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Realtime voice models and open LLM inference behind one developer API
Voice AI
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AI Model and API Platforms
Find platforms for accessing, deploying, and managing AI models. Compare model catalogs, API access, inference services, fine-tuning, monitoring, and cost controls.
How to choose
- Pick by goal: quick access to many models depends on catalog coverage and a unified interface, self-hosting open models depends on GPU scheduling and deployment tooling, and enterprise use needs private networking, audit logs, and quotas.
- Cost control is central: the same model is priced very differently across platforms, so mind token versus request versus time billing and free-tier rate limits, and run a small load test before launch.
- Watch ecosystem and reliability: SDK languages, streaming, function calling, retries, and regional availability decide long-term maintenance cost.
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Frequently asked questions
Model API platforms vs. official APIs directly?
Aggregators give you one key for many models with unified billing and easy switching, great for validation and mid-scale use. Official APIs win on day-one new models, exclusive features, and quotas. Move critical models to direct connections at scale.
Is self-hosting worth it?
Keeping data in-network and lower cost at sustained high volume are the wins; the price is GPU operations, model updates, and scaling. Self-host for compliance or heavy usage, and validate demand on managed services first.
How do I control API costs?
Set spend caps and alerts, choose smaller models per task, cache repeated results, and trim context length. Replay real traffic to estimate per-call cost before launch so bills don't surprise you.