You're building a web application that needs AI capabilities. Maybe you want to add a chatbot, generate content summaries, or analyze user-uploaded images. The decision of which AI API to integrate affects your development timeline, monthly costs, and application performance in ways that become apparent only after you've committed significant engineering time.

This guide examines eight major AI API providers across five dimensions: pricing structures, context window limits, rate limiting approaches, SDK support, and enterprise features. We'll start with OpenAI and Anthropic (the high-volume leaders), move through Google Gemini (massive context windows), cover enterprise platforms (AWS Bedrock and Azure OpenAI), then explore specialized providers (Cohere, Mistral, and open-source platforms).

In brief

• OpenAI's GPT-4o-mini delivers 16.7x lower costs compared to GPT-4o while maintaining identical features, making it optimal for high-volume applications at $0.15 per million input tokens.
• Google Gemini offers 1,048,576 token context windows, currently the largest publicly available, with free tiers for the first 200k tokens, removing the need for chunking large documents.
• AWS Bedrock and Azure OpenAI provide enterprise-grade security with 99.9% uptime SLAs, VPC integration, and compliance certifications (SOC 2, HIPAA, GDPR).
• Authentication universally requires server-side API key storage across all providers, with exponential backoff retry strategies for 429/500/503 errors.

1. OpenAI API

OpenAI maintains its position as the most widely adopted AI API provider, with GPT-4o serving as the primary multimodal model. The API accepts both text and image inputs with a 128,000 token context window, supporting streaming responses, function calling, structured outputs, and fine-tuning capabilities.

The current model lineup includes GPT-4o and GPT-4o-mini, both featuring the same 128,000 token context window and vision capabilities. GPT-4o-mini maintains full feature parity with GPT-4o but is optimized for cost-sensitive applications.

GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens, while GPT-4o-mini delivers the same capabilities at $0.15 per million input tokens and $0.60 per million output tokens. The pricing difference becomes significant at scale. A chatbot processing 10M tokens daily (assuming equal input/output) pays $1,875/month with GPT-4o versus $112/month with GPT-4o-mini.

OpenAI implements a five-tier rate limiting system based on cumulative spend and account age. The Free tier caps at $100 monthly, while Tier 5 requires $1,000 paid spend and 30 days of account age, unlocking $200,000 monthly limits. Response headers including x-ratelimit-remaining-requests and x-ratelimit-reset-requests help you track usage in real-time.

Strengths:

• GPT-4o-mini costs $0.15/$0.60 per million tokens, representing 94% savings versus GPT-4o.
• Prompt caching reduces costs by 50% for repeated content patterns.
• Structured outputs force JSON responses to match your schema.
• Function calling lets you integrate external systems with verified schema compliance.

Weaknesses:

• 128k token context window smaller than competitors (Google Gemini: 1M+, AWS Bedrock: 1M).
• 99.9% uptime SLA requires enterprise Scale Tier upgrade.
• Rate limit progression requires spending thresholds with 7-30 day waiting periods.

Use Cases:

GPT-4o-mini works well for high-volume production applications where cost optimization matters. Consider a customer support chatbot: with GPT-4o-mini, a 500-token average conversation (250 input, 250 output) costs $0.0001875 per interaction. At 1,000 daily conversations, that's $5.63/month versus $93.75/month with GPT-4o.

The cost difference funds additional infrastructure or features. For applications requiring strict JSON outputs, such as populating database fields or generating structured content for Content API, OpenAI's structured outputs mode gets rid of parsing errors that plague regex-based validation. The function calling capabilities integrate with REST endpoints to query, create, or update content directly through conversational interfaces.

Developers building applications with headless CMS architecture can use function calling to query content directly through AI interfaces, which you can manage through admin panels.

2. Anthropic Claude API

Anthropic transitioned to Claude 4.x models in late 2025, retiring all Claude 3.x versions between October 28, 2025 and January 5, 2026. This rapid deprecation cycle means migration planning becomes part of your integration timeline when working with Anthropic.

