Choosing LLMs

>>> A guide to choosing the right LLM for your needs.

Choosing Models

Different models fit different workloads. Premium models handle reasoning, planning, coding, and long-form analysis. Cheaper models work well for rewriting, extraction, tagging, support replies, or simple automation.

Model Tiers & Use Cases

Premium Models

GPT-5, Gemini 3, Claude 4.5 Opus

Best for complex reasoning, coding, creative writing, and nuanced analysis. These models have the highest accuracy but come with higher latency and cost. Use them for user-facing tasks where quality is paramount.

Mid-Tier Models

Gemini 2.5 Pro, GPT-4o

A balance of performance and speed. Excellent for summarization, standard chat interactions, and data extraction. They are significantly faster and cheaper than premium models while maintaining good reasoning capabilities.

Budget / High-Volume Models

Gemini 2.5 Flash, DeepSeek, Llama 3 8B

Ideal for high-volume automation, simple classification, tagging, and formatting tasks. These models are extremely fast and cost-effective, making them perfect for background jobs where deep reasoning isn't required.

Start with a premium model to validate your use case, then try to downgrade to a smaller model or fine-tune a cheaper one for production to save costs.

Managing Cost

Strategic approaches to reduce your LLM spending without sacrificing quality.

Reduction Strategies

Use Premium Models Only When Needed

Reserve expensive models for complex reasoning tasks. Use cheaper models for simple operations like classification, extraction, or formatting.

Request Short or Structured Replies

Specify output formats like JSON or bullet points. Limit response length when possible. Output tokens are typically more expensive than input tokens.

Avoid Unnecessary Chit-Chat

Be concise in your prompts. Remove pleasantries and redundant context. Every token in your prompt costs money, especially when repeated across thousands of requests.

Track token usage early in development. Cost surprises come later when you scale to production traffic.

Hosted APIs

Hosted APIs are ideal for most builders — no GPUs, scaling, upgrades, or DevOps required.

1. Direct from Model Provider

OpenAI, Anthropic, Google

Best documentation and developer tools available directly from the source.
Early access to new models, features, evals, and embeddings.
Strong enterprise controls including SOC2, HIPAA compliance, and SLAs.
Clear support channels and reliability guarantees.

2. Aggregators / Unified Gateways

OpenRouter, etc.

One single API to access many models from different vendors.
Centralized billing and credit usage across all models.
Easy model comparison and routing without changing code.
Excellent for teams wanting to avoid vendor lock-in.

Self-Hosting

Full control, privacy, and customization — but requires infrastructure expertise.

Why Self-Host?

Control & Privacy

Complete ownership of your data. No external API calls.

Customization

Fine-tune or optimize models for your specific use case.

Cost at Scale

Potentially cheaper at very large volume.

Considerations

Self-hosting requires deployment, monitoring, and inference optimization expertise. You are responsible for uptime, scaling, and security updates.

Licensing: Llama 3, Mistral, and DeepSeek allow broad commercial use. Others may not permit hosted serving. Always check the model license.

Most teams start with hosted APIs and only move to self-hosting when scale, regulation, or customization demands it.

Core Implementation Tips

Essential practices for production LLM deployments.

01. Version Your Prompts

Treat prompts like code. Version them in git, give them descriptive names, and create reusable templates for common patterns.

02. Structured Output

Request JSON, lists, or other structured formats to avoid ambiguity and make parsing easier. This reduces errors and improves consistency.

03. Cache Everything

Implement caching for identical or similar queries to cut both cost and latency. A simple cache can reduce API calls by 30-50%.

04. Stream Responses

Use streaming APIs to show results as they're generated. This dramatically improves perceived performance and user experience.

05. Track Usage

Monitor token consumption from day one. Set up alerts for unusual spikes. Cost surprises come later when you're at scale.

Key Takeaway

Production LLM deployments require careful planning around cost, performance, and reliability. Start simple with hosted APIs, monitor closely, and optimize as you scale.

Choosing LLMs

>>> A guide to choosing the right LLM for your needs.

Choosing Models

Model Tiers & Use Cases

Premium Models

GPT-5, Gemini 3, Claude 4.5 Opus

Mid-Tier Models

Gemini 2.5 Pro, GPT-4o

Budget / High-Volume Models

Gemini 2.5 Flash, DeepSeek, Llama 3 8B

Start with a premium model to validate your use case, then try to downgrade to a smaller model or fine-tune a cheaper one for production to save costs.

Managing Cost

Strategic approaches to reduce your LLM spending without sacrificing quality.

Reduction Strategies

Use Premium Models Only When Needed

Reserve expensive models for complex reasoning tasks. Use cheaper models for simple operations like classification, extraction, or formatting.

Request Short or Structured Replies

Specify output formats like JSON or bullet points. Limit response length when possible. Output tokens are typically more expensive than input tokens.

Avoid Unnecessary Chit-Chat

Be concise in your prompts. Remove pleasantries and redundant context. Every token in your prompt costs money, especially when repeated across thousands of requests.

Track token usage early in development. Cost surprises come later when you scale to production traffic.

Hosted APIs

Hosted APIs are ideal for most builders — no GPUs, scaling, upgrades, or DevOps required.

1. Direct from Model Provider

OpenAI, Anthropic, Google

Best documentation and developer tools available directly from the source.
Early access to new models, features, evals, and embeddings.
Strong enterprise controls including SOC2, HIPAA compliance, and SLAs.
Clear support channels and reliability guarantees.

2. Aggregators / Unified Gateways

OpenRouter, etc.

One single API to access many models from different vendors.
Centralized billing and credit usage across all models.
Easy model comparison and routing without changing code.
Excellent for teams wanting to avoid vendor lock-in.

Self-Hosting

Full control, privacy, and customization — but requires infrastructure expertise.

Why Self-Host?

Control & Privacy

Complete ownership of your data. No external API calls.

Customization

Fine-tune or optimize models for your specific use case.

Cost at Scale

Potentially cheaper at very large volume.

Considerations

Self-hosting requires deployment, monitoring, and inference optimization expertise. You are responsible for uptime, scaling, and security updates.

Licensing: Llama 3, Mistral, and DeepSeek allow broad commercial use. Others may not permit hosted serving. Always check the model license.

Most teams start with hosted APIs and only move to self-hosting when scale, regulation, or customization demands it.

Core Implementation Tips

Essential practices for production LLM deployments.

01. Version Your Prompts

Treat prompts like code. Version them in git, give them descriptive names, and create reusable templates for common patterns.

02. Structured Output

Request JSON, lists, or other structured formats to avoid ambiguity and make parsing easier. This reduces errors and improves consistency.

03. Cache Everything

Implement caching for identical or similar queries to cut both cost and latency. A simple cache can reduce API calls by 30-50%.

04. Stream Responses

Use streaming APIs to show results as they're generated. This dramatically improves perceived performance and user experience.

05. Track Usage

Monitor token consumption from day one. Set up alerts for unusual spikes. Cost surprises come later when you're at scale.

Key Takeaway

Production LLM deployments require careful planning around cost, performance, and reliability. Start simple with hosted APIs, monitor closely, and optimize as you scale.