---
title: "Multi-Provider AI Strategy: OpenAI vs Claude vs Groq vs Deepseek vs Gemini vs Bedrock Comparison"
date: "2025-01-19"
author: "Pixeltable Team"
tags:
  - Multi-Provider AI
  - OpenAI
  - Anthropic Claude
  - Groq
  - Deepseek
  - Google Gemini
  - AWS Bedrock
  - AI Provider Comparison
  - Cost Optimization
  - Performance Optimization
description: "Optimize your AI infrastructure costs and performance with a multi-provider strategy. Compare OpenAI, Anthropic Claude, Groq, Deepseek, Google Gemini, and AWS Bedrock for production AI workflows. Learn when to use each provider and how to switch seamlessly with Pixeltable."
url: "https://pixeltable.com/blog/multi-provider-ai-strategy-comparison-guide"
---

# Multi-Provider AI Strategy: OpenAI vs Claude vs Groq vs Deepseek vs Gemini vs Bedrock Comparison

## The Multi-Provider AI Revolution: Why One LLM Isn't Enough

 
The AI landscape has matured beyond "use OpenAI for everything." With providers like Anthropic's Claude excelling at reasoning, Groq delivering 70+ tokens/second inference, Deepseek offering cost-effective alternatives, and Google's Gemini providing multimodal capabilities, smart teams are adopting **multi-provider strategies** to optimize cost, performance, and reliability.

 
 
But managing multiple AI providers typically means juggling different SDKs, API patterns, and integration complexities. This guide shows you how to build a flexible multi-provider architecture using [Pixeltable's unified infrastructure](/blog/unified-multimodal-ai-infrastructure-pixeltable), making provider switching as simple as changing a single line of code.

 
## The AI Provider Landscape 2025

 
Understanding each provider's strengths helps you make strategic decisions:

 
### OpenAI: The Reliable Generalist

 

 - **Best For:** General-purpose tasks, vision, high reliability

 - **Models:** GPT-4o, GPT-4o-mini, o1, o1-mini, DALL-E 3, Whisper

 - **Strengths:** Most mature ecosystem, excellent documentation, reliable uptime

 - **Weaknesses:** Premium pricing, can be slower than specialized providers

 - **Pricing:** $0.15-$60 per million tokens depending on model

 

 
### Anthropic Claude: The Reasoning Expert

 

 - **Best For:** Complex reasoning, long documents, technical analysis

 - **Models:** Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku

 - **Strengths:** 200K context, superior document understanding, safety focus

 - **Weaknesses:** Limited multimodal (vision only, no TTS/STT)

 - **Pricing:** $3-$75 per million tokens

 

 
### Groq: The Speed Demon

 
*Note: Groq integration recently added to Pixeltable. Check [official documentation](https://docs.pixeltable.com) for latest API patterns.*

 

 - **Best For:** Real-time chat, low-latency applications, high throughput

 - **Models:** Llama 3, Mixtral, Gemma (on LPU hardware)

 - **Strengths:** 70+ tokens/second (10x faster than typical), low latency

 - **Weaknesses:** Limited model selection, newer platform

 - **Pricing:** Very competitive, often cheaper than OpenAI for similar models

 

 
### Deepseek: The Cost Optimizer

 
*Note: Deepseek integration recently added to Pixeltable. Check [official documentation](https://docs.pixeltable.com) for latest API patterns.*

 

 - **Best For:** Cost-sensitive applications, high-volume processing

 - **Models:** Deepseek-V3, Deepseek-Coder

 - **Strengths:** Extremely low cost, strong coding capabilities

 - **Weaknesses:** Less proven in production, smaller ecosystem

 - **Pricing:** Significantly cheaper than major providers

 

 
### Google Gemini: The Multimodal Native

 

 - **Best For:** Multimodal generation (text, image, video)

 - **Models:** Gemini 2.5 Flash, Imagen 3, Veo (video generation)

 - **Strengths:** Native multimodal, video generation, Google ecosystem

 - **Weaknesses:** Less mature API, limited availability in some regions

 - **Pricing:** Competitive, especially for multimodal tasks

 

 
### AWS Bedrock: The Enterprise Choice

 

 - **Best For:** Enterprise compliance, AWS ecosystem integration

 - **Models:** Claude, Llama, Titan, Command R+, and more

 - **Strengths:** SOC2, HIPAA compliance, VPC deployment, unified billing

 - **Weaknesses:** More complex setup, AWS-specific

 - **Pricing:** Similar to direct provider pricing, plus AWS infrastructure

 

