If you are building a software product with AI capabilities, the choice of which AI platform to build on is one of the most consequential technical decisions you will make early in development. The three dominant options are OpenAI (GPT models), Anthropic (Claude models), and Google (Gemini models). Each has different API characteristics, pricing, context window sizes, performance profiles, and ecosystem maturity.
This is a practical guide for founders and product teams making this decision, not a benchmark comparison.
OpenAI: the incumbent with the richest ecosystem
OpenAI has the most mature developer ecosystem by a significant margin. The documentation is extensive, the community is large, and third-party tooling built on OpenAI's API is more developed than for any other provider.
When to build on OpenAI:
- You want access to the widest range of pre-built integrations and libraries
- Your product requires image generation (DALL-E integration)
- Your product requires real-time voice capabilities (Realtime API)
- You are building on existing tools in the OpenAI ecosystem (LangChain, LlamaIndex, and most frameworks default to OpenAI)
- You need fine-tuning capabilities for specialised domains
Limitations:
- The context window for most models (128K tokens for GPT-4o) is smaller than Anthropic's
- API pricing has changed multiple times; cost predictability can be a concern at scale
- OpenAI's pricing for high-volume applications is not always the most competitive
Anthropic: the strong choice for text and reasoning
Anthropic's Claude models are often the top performers for text-heavy, reasoning-intensive tasks. The context window (up to 200K tokens for Claude 3) is significantly larger than OpenAI's standard models, making it the stronger choice for applications that need to process long documents.
When to build on Anthropic:
- Your application processes long documents (contracts, research papers, large datasets)
- The core value of your product is reasoning quality (analysis, summaries, content generation)
- Your application needs to follow complex, multi-condition instructions reliably
- Safety and constitutional AI considerations are important for your use case
- You want the highest-quality text output with minimal hallucination in structured tasks
Limitations:
- Smaller ecosystem: fewer pre-built integrations, fewer examples, smaller community
- No image generation native to the platform
- API rate limits are more conservative than OpenAI at equivalent tiers
Google: the choice for existing Google infrastructure users
Google's Gemini models are strong across modalities (text, code, image, audio) and have deep integration with Google Cloud infrastructure. For businesses already on Google Cloud, using Google AI reduces vendor complexity.
When to build on Google:
- Your infrastructure is on Google Cloud and vendor consolidation matters
- Your product requires deep Google Workspace integration (Gmail, Drive, Docs)
- You need multimodal capabilities that span text, image, and audio natively
- Your product processes video (Gemini has strong video understanding capabilities)
- You need access to real-time Google Search results in your AI responses
Limitations:
- API ecosystem is less mature than OpenAI's
- Google's track record of product longevity is mixed
- Documentation and community support are behind OpenAI
Making the decision
For most early-stage products:
Default to OpenAI if: you are building quickly, need ecosystem support, or are not sure yet what your exact requirements are. The ecosystem support reduces development time.
Choose Anthropic if: your product's core value is text quality or reasoning in long documents. The performance difference is meaningful enough to justify the smaller ecosystem.
Choose Google if: you are already on Google Cloud or your product is deeply Google Workspace native.
You do not have to pick one forever. Many production AI applications use multiple providers for different tasks: OpenAI for image generation, Anthropic for document analysis, Google for real-time search grounding. Your architecture can use the strongest model for each specific task.
Practical considerations beyond model performance
Pricing at scale. Run your expected volume through each provider's pricing calculator before committing to architecture. API costs at 1M requests per month look very different from costs at 10K requests.
Rate limits. Early access accounts have conservative rate limits. Understand the path to higher limits before launch, especially if your product could go viral.
Latency. Different models have different response latencies. For real-time applications (chatbots, voice interfaces), latency matters more than raw quality.
Data privacy. Review each provider's data handling policies. Some enterprise customers will require specific data handling commitments that only some providers make.
Terms of service. Review what each provider allows and prohibits for your use case. Some applications in regulated industries have constraints.
Getting architecture guidance
Choosing an AI platform is part of a broader architecture decision. Our AI integration team can help you evaluate which platform best fits your specific product requirements and build your initial architecture.
Before that conversation, use the AI feasibility checker to assess your use case. Then get in touch to discuss the build.