OpenAI API vs Google Cloud AI: which AI API is best for your startup in 2024?

As a business owner, you need to make lots and lots of important decisions. As an AI startup owner or not yet AI but trying to dip your toes in it, one of those decisions is AI API. There are a few in the market, and some are better than others for specific use cases and needs. 

For example, if your startup focuses on advanced NLP tasks like conversational agents or content generation), one AI API is likely to be the better option due to its superior performance in those areas. 

Or perhaps you need an AI platform that handles multimodal inputs and offers strong integration with existing enterprise systems? 

Today, we are comparing OpenAI API vs Google Cloud AI, and by the end of this article, you’ll know which to choose for any of these use cases and more.

AI API recommendations for startups

Before we move to the actual OpenAI API vs Google Cloud AI comparison, we’d like to take a quick detour into what actually matters when choosing an AI API for a startup. This way, you’ll know better how to read and assess all the technical specifications regarding each API.

Assess your use case. 

What API capabilities does your specific startup need? If NLP is at the heart, OpenAI might be more suitable; for a wider range of AI applications, Google Cloud could be better.

Budget considerations. 

To predict your costs more accurately, analyze the pricing models of the APIs you are considering in more detail. Also, check your current needs, but don’t forget that you need that future scalability, too.

Technical expertise. 

Check your team's expertise! If you need more skilled workers that specialize in AI, then perhaps dedicated teams might be what you need. In general, OpenAI offers simplicity, while Google Cloud may require more in-depth knowledge of cloud services.

Scalability plans. 

Think about your growth trajectory, too. What’s in store for your company? Some AI API infrastructures might offer more and better options for scaling large applications.

Compliance needs. 

Some industries need stricter compliance requirements. If yours is one of them, then perhaps Google's certifications will provide more necessary assurances.

Trial periods. 

Of course, always check free tiers or trial offers to test both platforms before committing, as with all the platforms, really. Try before you buy.

OpenAI API vs Google Cloud AI: comparison

So, since OpenAI API and Google Cloud AI's Gemini are the two leading platforms right now (apart from a few large and capable alternatives) that offer AI services to developers and businesses, we’re going to focus on them and compare them using the following categories - capabilities, integrations, pricing, customizations and scalability, and security and support. 

Capabilities

Both OpenAI and Gemini offer a lot of good stuff in terms of text generation and image understanding. OpenAI mainly focuses on text and image processing. It supports formats like PNG, JPEG, WebP, and non-animated GIFs.

Gemini goes a step further by also offering audio and video processing. It supports audio formats like WAV, MP3, and FLAC and video formats like MP4 and AVI. Gemini also handles images up to 20MB per prompt, supports more image formats (including HEIC and HEIF), and allows higher resolutions. 

When it comes to function and tool handling, OpenAI allows parallel tool calls, which means it can handle multiple tools at once. Gemini doesn’t support this yet but offers built-in code execution and semantic caching, and that can also improve performance by storing and reusing previous computations.

Integrations

Both platforms allow you to integrate external tools and functions into your applications. They use a "tools array" to define these integrations, so it’s easier to manage and invoke different tools.

OpenAI uses a system message for instructions and lets you send new tool messages with specific identifiers that can streamline the integration process. Gemini uses a parts array for system instructions and requires clients to append function messages with a function response part.

To rephrase, if you need AI API for applications that involve visual data, both platforms support tool use with vision capabilities. However, Gemini's support for audio and video formats means it can integrate more seamlessly with multimedia applications.

Pricing

The Google Cloud AI API is character-based, meaning that it bills based on the amount of characters sent and received. This benefits some languages, such as French and German. 

On the other hand, the OpenAI API pricing is token-based, so the billing is based on the number of tokens exchanged between the user and the model. This favors English speakers, as English is the cheapest language for both of OpenAI's tokenizers.

Google's character-based billing model seems to be a great deal for Japanese or Korean speakers, while OpenAI's token-based approach appears to favor English speakers. 

However, it is important to consider that pricing is not the sole factor in choosing between the two APIs. For example, the power of these models should also be on your personal list of considerations for your startup.

Customizations and scalability

Take a look at customization options, too, if you need to adjust the AI services to your more specific needs. 

With both OpenAI and Gemini’s APIs, you can adjust settings like temperature (which affects the randomness of outputs), maximum tokens (limiting the length of responses), and stop sequences (to control where responses end).

OpenAI offers additional customization with the ability to set a random seed and generate multiple response candidates using the 'n' parameter, although this might affect streaming capabilities. Gemini supports the 'top_k' parameter that lets you adjust the number of highest-probability vocabulary tokens to keep for generation.

In terms of scalability, both platforms support streaming responses, which means they can handle simultaneous data processing in actual time. This feature is pretty much necessary for applications like chatbots or live data analysis.

Security and support

Security is definitely a key concern when dealing with AI services, whatever they are. 

OpenAI uses a Moderation API to handle safety settings, and that helps filter out inappropriate content. Gemini offers detailed, category-based safety settings and provides safety feedback in its responses, which means you’ll have more control over your content moderation.

Another nice thing is that both platforms delete images after processing to protect user data. OpenAI provides optional usage metrics in streams and has error handling features, although some are undocumented. Gemini's documentation on error handling and usage metrics isn't specified, so you might need to consult their support for detailed information.

OpenAI API vs Google Cloud AI: which is best for your startup?

So, a quick sum-up. Basically, see how many of the points each of the APIs score based on your needs.

Consider OpenAI API if:

• Your and your startup’s main focus is on advanced natural language processing tasks like text and image processing.
• You need the ability to handle multiple tools simultaneously.
• You need state-of-the-art language models with minimal setup.
• Quick prototyping and deployment are also of the essence.
• You need customization features like setting a random seed or generating multiple response options.
• You prefer a more straightforward pricing based on usage.

Consider Google Cloud AI API if:

• You need a more broad spectrum of AI services (e.g., audio and video processing, vision, speech, translation) beyond just NLP.
• You require support for higher image resolutions and more image formats.
• Integration with other cloud services and infrastructure is very important for you.
• You need more extensive customization and control over models.
• You need detailed safety settings and feedback for better content moderation.

Overall, the advice is very simple - the best choice depends on the scope of your startup’s AI needs. OpenAI excels in generative AI and language tasks, while Google Cloud AI provides a more extensive, enterprise-friendly platform for varied AI deployments. 

If you need more help, don’t hesitate to reach out to our Merge team - we’ve done our fair share of AI API integrations and can figure out the best solution for you.