Claude Sonnet 4, Claude Opus 4.5, and Claude Haiku 4.5 now offer 1 million token context windows, significantly exceeding OpenAI's 128,000 token limit. The @anthropic-ai/sdk v0.71.2 TypeScript SDK released December 5, 2025, gives you official Node.js support with streaming helpers.

Anthropic enforces rate limits using a token bucket algorithm across Requests Per Minute (RPM), Input Tokens Per Minute (ITPM), and Output Tokens Per Minute (OTPM). Cached tokens generally don't count toward input token rate limits, providing significant cost advantages for applications with repeated prompts.

Claude Haiku 4.5 offers competitive pricing at $0.50 per million input tokens and $2.50 per million output tokens. Historical reference from Claude 3.x migration documentation. Review current Claude 4.x pricing for production deployments. All Claude 3.x models have been retired, and Anthropic recommends immediate migration to Claude 4.x models for new development projects.

Strengths:

• 1M token context windows for processing entire documents without chunking.
• Prompt caching delivers 50% cost reduction for repeated content.
• Strong reasoning for multi-step logic and nuanced analysis.
• SDK maintains active development with multiple releases per month.

Weaknesses:

• No publicly available SLA commitments.
• Rapid model deprecation cycle (Claude 3.x deprecated within 3 months).
• Rate limit specifics vary by tier and model generation.

Use Cases:

The 1M+ token context window removes the need for chunking strategies that introduce complexity. Instead of implementing RAG with vector databases, you can pass entire codebases directly to Claude. For a 50-file application averaging 500 lines per file (roughly 200k tokens), Claude processes the full context in a single request.

You can store these documents using content management systems and trigger Claude analysis through webhook events. The prompt caching feature shines here: cache your codebase as system context, then pay only for query tokens. At $0.50 per million cached input tokens (90% discount), a 200k token codebase costs $0.10 per cache, then $0.05 per million tokens for cache reads.

A development tool making 1,000 queries per day pays $0.10 for caching plus $0.05 for reads versus $500 without caching. The reasoning capabilities suit applications requiring nuanced analysis, such as AI-powered content workflows. Content API gives you RESTful endpoints for Claude integration.

3. Google Gemini API

Google's Gemini lineup includes the latest Gemini 3 series and stable Gemini 2.5 production models. Gemini 3 Pro and Gemini 3 Flash both support 1,048,576 input tokens and 65,536 output tokens, representing the largest publicly available context windows. These models accept text, images, video, audio, and PDF documents.

Gemini 2.5 Flash Live introduces real-time capability with native audio generation as an output modality. This model supports 131,072 input tokens and 8,192 output tokens while processing audio, video, and text inputs using 16-bit PCM format at 16kHz for input and 24kHz for output. The low-latency streaming over WebSocket supports conversational AI with barge-in capability.

Google implements a hybrid pricing model with generous free tiers. Both Gemini 2.5 Pro and Gemini 2.5 Flash offer completely free access for the first 200k tokens. The free tier works well for prototypes, but here's the catch most teams miss—your usage data improves Google's models unless you're on a paid plan.

If you're processing proprietary content or customer data, budget for paid tiers from day one. Retrofitting data privacy after launch creates compliance headaches. When exceeding free tier limits, Gemini 2.5 Flash costs $0.30 per million tokens for text/image/video inputs and $2.50 per million output tokens. Gemini 3 Flash gives you even lower pricing at $0.50 per million tokens for inputs with a 50% Batch API discount reducing these rates further.

Strengths:

• 1,048,576 token context windows handle entire codebases without chunking.
• Free tier gives you prototyping without costs.
• 50% cost reduction with Batch API.
• Gemini 2.5 Flash Live supports native audio input/output.
• Context caching offers 90% cost reduction for repeated prompts.

Weaknesses:

• Free tier content used to improve Google products.
• Model availability varies by region.
• Complex token-based pricing structure with different rates for audio versus text/image/video.
• Gemini 2.5 Flash Live WebSocket streaming adds infrastructure complexity.

Use Cases:

Gemini's massive context window works well for applications processing large documents, such as content migration workflows analyzing entire site structures. Integration with backend systems supports seamless document processing and analysis. The Batch API's 50% discount makes Gemini 3 Flash particularly attractive for non-real-time workflows.

