Tooling Catalog
Model Providers and Gateways
Model APIs, routing layers, gateway controls, realtime, voice, and multimodal providers
Last verified 2026-09-24
These tools matter when provider routing, latency, modality, governance, or hosted model tools become product infrastructure.
Model Providers and Gateways
Keep model choice behind a routing layer when possible. Prefer providers with strong , , , or multimodal support.
Model-Choice Axes
A handful of axes cover most model-selection decisions, independent of which provider you pick:
- Size tier - a larger model trades cost and latency for accuracy; a smaller one trades accuracy for cost and speed. Match the tier to what the step needs.
- Context window size - how much the model can hold at once; a task that needs to reason over a large document or long history needs a large window regardless of size tier.
- Reasoning models and effort - some models support an explicit effort setting that trades latency for more deliberation before answering. Set it per call: raise it for hard steps and leave it low elsewhere.
A practical sequencing: start with a hosted frontier model to prove the task is solvable at all and establish a quality baseline, then move down size tiers, or to open-weight models, once the task is well-understood, to cut cost while checking the still pass.
| Tool | Website | What it is for | Tier | Notes |
|---|---|---|---|---|
| OpenAI | platform.openai.com | Frontier models, Responses , tools, embeddings, realtime, image, speech, and evals | Core | Core provider for agentic tool use, structured output, hosted tools, and OpenAI-native workflows. |
| Anthropic | platform.claude.com | Claude models, tool use, long context, coding agents | Core | Strong reasoning and coding-agent ecosystem. |
| Google Gemini | ai.google.dev | Gemini models, multimodal inputs, long context | Core | Strong multimodal option, especially when Google Cloud alignment matters. |
| Google Vertex AI | cloud.google.com | Enterprise model hosting and Gemini on GCP | Conditional | Use when governance, service accounts, regions, or GCP data access matter. |
| Azure AI Foundry | learn.microsoft.com | Azure-hosted model platform and enterprise AI tooling | Conditional | Best when Microsoft enterprise controls are required. |
| Amazon Bedrock | docs.aws.amazon.com | AWS-hosted foundation models and agent services | Conditional | Best when the app and data already live in AWS. |
| Mistral AI | docs.mistral.ai | European model provider with language, coding, and embedding models | Core | Durable independent provider with strong ecosystem support. |
| Cohere | docs.cohere.com | Reranking, embeddings, and language models | Conditional | Especially relevant for retrieval quality. |
| Groq | console.groq.com | Low-latency | Conditional | Useful for fast open-model serving. |
| xAI | docs.x.ai | Grok models | Conditional | Use when model behavior, latency, or pricing fits a specific product need. |
| OpenRouter | openrouter.ai | Multi-provider gateway | Core | Strong for experimentation and model optionality, but keep production policy explicit. |
| Vercel AI Gateway | vercel.com | Managed model gateway for AI SDK apps | Core | Best fit when deploying AI SDK apps on Vercel. |
| Cloudflare AI Gateway | developers.cloudflare.com | Managed model gateway with caching, , , observability, key storage, retries, and dynamic routing | Conditional | Strong when model policy should sit near Workers, edge services, or Cloudflare-managed AI infrastructure. |
| LiteLLM | docs.litellm.ai | OpenAI-compatible gateway and proxy across many providers | Conditional | Useful for self-hosted routing, budgets, logging, and provider abstraction. |
| Cloudflare Workers AI | developers.cloudflare.com | Serverless inference for open models on Cloudflare's global network | Conditional | Strong for Cloudflare-native apps, edge workloads, and models colocated with Workers. |
| Hugging Face Inference | huggingface.co | Hosted open-model inference | Conditional | Good for breadth of open models and quick experiments. |
| Together AI | docs.together.ai | Hosted open-model inference | Conditional | Common OpenAI-compatible provider for open models. |
| Fireworks AI | docs.fireworks.ai | Fast hosted open-model inference | Conditional | Use when a supported open model and latency profile fit. |
| DeepInfra | deepinfra.com | Hosted open-model inference | Conditional | Cost-effective OpenAI-compatible option for many workloads. |
| Perplexity | docs.perplexity.ai | Search-grounded model API | Conditional | Use for answer-with-current-web-context flows, not as a general default. |
| Ollama | ollama.com | Local model serving | Conditional | Durable local development and privacy path. |
| LM Studio | lmstudio.ai | Local model serving and OpenAI-compatible endpoint | Conditional | Good for local testing, demos, and offline workflows. |
Gateway and Runtime Controls
Use these capabilities when model access becomes production infrastructure rather than a single SDK call.
Multimodal and Voice AI
Only include these when media or voice is part of the agent product.
| Tool | Website | What it is for | Tier | Notes |
|---|---|---|---|---|
| OpenAI Realtime | platform.openai.com | Realtime speech, audio, and multimodal interaction | Conditional | Strong for voice agents in OpenAI-centric systems. |
| Vapi | vapi.ai | Platform for building and deploying voice | Conditional | Strong option when the product needs phone/web voice agents quickly. |
| OpenAI Image Generation | platform.openai.com | Image generation and editing | Conditional | Good default when already using OpenAI. |
| Google Gemini Live API | ai.google.dev | Low-latency bidirectional Gemini audio and video interactions | Conditional | Good when the agent stack is Gemini/Google-aligned; note that Google AI docs still mark Live API capabilities as preview. |
| ElevenLabs | elevenlabs.io | Text-to-speech, voice cloning, and conversational voice agents | Conditional | Strong specialized voice provider. |
| Deepgram | deepgram.com | Speech-to-text and voice AI APIs | Conditional | Strong transcription and voice-agent infrastructure. |
| AssemblyAI | assemblyai.com | Speech-to-text and audio intelligence | Conditional | Useful for transcription-heavy systems. |
| Azure AI Speech | learn.microsoft.com | Enterprise speech services | Conditional | Best when Azure compliance and enterprise controls matter. |
| Google Speech-to-Text | cloud.google.com | Speech recognition | Conditional | Best when aligned with Google Cloud. |
| Speechify API | docs.speechify.ai | Text-to-speech, voice cloning, and real-time voice agents | Watch | Consider for consumer-facing voice and narration workflows; verify language, latency, and licensing requirements per product. |
| Sarvam AI | docs.sarvam.ai | Indian-language chat, translation, speech-to-text, text-to-speech, transliteration, and language identification APIs | Conditional | Strong regional option when Indic language coverage is a product requirement. |
| Murf API | murf.ai | Text-to-speech and streaming voice generation APIs | Watch | Consider for voiceover and conversational audio, but validate real-time performance and licensing before making it core. |
| Agora Conversational AI | agora.io | Realtime communications infrastructure for voice AI experiences | Conditional | Relevant when voice agents need WebRTC, telephony, and low-latency media infrastructure. |
| Replicate | replicate.com | Hosted model inference for image, video, audio, and open models | Conditional | Good for quick access to creative and open-source models. |
| fal | fal.ai | Fast media model inference | Conditional | Strong for image/video generation workloads. |
| Runway | docs.dev.runwayml.com | Video generation APIs | Watch | Use only when video generation is core to the product. |
| Luma AI | docs.lumalabs.ai | Image and video generation APIs | Watch | Track for media-agent workflows. |