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Multimodal Content Generation Apis Model Hosting Inference Agent Platforms Frameworks Building Tools Agent Platforms Orchestration AI Agents AI Agents Infrastructure

MuAPI

✓ verifiedPaid

Unified pay-per-generation API for 500+ image, video and audio models like FLUX, Kling and Seedance at low cost.

411K visits/mo

Fireworks AI

✓ verifiedPaid

Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.

611K visits/mo1.3K saves

Modal

✓ verifiedFreemium

Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.

988K visits/mo

Dify.ai

✓ verifiedFreemium

Open-source platform to build, deploy and monitor agentic AI workflows and RAG apps, with cloud, self-host and enterprise options.

1.1M visits/mo

Paperclip - ing

✓ verifiedFree

Open-source, self-hosted app to manage teams of AI agents like a company - org chart, goals, budgets and per-agent approvals.

942K visits/mo

Pricing

No public pricing

On-Demand H100/H200: $7/GPU-hour

On-Demand B200: $10/GPU-hour

On-Demand B300: $12/GPU-hour

Fine-tuning (LoRA SFT, models up to 16B): from $0.50 per 1M training tokens

Starter: $0/mo + compute ($30 free credit)

Team: $250/mo + compute

Sandbox: Free (200 message credits)

Professional: $590/workspace/year

Team: $1,590/workspace/year

No public pricing

Core features

✦Single API for 500+ image, video and audio models
✦Pay-per-generation billing with no subscription
✦No charge on failed tasks
✦Workflows, agents and studio tools
✦MCP and CLI integrations, white-label option

✦Serverless per-token inference with OpenAI/Anthropic-compatible APIs
✦On-demand dedicated and reserved GPU deployments
✦Fine-tuning and reinforcement-learning training pipelines
✦Large library of open LLM, vision, image and audio models
✦Optimized inference engine for throughput and latency

✦Serverless GPU compute defined in Python
✦Sub-second container cold starts
✦Autoscale 0 to 1000+ GPUs
✦Inference, training and batch workloads
✦Secure sandboxes for untrusted code
✦Built-in logging and observability

✦Visual workflow studio for agents
✦RAG knowledge pipelines
✦Agent runtime with tools and memory
✦Marketplace of models and plugins
✦Publish as app, API or MCP tool
✦Logging, analytics and monitoring

✦Manage teams of AI agents
✦Bring-your-own-agent (any runtime/provider)
✦Org chart with roles and reporting lines
✦Goal alignment for tasks
✦Per-agent budget and cost controls
✦Ticket system with full audit trail

Use cases

→Building apps on top of many generative models via one API
→Generating images, video and audio at scale
→Cutting model API costs versus direct providers
→Deploying white-label AI generation studios

→Serving open models in production apps and agents
→Fine-tuning models on private data
→Powering code assistants, chatbots and RAG at scale

→Deploying and scaling model inference
→Fine-tuning and training models
→Running batch/parallel AI jobs
→Executing untrusted code in sandboxes

→Building AI agents and chatbots
→Creating RAG-based knowledge apps
→Deploying LLM apps at enterprise scale

→Orchestrating agents across business functions
→Running dev, marketing and research agents
→Building autonomous-business workflows
→Governing and budgeting agent work

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