A content migration analyzing 1 million blog posts at 1,000 tokens each costs $250 with standard API versus $125 with Batch API. Submit batches overnight, retrieve results in the morning. For real-time applications, Gemini 2.5 Flash Live's native audio capabilities support building voice-based content creation tools with low-latency streaming.

The barge-in capability lets users interrupt AI responses naturally, essential for conversational interfaces where users want to redirect mid-response. The Google Maps grounding feature supports location-aware content generation for travel or local business applications.

4. AWS Bedrock

AWS Bedrock gives you unified API access to multiple foundation model families through serverless infrastructure. The platform includes Amazon's proprietary Nova family alongside third-party models like OpenAI's GPT-OSS models, supporting multi-model strategies within a single API interface.

The Nova family includes models with context windows ranging from 128,000 to 1,000,000 tokens. Nova Premier offers enhanced multimodal capabilities, while specialized Nova models handle specific functions: Canvas for image generation, Reel for video generation, and Sonic for speech understanding.

Bedrock implements usage-based pricing with token-based charges. Batch processing offers approximately 50% cost discounts for non-real-time workloads. Bedrock's higher per-token costs become significant at scale. Processing 10M tokens daily with Nova models costs substantially more than GPT-4o-mini or Gemini Flash alternatives. Custom model import and fine-tuning incur additional hourly charges based on instance type and customization method.

AWS Bedrock supports three customization approaches: fine-tuning for task-specific adaptation, continued pre-training for domain-specific knowledge extension, and reinforcement fine-tuning using human feedback.

Here's where this gets interesting for enterprise teams already on AWS: the multi-model access means you can A/B test OpenAI, Anthropic, and Meta models through a single API integration. Organizations building API-first applications can integrate Bedrock endpoints alongside REST and GraphQL APIs for unified content plus AI workflows. You pay AWS's markup, but you get rid of vendor lock-in risks and cross-cloud networking complexity. Whether that trade-off works depends on your monthly token volumes.

Strengths:

• Multi-model access through unified API supporting flexible model selection.
• 99.9% uptime SLA with graduated service credits.
• AWS PrivateLink integration keeps API traffic within AWS network.
• Three-tier customization approach for specialized workflows.
• Compliance certifications including SOC 2, HIPAA, GDPR, ISO 27001.

Weaknesses:

• Higher inference costs for large-scale deployments.
• Model availability varies significantly by AWS region.
• Requires AWS infrastructure knowledge for optimal configuration.

Use Cases:

Bedrock suits enterprise deployments with existing AWS infrastructure commitments. Organizations requiring headless CMS deployments on AWS can integrate Bedrock without cross-cloud networking complexity.

The multi-model access supports A/B testing different AI providers through a single API, reducing vendor lock-in risks. Cross-region inference benefits globally distributed applications built with API-first architecture. The three-tier customization approach supports specialized content generation workflows where generic models underperform.

5. Azure OpenAI Service

Azure OpenAI Service gives you access to OpenAI models including GPT-4.1 series and GPT-4o variants, with enterprise-grade security and compliance certifications. The service guarantees that customer data is not used to retrain foundation models, providing explicit privacy protections for proprietary information.

Azure OpenAI Service implements per-token billing with regional variation. GPT-4o in the East US region costs $5 per million input tokens and $15 per million output tokens. Enterprise plans include volume discounts and dedicated account management.

Azure integration gives you native VNET support with configurable network security policies. If your team already manages Azure infrastructure, this integration feels natural. VNET configurations use the same tooling you already know. Otherwise, it adds another platform to learn, and Azure's learning curve isn't trivial.

Don't underestimate the onboarding time if you're coming from AWS or GCP. Role-Based Access Control integrates with Azure Active Directory for team management. The platform achieves 99.9% uptime SLA with standard Azure service credit structure.

Fine-tuning support covers GPT-4o, GPT-4.1, and Llama 2 models through Microsoft Foundry portal, Python SDK, and REST API interfaces.

Strengths:

• Explicit guarantee that customer data doesn't train models.
• 99.9% uptime SLA for deployments.
• Native VNET integration and private endpoint support.
• Azure Resource Manager supports infrastructure-as-code.
• Compliance certifications including SOC 2, HIPAA, GDPR, ISO 27001.

