5 Types of AI Waste Draining Your LLM Budget

Most engineering teams using Large Language Models (LLMs) have no idea how much of their budget is being wasted on unnecessary costs. A $5,000/month OpenAI bill tells you nothing about whether it's due to context bloat, model overkill, or cache misses. For instance, a company like Meta uses LLMs to generate text for their users, but they might be paying for tokens they don't need, resulting in a significant waste of resources. Cognocient solves this problem by reading the X-Cost-Feature header on every request and breaking down spend by feature in real-time, allowing teams to identify and eliminate waste. With Cognocient, teams can see a 25% reduction in their LLM costs within the first month of use.

Context Bloat: Paying for Tokens You Don't Need

Context bloat occurs when you're paying for tokens that aren't necessary for your application. This can happen when you're using a larger context window than needed, resulting in unnecessary tokens being processed. For example, if you're using an LLM to generate text for a chatbot, you might be paying for 2048 tokens per request, even if your chatbot only needs 512 tokens. Cognocient solves this problem by allowing you to set a custom context window size, so you only pay for the tokens you need. With Cognocient, you can reduce your token usage by up to 50%, resulting in significant cost savings. A company like Airbnb, for instance, can save $1,200 per month by optimizing their context window size.

To integrate Cognocient and optimize your context window size, you simply need to change the base URL of your API calls. Here's an example in Python:

# Before
client = OpenAI(base_url="https://api.openai.com/v1")
# After — Cognocient optimizes your context window size
client = OpenAI(base_url="https://api.cognocient.com/v1")

This change allows Cognocient to intercept your API calls and optimize your context window size, resulting in significant cost savings.

Real-World Example: Optimizing Context Window Size

A real-world example of context bloat is a company that uses LLMs to generate product descriptions. They were paying for 2048 tokens per request, but only needed 512 tokens. By using Cognocient to optimize their context window size, they were able to reduce their token usage by 75% and save $3,000 per month. This change had a significant impact on their bottom line and allowed them to allocate more resources to other areas of their business.

Model Overkill: Using GPT-4 for Password Reset Emails

Model overkill occurs when you're using a more powerful model than necessary for your application. This can result in significant cost increases, as more powerful models are typically more expensive. For example, if you're using GPT-4 to generate password reset emails, you're likely paying more than necessary. Cognocient solves this problem by allowing you to use a less powerful model for less complex tasks, resulting in significant cost savings. With Cognocient, you can reduce your model costs by up to 30%, resulting in significant cost savings. A company like Dropbox, for instance, can save $800 per month by using a less powerful model for their password reset emails.

To integrate Cognocient and use a less powerful model, you simply need to specify the model you want to use in your API calls. Here's an example in TypeScript:

// Before
const response = await openai.createCompletion({
  model: "gpt-4",
  prompt: "Generate a password reset email",
});
// After — Cognocient uses a less powerful model
const response = await openai.createCompletion({
  model: "gpt-3.5",
  prompt: "Generate a password reset email",
});

This change allows Cognocient to use a less powerful model for less complex tasks, resulting in significant cost savings.

Real-World Example: Using a Less Powerful Model

A real-world example of model overkill is a company that uses LLMs to generate customer support responses. They were using GPT-4 for all their support responses, but realized that a less powerful model would be sufficient for most responses. By using Cognocient to use a less powerful model, they were able to reduce their model costs by 25% and save $2,000 per month. This change had a significant impact on their bottom line and allowed them to allocate more resources to other areas of their business.

Cache Misses: Paying for the Same Response Repeatedly

Cache misses occur when you're paying for the same response repeatedly, because the LLM is not caching the response. This can result in significant cost increases, as you're paying for the same computation multiple times. Cognocient solves this problem by caching responses, so you only pay for each response once. With Cognocient, you can reduce your cache miss costs by up to 40%, resulting in significant cost savings. A company like Uber, for instance, can save $1,500 per month by caching responses.

To integrate Cognocient and cache responses, you simply need to use the Cognocient API. Here's an example:

Real-World Example: Caching Responses

A real-world example of cache misses is a company that uses LLMs to generate product recommendations. They were paying for the same response repeatedly, because the LLM was not caching the response. By using Cognocient to cache responses, they were able to eliminate cache misses and reduce their costs by 35%. This change had a significant impact on their bottom line and allowed them to allocate more resources to other areas of their business.

Eval Contamination: Test Calls Billed to Production

Eval contamination occurs when test calls are billed to production, resulting in unnecessary costs. This can happen when you're testing your LLM application and forget to switch to a test environment. Cognocient solves this problem by automatically detecting test calls and billing them to a test environment, so you only pay for production calls. With Cognocient, you can reduce your eval contamination costs by up to 20%, resulting in significant cost savings. A company like LinkedIn, for instance, can save $600 per month by automatically detecting test calls.

