OpenAI

APIs and tools for building AI products

5.0•869 reviews•

33K followers

APIs and tools for building AI products

5.0•869 reviews•

33K followers

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Foundation Models

The most powerful platform for building AI products. Build and scale AI experiences powered by industry-leading models and tools.

OpenAI Reviews

The community submitted 869 reviews to tell us what they like about OpenAI, what OpenAI can do better, and more.

5.0

Based on 869 reviews

Review OpenAI?

Reviewers mostly see OpenAI as strong, practical, and easy to build with: they praise fast, reliable models, good documentation, and broad use across coding, writing, research, structured outputs, and image generation. Makers of Wispr Flow, TestMu AI, and Databox specifically highlight quick idea testing, low-latency work, and dependable parsing and formatting. The main complaints are familiar: high or unpredictable costs, usage limits, model-update churn, limited long-term context, and occasional generic or off-target answers.

+873

Summarized with AI

Pros

AI API (42)

fast performance (28)

AI integration ease (22)

AI model variety (20)

AI productivity boost (20)

AI innovation (19)

AI text generation (16)

AI creativity enhancement (14)

AI developer experience (12)

AI natural language processing (11)

Cons

AI accuracy issues (9)

AI pricing (3)

AI environmental impact (2)

Pros

Cons

Reviews

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Most Informative

Founder Reviews (842)

Other Reviews (27)

used OpenAI to build FetchSandbox

•8 reviews

What's great

I run GPT alongside Claude in FetchSandbox's generation pipeline — parsing OpenAPI specs, discovering realistic workflows, sanity-checking output across 60+ APIs. Where GPT earns its place is breadth and reliability: it's seen every API convention under the sun, and its structured output (function calling / JSON mode) parses cleanly every time — which matters when you're generating at volume and can't babysit each result. It's the dependable workhorse: fast, consistent, and it just works. For a solo founder shipping a lot, that consistency is gold.

What needs improvement

Main friction is scaling headroom — rate limits and the tier ramp can bite when a batch job spikes, so I've had to build retries/backoff around occasional latency. Smoother burst handling for high-volume generation jobs would help. Model version churn is the other one: pinning a version and migrating on deprecation adds overhead. Minor next to how much it just works, though.

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327 views3mo ago

used OpenAI to build Velozity

•5 reviews

We wanted our users to bring the AI they already pay for, so supporting OpenAI was essential. Its models are fast and reliable for everyday work like summarising conversations, drafting replies, and turning meetings into tasks, and the developer tools made it straightforward to build on. Many of our users already rely on it, and letting them keep that choice inside Velozity removes a reason not to switch.

Alternatives Considered

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2 views7d ago

used OpenAI to build Hyperif

•1 review

At Hyperif, we rely on Azure OpenAI to power our platform, enabling us to offer a robust, secure, and scalable solution to our users. The ability to run our own dedicated instances and infrastructure ensures that we can maintain complete control over data privacy, safeguarding our users' information. Azure OpenAI allows us to leverage OpenAI’s powerful models while providing the separation and security we need. It’s the perfect balance—highly flexible, with seamless API integration that’s both easy to use and implement. This makes it simple for us to integrate advanced AI functionality into Hyperif, delivering consistent performance with peace of mind. The clear and straightforward APIs have allowed us to focus on building the features our users love, without worrying about the complexities of the underlying infrastructure. With OpenAI through Azure, we’re able to provide a secure and powerful AI-driven experience while ensuring our users’ data stays private and protected.

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256 views1yr ago

•1 review