Weaknesses:

• Regional model availability varies by Azure region.
• Quota limits managed per subscription and region.
• Pricing varies by region and model type.

Use Cases:

Azure OpenAI Service suits Microsoft enterprise customers with existing Azure commitments. The platform gives you explicit data privacy guarantees for applications handling proprietary content such as internal knowledge bases built with TypeScript development, or customer data analysis.

Azure supports fine-tuning through Python SDKs, REST APIs, and Azure Resource Manager. VNET integration supports hybrid cloud deployments combining on-premise and Azure resources through configurable network security policies.

6. Cohere API

Cohere specializes in retrieval-augmented generation (RAG) workflows with purpose-built tooling. Cohere organizes models into four categories: Command for generation, Embed for vectors, Rerank for search optimization, and Aya for multilingual work. The specialization makes integration decisions straightforward. If you need RAG, you're looking at Embed plus Rerank plus Command in combination.

Command A offers a 256,000 token context window at $0.010 per thousand input tokens. Command R7B costs 81.25% less at $0.001875 per thousand input tokens while maintaining a 128,000 token context window. Official SDKs span Python, TypeScript, Java, and Go.

Strengths:

• Purpose-built RAG tooling with Embed plus Rerank plus Command integration.
• Rerank API processes up to 1,000 documents per request.
• 81.25% cost reduction with Command R7B versus Command A.
• Official SDKs for Python, TypeScript, Java, and Go.

Weaknesses:

• Specialized focus limits general-purpose generation capabilities.
• No published SLA for API availability.
• Smaller context windows than competitors (256k max versus Google Gemini's 1M+).

Use Cases:

Cohere works well for search-heavy applications requiring sophisticated retrieval. A content recommendation engine built with backend customization can use Embed models to create vector representations of articles, Rerank models to optimize search results, and Command models to generate personalized summaries.

The Rerank API accepts up to 1,000 documents per request, returning relevance scores that inform result ordering. Integration pattern: retrieve top 100 results from search, pass to Rerank, display top 10 with confidence scores. Cohere's Embed models generate 768 or 1024-dimensional vectors depending on model choice. Store vectors in Postgres with pgvector extension for sub-50ms similarity searches at scale, combining content management with semantic search capabilities.

7. Mistral AI API

Mistral AI differentiates through open-weight models available for self-hosting alongside managed API access, offering deployment flexibility similar to Strapi's deployment options. The model portfolio includes Featured Models (production models), Frontier Models (highest-capability), and Specialist Models (domain-specific optimization).

Mistral gives you both hosted API access and open-weight models for self-deployment scenarios. The platform supports multiple inference engine implementations, providing flexibility for on-premise deployments in regulated environments.

Strengths:

• Self-hosting option for data sovereignty requirements.
• Open-weight models support on-premise deployment.
• Multiple inference engine implementations.
• Flexible deployment between cloud API and self-hosted.

Weaknesses:

• Smaller ecosystem compared to OpenAI or Google.
• Limited documentation for self-deployment scenarios.
• Open-weight model performance may lag behind proprietary alternatives.

Use Cases:

Mistral suits organizations with data sovereignty requirements prohibiting cloud AI processing. The self-deployment capabilities give you infrastructure control for production workloads while accessing current-generation models.

Choosing the Right AI API for Your Stack

Selecting an AI API requires balancing cost, capabilities, and infrastructure fit. GPT-4o-mini ($0.15/$0.60 per 1M tokens) and Gemini 3 Flash ($0.50/$3.00 per 1M tokens) optimize for cost; Claude and Gemini's million-token windows eliminate chunking; Bedrock and Azure OpenAI provide enterprise compliance.

These integration patterns—function calling, structured outputs, prompt caching—pair naturally with API-first architectures. If you're building with a headless CMS like Strapi, AI APIs connect through REST and GraphQL endpoints, webhooks, and backend customization. Start with Strapi's native AI features for internationalization and metadata generation, then extend with marketplace plugins and custom integrations as needs grow.

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