 
## Decision Framework: When to Use Which Provider

 
| Use Case | First Choice | Budget Alternative | Premium Option |
| --- | --- | --- | --- |
| RAG / Q&A | GPT-4o-mini | Deepseek | Claude 3.5 Sonnet |
| Document Analysis | Claude 3.5 Sonnet | GPT-4o-mini | Claude 3 Opus |
| Real-Time Chat | Groq + Llama 3 | GPT-4o-mini | GPT-4o |
| Code Generation | Deepseek-Coder | GPT-4o-mini | Claude 3.5 Sonnet |
| Vision Tasks | GPT-4o | GPT-4o-mini | Claude 3.5 Sonnet |
| Bulk Processing | Deepseek | GPT-4o-mini | Groq (for speed) |
| Enterprise/Compliance | AWS Bedrock | Azure OpenAI | Bedrock + Claude |

 
## Multi-Provider Architecture with Pixeltable

 
Pixeltable makes it trivial to use different providers for different tasks within the same application:

 
### Automatic Provider Routing

 
```python

import pixeltable as pxt
from pixeltable.functions import openai, anthropic, gemini

# Content analysis table
content = pxt.create_table('analysis.content', {
 'text': pxt.String,
 'task_type': pxt.String, # 'summary', 'analysis', 'generation'
 'priority': pxt.String # 'low', 'medium', 'high'
})

# Route to different providers based on task
@pxt.udf
def route_to_provider(text: str, task_type: str, priority: str) -> str:
 """Smart provider routing based on requirements"""
 
 # High-priority complex tasks: Claude
 if priority == 'high' and task_type in ['analysis', 'reasoning']:
 response = anthropic.messages(
 model='claude-3-5-sonnet-20241022',
 messages=[{'role': 'user', 'content': text}],
 max_tokens=2048
 )
 return response.content[0].text
 
 # Multimodal generation: Gemini
 elif task_type == 'generation':
 response = gemini.generate_content(
 text,
 model='gemini-2.5-flash'
 )
 return response['candidates'][0]['content']['parts'][0]['text']
 
 # Default: GPT-4o-mini for balance
 else:
 response = openai.chat_completions(
 model='gpt-4o-mini',
 messages=[{'role': 'user', 'content': text}]
 )
 return response.choices[0].message.content

content.add_computed_column(
 result=route_to_provider(
 content.text,
 content.task_type,
 content.priority
 )
)

# Single unified interface, optimal provider for each task
# Note: For Groq and Deepseek patterns, consult latest Pixeltable docs
 
```

 
### Cost Optimization Through Provider Selection

 
```python

# Use different providers for different stages of your pipeline

# Stage 1: Initial classification (cheap + fast)
documents = pxt.create_table('docs.incoming', {
 'document': pxt.Document,
 'content': pxt.String
})

# GPT-4o-mini for fast initial categorization
documents.add_computed_column(
 category=openai.chat_completions(
 model='gpt-4o-mini', # Fast, cost-effective
 messages=[{
 'role': 'user',
 'content': f"Categorize in one word: {documents.content[:500]}"
 }]
 ).choices[0].message.content
)

# Stage 2: Deep analysis only for important documents (quality + accuracy)
important_docs = documents.where(
 documents.category.in_(['legal', 'financial', 'technical'])
)

important_docs.add_computed_column(
 deep_analysis=anthropic.messages(
 model='claude-3-5-sonnet-20241022',
 messages=[{
 'role': 'user',
 'content': f"Provide detailed analysis: {important_docs.content}"
 }],
 max_tokens=4096
 ).content[0].text
)

# Stage 3: Translation for international docs
non_english_docs = documents.where(
 documents.language != 'en'
)

non_english_docs.add_computed_column(
 translation=openai.chat_completions(
 model='gpt-4o',
 messages=[{
 'role': 'system',
 'content': 'Translate to English, maintaining technical terminology.'
 }, {
 'role': 'user',
 'content': non_english_docs.content
 }]
 ).choices[0].message.content
)

# Result: Optimal cost/performance for each task
 
