Best AI Model APIs & Infra
This category is for developers building on top of AI rather than using it through a chat interface: foundation model APIs (an LLM API) from the major labs, plus infrastructure for running open models, hosting inference, and routing requests across providers. Pricing per token, latency, and model selection are the comparison points that actually matter here, not brand reputation. Compare per-token cost across providers before you commit; the difference for similar output quality can be an order of magnitude. See each provider's own pricing page, linked from its tool page below, for exact current rates.
What to look for
- Compare per-token pricing carefully, costs can differ by an order of magnitude between providers for similar quality.
- If you need to run open-weight models, check GPU availability and cold-start times, not just listed specs.
- Multi-provider routers are useful if you want to avoid lock-in to a single model vendor.
All 15 tools in this category
OOpenAI APIGPT models for chat, reasoning, and multimodal tasks.
AAnthropic APIClaude models via API, with long context windows.
GGemini APIGoogle's multimodal models for developers.
GGroqUltra-low-latency inference on custom LPU hardware.
TTogether AIHosted open-source models with fine-tuning support.
OOpenRouterOne API to route requests across 300+ models.
RReplicateRun and fine-tune open models with a simple API.
HHugging FaceThe largest hub for open models, datasets, and demos.
AAssemblyAISpeech-to-text and audio-understanding models delivered as a developer API.
CCohereEnterprise-focused LLM and embedding APIs for search, RAG, and chat.
LLightning AICloud platform to build, train, and deploy AI, from the PyTorch Lightning team.
EEvidently AIOpen-source evaluation and monitoring for ML and LLM systems.
LLeap AIAPIs and SDKs to add image generation and fine-tuning to your app.
SSequence MonkeyMobvoi's multimodal large-model open platform for developers.
AAlibaba Cloud BailianAlibaba Cloud's one-stop platform to build and deploy LLM apps.
Side-by-side comparison
| Tool | Best for | Pricing | Free tier? |
|---|---|---|---|
| OpenAI API | GPT models for chat, reasoning, and multimodal tasks. | Usage-based, pay per token | No |
| Anthropic API | Claude models via API, with long context windows. | Usage-based, pay per token | No |
| Gemini API | Google's multimodal models for developers. | Free tier, usage-based pricing above limits | Yes |
| Groq | Ultra-low-latency inference on custom LPU hardware. | Free tier, usage-based API pricing | Yes |
| Together AI | Hosted open-source models with fine-tuning support. | Usage-based, pay per token or per GPU-hour | No |
| OpenRouter | One API to route requests across 300+ models. | Usage-based, pay per token across providers | No |
| Replicate | Run and fine-tune open models with a simple API. | Usage-based, pay per second of compute | No |
| Hugging Face | The largest hub for open models, datasets, and demos. | Free tier, paid Pro/Enterprise and compute plans | Yes |
| AssemblyAI | Speech-to-text and audio-understanding models delivered as a developer API. | Usage-based API pricing, free tier to start | No |
| Cohere | Enterprise-focused LLM and embedding APIs for search, RAG, and chat. | Usage-based API pricing, free trial keys | No |
| Lightning AI | Cloud platform to build, train, and deploy AI, from the PyTorch Lightning team. | Free tier, plus usage-based and paid plans | Yes |
| Evidently AI | Open-source evaluation and monitoring for ML and LLM systems. | Open-source, plus paid Cloud | Yes |
| Leap AI | APIs and SDKs to add image generation and fine-tuning to your app. | Free tier, plus usage-based API pricing | Yes |
| Sequence Monkey | Mobvoi's multimodal large-model open platform for developers. | Free tier, plus usage-based API | Yes |
| Alibaba Cloud Bailian | Alibaba Cloud's one-stop platform to build and deploy LLM apps. | Usage-based, plus free trial credits | No |
FAQ
Which AI model API is cheapest?
It changes by the month as providers cut prices, so check current per-token rates rather than trusting older comparisons. Open-weight models hosted yourself are usually cheapest at scale if you can absorb the GPU and ops overhead.
What's the difference between a model API and an inference platform?
A model API (OpenAI, Anthropic, Google) gives you access to a specific closed model. An inference platform runs open-weight models for you, or lets you self-host them, which trades some quality ceiling for cost control and no vendor lock-in.
Do I need a multi-provider router?
Only if avoiding lock-in to one vendor, or routing by cost and latency across models, actually matters for your use case. For a single small project, calling one provider's API directly is simpler and has less to debug.