Burnwise is an AI cost copilot that analyzes where your LLM budget actually goes, links costs to product features, and gives you concrete decisions to cut AI spending by 40% without sacrificing quality.

Which AI providers does Burnwise support?

Burnwise supports all major LLM providers including OpenAI (GPT-5.2, GPT-4), Anthropic (Claude 4.5), Google (Gemini 3.0), Mistral, xAI (Grok), DeepSeek, and Perplexity.

How long does it take to integrate Burnwise?

Burnwise can be integrated in under 5 minutes with just a few lines of code. Simply install the SDK, initialize with your API key, and wrap your existing AI client.

Does Burnwise track my prompts or completions?

No. Burnwise only tracks metadata like token counts, model names, costs, and latency. We never track prompt content, completion content, or any user data within prompts.

How much can I save with Burnwise?

Teams using Burnwise typically reduce their LLM costs by 20-40% through model arbitrage, feature-level optimization, and eliminating waste - all while maintaining output quality.

Use Case

Best AI for Data Analysis 2026 - Analytics & Insights

Compare AI models for data analysis, summarization, and insight generation from large datasets.

Input/Output Ratio

80% / 20%

Typical for this use case

Cheapest Option

$1.00

per 10M tokens/month

Top Pick

gemini-3.0-pro

Quality + Value balanced

Browse by Use Case

Chatbot / Customer Support Code Generation Content Writing Data Analysis Document Summarization Translation Research & Search AI Agents & Orchestration Embeddings & RAG Image Generation

Recommended Models for Data Analysis

claude-opus-4.5Anthropic

Cheapest Models for Data Analysis

Based on 80% input / 20% output token ratio typical for this use case.

Rank	Model	Cost/10M tokens	vs Best Quality
#1	ministral-8bMistral	$1.00	-98%
#2	ministral-14bMistral	$1.50	-96%
#3	gpt-4.1-nanoOpenAI	$1.60	-96%
#4	gemini-2.5-flash-liteGoogle	$1.60	-96%
#5	gemini-2.0-flashGoogle	$1.60	-96%

Key Considerations

*Input-heavy = prioritize cheap input pricing
*Long context essential for large datasets
*Consider Gemini for 2M context window
*Batch processing can reduce costs

Quick Links

Cost Calculator All Models SDK Documentation

Optimize Your Costs

Track every API call and get AI-powered recommendations to reduce costs by 20-40%.

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