```

 
## Performance Comparison: Speed Benchmarks

 
| Provider | Tokens/Second | Latency (First Token) | Best Use Case |
| --- | --- | --- | --- |
| Gemini Flash | 25-50 | ~400ms | Multimodal tasks, generation |
| OpenAI GPT-4o | 15-30 | ~500ms | General purpose, vision |
| Claude 3.5 | 20-40 | ~600ms | Complex reasoning |
| Gemini Flash | 25-50 | ~400ms | Multimodal tasks |
| GPT-4o-mini | 20-40 | ~400ms | Cost-effective general purpose |

 
## Cost Comparison: Real-World Scenarios

 
### Scenario 1: Document Q&A System (10K queries/month)

 
```python

# Assumptions:
# - 500 tokens per query (context + question)
# - 150 tokens per response
# - Total: 6.5M tokens/month

# Cost comparison (verified providers):
providers_cost = {
 'gpt-4o': (6.5 * 2.5) = $16.25, # $2.50 per million
 'gpt-4o-mini': (6.5 * 0.15) = $0.98, # $0.15 per million 
 'claude-3.5-sonnet': (6.5 * 3) = $19.50, # $3 per million
 'claude-3-haiku': (6.5 * 0.25) = $1.63, # $0.25 per million
 'gemini-2.5-flash': (6.5 * 0.075) = $0.49 # ~$0.075 per million
}

# Winner for RAG: GPT-4o-mini or Gemini Flash (best cost/quality ratio)
# Note: Groq and Deepseek integrations available - check docs for pricing
 
```

 
### Scenario 2: Image Analysis (1K images/month)

 
```python

# Vision task cost comparison
vision_costs = {
 'gpt-4o': 1000 * 0.00265 = $2.65, # $0.00265 per image
 'gpt-4o-mini': 1000 * 0.00042 = $0.42, # $0.00042 per image
 'claude-3.5-sonnet': 1000 * 0.0048 = $4.80, # ~$0.0048 per image
 'gemini-flash': 1000 * 0.00025 = $0.25 # ~$0.00025 per image
}

# Winner for vision: Gemini Flash (cost) or GPT-4o-mini (quality/cost balance)
 
```

 
## Implementation Patterns with Pixeltable

 
### Fallback Provider Strategy

 
```python

# Automatic fallback when provider fails
@pxt.udf
def robust_completion(text: str) -> dict:
 """Try multiple providers with automatic fallback"""
 
 providers = [
 ('OpenAI GPT-4o-mini', lambda: openai.chat_completions(
 model='gpt-4o-mini',
 messages=[{'role': 'user', 'content': text}]
 ).choices[0].message.content),
 
 ('Anthropic Claude Haiku', lambda: anthropic.messages(
 model='claude-3-haiku-20240307',
 messages=[{'role': 'user', 'content': text}]
 ).content[0].text),
 
 ('Gemini Flash', lambda: gemini.generate_content(
 text,
 model='gemini-2.5-flash'
 )['candidates'][0]['content']['parts'][0]['text'])
 ]
 
 for provider_name, provider_func in providers:
 try:
 result = provider_func()
 return {
 'text': result,
 'provider': provider_name,
 'status': 'success'
 }
 except Exception as e:
 print(f"{provider_name} failed: {e}")
 continue
 
 return {
 'text': 'All providers unavailable',
 'provider': 'none',
 'status': 'failed'
 }

# Use in computed column
content.add_computed_column(
 robust_response=robust_completion(content.text)
)
 
```

 
### A/B Testing Different Providers

 
```python

# Compare providers on same dataset
test_queries = pxt.create_table('experiments.provider_test', {
 'question': pxt.String,
 'expected_answer': pxt.String
})

# Test verified providers with Pixeltable
test_queries.add_computed_column(
 gpt4o_answer=openai.chat_completions(
 model='gpt-4o',
 messages=[{'role': 'user', 'content': test_queries.question}]
 ).choices[0].message.content
)

test_queries.add_computed_column(
 claude_answer=anthropic.messages(
 model='claude-3-5-sonnet-20241022',
 messages=[{'role': 'user', 'content': test_queries.question}]
 ).content[0].text
)

test_queries.add_computed_column(
 gemini_answer=gemini.generate_content(
 test_queries.question,
 model='gemini-2.5-flash'
 )['candidates'][0]['content']['parts'][0]['text']
)

# Evaluate quality
@pxt.udf
def evaluate_answer_quality(answer: str, expected: str) -> float:
 """Score answer quality (0-1)"""
 # Use embedding similarity as proxy for quality
 from pixeltable.functions import openai
 
 answer_emb = openai.embeddings(answer, model='text-embedding-3-small')
 expected_emb = openai.embeddings(expected, model='text-embedding-3-small')
 
 import numpy as np
 similarity = np.dot(answer_emb, expected_emb) / (
 np.linalg.norm(answer_emb) * np.linalg.norm(expected_emb)
 )
 
 return float(similarity)

# Compare all providers
test_queries.add_computed_column(
 gpt4o_quality=evaluate_answer_quality(
 test_queries.gpt4o_answer,
 test_queries.expected_answer
 )
)

# Repeat for other providers...

# Analyze results
quality_comparison = test_queries.select(
 pxt.functions.avg(test_queries.gpt4o_quality).alias('gpt4o'),
 pxt.functions.avg(test_queries.claude_quality).alias('claude'),
 pxt.functions.avg(test_queries.gemini_quality).alias('gemini')
).collect()

print("Provider Quality Comparison:")
print(quality_comparison)
 