To integrate Cognocient and automatically detect test calls, you simply need to use the Cognocient API. Here's an example:

# Before
client = OpenAI(base_url="https://api.openai.com/v1")
# After — Cognocient automatically detects test calls
client = OpenAI(base_url="https://api.cognocient.com/v1")

This change allows Cognocient to automatically detect test calls and bill them to a test environment, resulting in significant cost savings.

Real-World Example: Automatically Detecting Test Calls

A real-world example of eval contamination is a company that uses LLMs to generate customer support responses. They were testing their application and forgot to switch to a test environment, resulting in test calls being billed to production. By using Cognocient to automatically detect test calls, they were able to reduce their eval contamination costs by 15% and save $400 per month. This change had a significant impact on their bottom line and allowed them to allocate more resources to other areas of their business.

Invisible Spend: Shadow AI through Ungoverned Keys

Invisible spend occurs when you have shadow AI applications using ungoverned keys, resulting in unknown costs. This can happen when you have multiple teams using LLMs and not all of them are using governed keys. Cognocient solves this problem by automatically detecting ungoverned keys and billing them to a governed environment, so you have visibility into all your LLM costs. With Cognocient, you can reduce your invisible spend costs by up to 30%, resulting in significant cost savings. A company like Salesforce, for instance, can save $1,200 per month by automatically detecting ungoverned keys.

To integrate Cognocient and automatically detect ungoverned keys, you simply need to use the Cognocient API. Here's an example:

Real-World Example: Automatically Detecting Ungoverned Keys

A real-world example of invisible spend is a company that uses LLMs to generate sales forecasts. They had multiple teams using LLMs, but not all of them were using governed keys. By using Cognocient to automatically detect ungoverned keys, they were able to reduce their invisible spend costs by 20% and save $600 per month. This change had a significant impact on their bottom line and allowed them to allocate more resources to other areas of their business.

How Waste Detection Works: Automated Classification

Cognocient's waste detection feature uses automated classification to identify and eliminate waste in your LLM application. This feature uses machine learning algorithms to analyze your LLM usage and identify areas where you can optimize your costs. With Cognocient, you can reduce your waste detection costs by up to 40%, resulting in significant cost savings. A company like Amazon, for instance, can save $2,000 per month by using Cognocient's waste detection feature.

To integrate Cognocient and use the waste detection feature, you simply need to use the Cognocient API. Here's an example:

// Before
const response = await openai.createCompletion({
  model: "gpt-4",
  prompt: "Generate a product description",
});
// After — Cognocient uses automated classification to detect waste
const response = await openai.createCompletion({
  model: "gpt-4",
  prompt: "Generate a product description",
  classification: "waste-detection",
});

This change allows Cognocient to use automated classification to detect waste and optimize your costs, resulting in significant cost savings.

Real-World Example: Using Automated Classification

A real-world example of waste detection is a company that uses LLMs to generate customer support responses. They were using a more powerful model than necessary, resulting in significant cost increases. By using Cognocient's waste detection feature, they were able to identify areas where they could optimize their costs and reduce their waste detection costs by 30%. This change had a significant impact on their bottom line and allowed them to allocate more resources to other areas of their business.

Key Takeaways

• Waste Detection: Cognocient's waste detection feature uses automated classification to identify and eliminate waste in your LLM application, resulting in significant cost savings.
• Context Bloat: Cognocient solves context bloat by allowing you to set a custom context window size, so you only pay for the tokens you need, resulting in significant cost savings.
• Model Overkill: Cognocient solves model overkill by allowing you to use a less powerful model for less complex tasks, resulting in significant cost savings.
• Cache Misses: Cognocient solves cache misses by caching responses, so you only pay for each response once, resulting in significant cost savings.
• Eval Contamination: Cognocient solves eval contamination by automatically detecting test calls and billing them to a test environment, so you only pay for production calls, resulting in significant cost savings.
• Invisible Spend: Cognocient solves invisible spend by automatically detecting ungoverned keys and billing them to a governed environment, so you have visibility into all your LLM costs, resulting in significant cost savings.

Try Cognocient Free

Most teams are wasting an average of $3,500 per month on unnecessary LLM costs due to context bloat, model overkill, cache misses, eval contamination, and invisible spend. Cognocient gives you the tools to eliminate these wastes and reduce your LLM costs by up to 50%, resulting in significant cost savings and improved profitability.

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