Compare Providers

This guide helps you compare AI providers across dimensions that matter for production deployments.

Provider Landscape

Anthropic

Claude models with industry-leading safety and long context

OpenAI

GPT-4 family with strong function calling and ecosystem

Google

Gemini models with multimodal capabilities and GCP integration

AWS Bedrock

Multi-model access with enterprise security

Comparison Framework

Evaluate providers across five key dimensions:

1. Model Capabilities

Compare model capabilities across Anthropic, OpenAI, and Google:
- Context window sizes
- Multimodal support (vision, audio)
- Function/tool calling
- Structured output support
- Fine-tuning availability

2. Pricing Structure

Create a pricing comparison for Anthropic Claude, OpenAI GPT-4,
and Google Gemini. Include:
- Input token pricing
- Output token pricing
- Batch processing discounts
- Prompt caching options
- Volume discounts

3. Performance Benchmarks

What are the latest benchmark results for Claude Sonnet,
GPT-4 Turbo, and Gemini Pro on:
- MMLU (general knowledge)
- HumanEval (coding)
- GSM8K (math reasoning)
- Latency measurements
Cite specific benchmark sources.

4. Enterprise Features

Feature	Anthropic	OpenAI	Google	AWS Bedrock
SOC 2	Yes	Yes	Yes	Yes
HIPAA	Yes	Yes	Yes	Yes
Data Residency	Limited	Limited	Yes	Yes
Private Deployment	No	No	Yes	Yes
SLA	99.9%	99.9%	99.9%	99.99%

5. Developer Experience

Consider the practical aspects:

Compare developer experience across providers:
- SDK quality and documentation
- Error handling and debugging
- Rate limit management
- Streaming support
- Observability integrations

Step-by-Step Comparison

Apply Provider Blueprints

Start by applying blueprints for each provider you’re evaluating:
- Anthropic Claude Blueprint
- OpenAI GPT-4 Blueprint
- Google Vertex AI Blueprint

Define Your Evaluation Criteria

Create a scenario with your specific requirements:

name: Provider Evaluation
workload_type: chat
slo_requirements:
  p95_latency_ms: 500
  availability: 99.9
compliance:
  certifications: [SOC2, HIPAA]
  regions: [us-east-1, eu-west-1]

Generate Comparison Artifacts

Ask Lattice to create structured comparisons:

Generate a comprehensive provider comparison matrix for my
scenario. Include pricing, performance, compliance, and
developer experience. Save as a table artifact.

Calculate Total Cost of Ownership

Get cost projections for your expected usage:

Calculate monthly costs for each provider assuming:
- 50M input tokens
- 10M output tokens
- 99.9% uptime requirement
Include infrastructure costs, not just API pricing.

Assess Risk Factors

Understand the risks of each choice:

What are the key risks of choosing each provider?
Consider: vendor lock-in, pricing changes, rate limits,
geographic availability, and support quality.

Provider-Specific Considerations

Anthropic

OpenAI

Google (Vertex AI)

AWS Bedrock

Multi-Provider Strategy

Consider a multi-provider approach for:

Resilience: Failover when one provider has issues
Cost optimization: Use different models for different tasks
Capability coverage: Leverage unique strengths of each

# Example multi-provider stack
primary:
  provider: anthropic
  model: claude-sonnet-4-20250514
  use_cases: [complex_reasoning, long_context]

secondary:
  provider: openai
  model: gpt-4-turbo
  use_cases: [function_calling, structured_output]

fallback:
  provider: google
  model: gemini-pro
  use_cases: [cost_sensitive, high_volume]

Decision Framework

Score each provider on your priority criteria:

Criterion	Weight	Anthropic	OpenAI	Google
Cost	30%	9	6	8
Performance	25%	9	8	7
Compliance	20%	8	8	9
Developer UX	15%	8	9	7
Ecosystem	10%	7	9	8
Weighted	100%	8.5	7.5	7.8

Next Steps

Configure Scenarios — Define workload-specific requirements
Build Stacks — Create deployment configurations