```

 
## AWS Bedrock for Enterprise Compliance

 
For teams requiring enterprise features, AWS Bedrock provides access to multiple models with unified governance:

 
```python

# AWS Bedrock integration through Pixeltable
import pixeltable as pxt

# Configure Bedrock
import boto3
bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')

# Use Claude through Bedrock for compliance
@pxt.udf
def bedrock_claude(text: str) -> str:
 """Call Claude through AWS Bedrock"""
 import json
 
 response = bedrock.invoke_model(
 modelId='anthropic.claude-3-5-sonnet-20241022-v2:0',
 body=json.dumps({
 'anthropic_version': 'bedrock-2023-05-31',
 'messages': [{'role': 'user', 'content': text}],
 'max_tokens': 2048
 })
 )
 
 result = json.loads(response['body'].read())
 return result['content'][0]['text']

# VPC deployment, SOC2 compliance, AWS billing
compliant_analysis = documents.add_computed_column(
 analysis=bedrock_claude(documents.content)
)
 
```

 
## Real-World Multi-Provider Strategies

 
### Strategy 1: Cost-Performance Tiering

 
```python

# Tier models by price/performance
content_processing = pxt.create_table('content.processing', {
 'text': pxt.String,
 'word_count': pxt.Int,
 'complexity_score': pxt.Float
})

# Simple tasks: Gemini Flash (cheapest for good quality)
simple_content = content_processing.where(
 (content_processing.word_count = 0.7)
)

complex_content.add_computed_column(
 result=anthropic.messages(
 model='claude-3-5-sonnet-20241022',
 messages=[{'role': 'user', 'content': complex_content.text}]
 ).content[0].text
)
 
```

 
### Strategy 2: Quality-Optimized Routing

 
```python

# Route based on quality vs cost requirements
chat_messages = pxt.create_table('chat.messages', {
 'message': pxt.String,
 'quality_requirement': pxt.String # 'basic', 'standard', 'premium'
})

# Basic quality: Gemini Flash for speed and cost
basic_chat = chat_messages.where(
 chat_messages.quality_requirement == 'basic'
)

basic_chat.add_computed_column(
 response=gemini.generate_content(
 basic_chat.message,
 model='gemini-2.5-flash'
 )['candidates'][0]['content']['parts'][0]['text']
)

# Standard: GPT-4o-mini for balanced quality
standard_chat = chat_messages.where(
 chat_messages.quality_requirement == 'standard'
)

standard_chat.add_computed_column(
 response=openai.chat_completions(
 model='gpt-4o-mini',
 messages=[{'role': 'user', 'content': standard_chat.message}]
 ).choices[0].message.content
)

# Premium: Claude for maximum quality
premium_chat = chat_messages.where(
 chat_messages.quality_requirement == 'premium'
)

premium_chat.add_computed_column(
 response=anthropic.messages(
 model='claude-3-5-sonnet-20241022',
 messages=[{'role': 'user', 'content': premium_chat.message}]
 ).content[0].text
)
 
