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API Crest
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Show DEV: API Crest — A Unified AI API Platform for Multimodal Apps

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Show DEV: API Crest — A Unified AI API Platform for Multimodal Apps

Hi DEV! 👋 We're building API Crest, a platform for developers who want to experiment with different AI models without juggling separate dashboards for every use case.

As apps move beyond text into images, video, audio, and agents, an often-overlooked challenge is the integration work: API credentials, request formats, model discovery, usage tracking, and keeping a clear view of costs.

What we built

API Crest combines a developer-facing API platform with a workspace for exploring AI capabilities.

Here's what's available today:

  • Model discovery: browse options for chat, image, video, and audio workflows.
  • API access: create API keys and access documentation for the supported protocols and model routes.
  • Usage visibility: inspect API usage and task logs.
  • Usage-based billing: review model-specific pricing, with USDT top-ups supported.
  • Experimentation: explore models before deciding what fits your application.

Model availability, API formats, and prices vary by model, so the model catalog and documentation are the source of truth.

An integration checklist that has helped us

If you're evaluating any multi-model API provider for a real project, I'd suggest starting with five checks:

  1. Pick one narrow workflow. For example, a chat assistant or a single image-generation task. Confirm the exact model ID and request format first.
  2. Handle errors explicitly. Rate limits, failed generations, and timeouts are part of normal production engineering.
  3. Track actual spend. A low-looking model price can still turn into a large monthly bill without usage visibility.
  4. Separate asynchronous media tasks. Video or other long-running generations need clear job states and sensible polling/retry behavior.
  5. Keep credentials server-side. Never ship a live API key inside a browser bundle or public repository.

We've designed API Crest with those day-to-day developer concerns in mind, and we're still learning from the people who use it.

Try it, and tell us what's missing

I'd especially love feedback from indie hackers, SaaS builders, and people working on AI agents.

What's the biggest obstacle when evaluating a new AI API provider? Is it pricing transparency, model compatibility, latency, error handling, payment flexibility, or something else?

I'm happy to answer questions about the platform and learn from what you're building.

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