by application, model, and team
4. Identify top cost drivers

Phase 2: Quick Wins (Week 3-4)

1. Implement external caching for repetitive queries (expect 20-40% reduction)
2. Optimize top 10 highest-volume prompts for token efficiency
3. Switch simple tasks to economy-tier models
4. Set up budget alerts

Phase 3: Deep Optimization (Month 2-3)

1. Build routing logic to select models based on task complexity
2. Implement batching for eligible workloads
3. Fine-tune caching TTLs and similarity thresholds
4. Add provider failover for reliability and cost optimization

Phase 4: Continuous Improvement (Ongoing)

1. Monthly cost reviews and optimization sprints
2. A/B testing for quality vs. cost tradeoffs
3. Evaluate new models for cost efficiency
4. Architecture reviews for systemic improvements

Measuring Success

Track these KPIs to validate your optimization efforts:

Metric	Target	Measurement
Cost per query	-40%	Total spend / successful queries
Cache hit rate	>35%	Cache hits / total requests
Model tier distribution	60/30/10	Requests per tier
Quality score	Maintain baseline	User feedback, automated eval
P95 latency	<2s	Response time tracking

Roadmap: Advanced Cost Features

While the strategies in this guide use patterns you can implement today, we are actively working on built-in cost optimization features for NeuroLink:

• Automatic model routing: ML-based task complexity detection for automatic tier selection
• Built-in semantic caching: Native caching with embedding-based similarity matching
• Cost dashboards: Real-time cost visualization and analytics
• Budget enforcement: Automatic throttling and degradation when budgets are exceeded
• Batch API support: Native integration with provider batch APIs for async workloads

Stay tuned to our changelog and documentation for updates on these features.

Conclusion

By now you have a complete cost optimization playbook: model tiering, prompt compression, response caching, request batching, provider failover, and cost tracking with budget alerts. Organizations implementing these patterns typically see 40-60% cost reduction within the first month and 50-70% within three months.

The implementation path:

1. Week 1-2: Instrument cost tracking and establish baselines
2. Week 3-4: Cache repetitive queries, optimize top prompts, switch simple tasks to economy models
3. Month 2-3: Build routing logic, implement batching, tune caching thresholds
4. Ongoing: Monthly cost reviews, A/B testing quality vs. cost, evaluate new models

Start measuring. The rest follows from the data.

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Related posts:

• Performance Benchmarking Guide for NeuroLink
• Multi-Provider Failover: Never Lose an API Call
• Real-Time AI: Streaming Response Patterns with NeuroLink