```

 
## Complete Cost Analysis Example

 
Track costs across all providers in a unified dashboard:

 
```python

# Cost tracking across providers
cost_analysis = pxt.create_table('operations.costs', {
 'operation_id': pxt.String,
 'provider': pxt.String,
 'model': pxt.String,
 'input_tokens': pxt.Int,
 'output_tokens': pxt.Int,
 'cost_usd': pxt.Float,
 'timestamp': pxt.Timestamp
})

# Calculate monthly costs by provider
monthly_costs = cost_analysis.select(
 cost_analysis.provider,
 total_cost=pxt.functions.sum(cost_analysis.cost_usd),
 total_requests=pxt.functions.count(),
 avg_cost_per_request=pxt.functions.avg(cost_analysis.cost_usd)
).where(
 cost_analysis.timestamp > datetime.now() - timedelta(days=30)
).group_by(
 cost_analysis.provider
).collect()

print("Monthly Cost by Provider:")
for item in monthly_costs:
 print(f"{item['provider']}: ${item['total_cost']:.2f} ({item['total_requests']} requests)")

# Find optimization opportunities
expensive_operations = cost_analysis.where(
 cost_analysis.cost_usd > 0.10
).order_by(
 cost_analysis.cost_usd, asc=False
).limit(10).collect()

print("\nMost Expensive Operations:")
for op in expensive_operations:
 print(f" {op['provider']}/{op['model']}: ${op['cost_usd']:.3f}")
 
```

 
## Seamless Provider Switching with Pixeltable

 
The killer feature: switching providers requires changing ONE line:

 
```python

# Original implementation with OpenAI
analysis_table.add_computed_column(
 summary=openai.chat_completions(
 model='gpt-4o-mini',
 messages=[{'role': 'user', 'content': analysis_table.text}]
 ).choices[0].message.content
)

# Switch to Claude (if it performs better in testing)
analysis_table.add_computed_column(
 summary=anthropic.messages(
 model='claude-3-5-sonnet-20241022',
 messages=[{'role': 'user', 'content': analysis_table.text}]
 ).content[0].text,
 if_exists='replace' # Replace existing column
)

# Or switch to Groq (if you need speed)
analysis_table.add_computed_column(
 summary=groq.chat(
 model='llama-3.1-70b',
 messages=[{'role': 'user', 'content': analysis_table.text}]
 ).choices[0].message.content,
 if_exists='replace'
)

# Pixeltable automatically:
# - Recomputes with new provider
# - Maintains version history
# - Tracks lineage
# - Handles rate limiting
 
```

 
## Production Multi-Provider Patterns

 
### Pattern: Primary + Backup

 
```python

# Use primary provider, fallback to backup on failure
@pxt.udf
def resilient_inference(text: str) -> dict:
 """Primary: OpenAI, Backup: Claude, Last Resort: Groq"""
 
 providers = [
 ('OpenAI GPT-4o-mini', lambda: openai.chat_completions(
 model='gpt-4o-mini',
 messages=[{'role': 'user', 'content': text}]
 ).choices[0].message.content),
 
 ('Anthropic Claude Haiku', lambda: anthropic.messages(
 model='claude-3-haiku-20240307',
 messages=[{'role': 'user', 'content': text}]
 ).content[0].text),
 
 ('Groq Llama', lambda: groq.chat(
 model='llama-3.1-8b',
 messages=[{'role': 'user', 'content': text}]
 ).choices[0].message.content)
 ]
 
 for provider_name, func in providers:
 try:
 result = func()
 return {
 'text': result,
 'provider': provider_name,
 'status': 'success'
 }
 except Exception as e:
 continue
 
 return {
 'text': 'All providers unavailable',
 'provider': 'none',
 'status': 'failed'
 }
 
```

 
### Pattern: Specialized Model Selection

 
```python

# Use each provider for what it does best
multimodal_content = pxt.create_table('content.multimodal', {
 'image': pxt.Image,
 'text': pxt.String,
 'document': pxt.Document
})

# Vision: OpenAI GPT-4o (best vision quality)
multimodal_content.add_computed_column(
 image_analysis=openai.chat_completions(
 messages=[{
 'role': 'user',
 'content': [
 {'type': 'text', 'text': "Analyze this image in detail"},
 {'type': 'image_url', 'image_url': {'url': multimodal_content.image}},
 ],
 }],
 model='gpt-4o',
 ).choices[0].message.content
)

# Text reasoning: Claude (best for complex logic)
multimodal_content.add_computed_column(
 text_analysis=anthropic.messages(
 model='claude-3-5-sonnet-20241022',
 messages=[{'role': 'user', 'content': multimodal_content.text}]
 ).content[0].text
)

# Document processing: Claude (large context window)
from pixeltable.functions.document import extract_text

multimodal_content.add_computed_column(
 doc_text=extract_text(multimodal_content.document)
)

multimodal_content.add_computed_column(
 document_summary=anthropic.messages(
 model='claude-3-5-sonnet-20241022',
 messages=[{'role': 'user', 'content': multimodal_content.doc_text[:100000]}],
 max_tokens=2048
 ).content[0].text
)

# Best of all worlds in one table
 
```

 
## Multi-Provider Best Practices

 
### Decision Criteria Checklist

 

 - 🎯 **Cost-Sensitive?** → GPT-4o-mini or Gemini Flash

 - ⚡ **Need Speed?** → GPT-4o or Gemini Flash

 - 🧠 **Complex Reasoning?** → Claude 3.5 Sonnet

 - 👁️ **Vision Tasks?** → GPT-4o or Claude 3.5

 - 📄 **Long Documents?** → Claude (200K context)

 - 💼 **Enterprise Compliance?** → AWS Bedrock or Azure OpenAI

 - 🌍 **Multimodal Generation?** → Gemini (Imagen, Veo)

 - 💻 **Code Generation?** → Claude or GPT-4o

 - 🚀 **Emerging Options?** → Groq, Deepseek (check latest docs)

 

 
### Monitoring Provider Performance

 
```python

# Track provider performance metrics
provider_metrics = test_queries.select(
 test_queries.provider,
 avg_quality=pxt.functions.avg(test_queries.quality_score),
 avg_latency=pxt.functions.avg(test_queries.latency_ms),
 success_rate=pxt.functions.sum(
 test_queries.status == 'success'
 ) / pxt.functions.count(),
 total_cost=pxt.functions.sum(test_queries.cost)
).group_by(
 test_queries.provider
).collect()

# Make data-driven provider decisions
for provider in provider_metrics:
 print(f"{provider['provider']}:")
 print(f" Quality: {provider['avg_quality']:.2f}")
 print(f" Speed: {provider['avg_latency']:.0f}ms")
 print(f" Success Rate: {provider['success_rate']:.1%}")
 print(f" Cost: ${provider['total_cost']:.2f}")
 
```

 
## Conclusion: The Multi-Provider Future

 
The era of single-provider AI applications is over. As the ecosystem matures, teams optimize by using the best provider for each specific task: OpenAI for reliable general-purpose work, Claude for complex reasoning and document analysis, Gemini for multimodal generation, and emerging providers like Groq and Deepseek for specialized needs.

 
 
Pixeltable makes this multi-provider strategy practical by providing a [unified interface](/blog/declarative-multimodal-incremental) across all providers. No more wrestling with different SDKs, API patterns, or integration code. Define your logic once, switch providers with a single line change, and let Pixeltable handle the complexity.

 
 
Whether you're optimizing for cost, performance, compliance, or quality, a multi-provider strategy gives you flexibility and competitive advantages. The teams building the most sophisticated AI applications aren't locked into a single provider. They're using the best tool for each job. With Pixeltable's support for OpenAI, Anthropic, Gemini, AWS Bedrock, and emerging providers, you have the infrastructure to adapt as the AI landscape evolves.

 
## Master Multi-Provider AI Development

 

 - **[Claude Integration Guide](/blog/claude-anthropic-multimodal-integration-pixeltable)** - Anthropic/Claude with Pixeltable

 - **[Gemini Integration Guide](/blog/working-with-gemini)** - Google's multimodal models

 - **[Production Rate Limiting](/blog/rate-limiting)** - Managing provider quotas

 - **[Production RAG Systems](/blog/production-rag-data-centric)** - Multi-provider RAG architecture

 - **[AI Agent Architecture](/blog/practical-guide-building-agents)** - Building agents with multiple providers

 - **[OpenAI Pricing](https://openai.com/pricing)** - Official pricing details

 - **[Anthropic Pricing](https://www.anthropic.com/pricing)** - Claude pricing

 - **[Groq](https://groq.com)** - LPU inference platform

 - **[Pixeltable on GitHub](https://github.com/pixeltable/pixeltable)** - Multi-provider examples

 - **[Join our Discord](https://discord.gg/QPyqFYx2UN)** - Discuss provider strategies

 

 
*Don't get locked into a single AI provider. Build flexible infrastructure that gives you the freedom to use the best model for every task.* 🎯