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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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Reka Core
✓ verifiedPaid
AI research lab building multimodal 'omni' foundation models and infrastructure aimed at robotics and physical-world applications.
252K visits/mo
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Claude
✓ verifiedFreemium
Anthropic's AI assistant for writing, coding, and analysis across web, mobile, and desktop, plus a developer API.
22M visits/mo231K saves
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SiliconFlow
✓ verified
Developer platform serving 200+ optimized LLMs via APIs; high traffic.
434K visits/mo1.1K saves
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WaveSpeedAI
✓ verifiedFree trial
Pay-per-use API hub aggregating 1000+ image, video, and audio generation models for developers building AI media pipelines.
2.2M visits/mo
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Replicate AI
✓ verifiedPaid
Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.
1.3M visits/mo17K saves
Pricing
No public pricing
Free: $0
Pro: $17/month billed annually ($200 up front), or $20/month
Max: From $100/month
Team: $20/seat/month billed annually ($25 monthly); premium seats $100/seat/month annually ($125 monthly)
Enterprise: Contact sales
No public pricing
Silver: $100 top-up (higher rate limits)
Gold: $1,000 top-up (higher rate limits)
Ultra: $10,000 top-up (highest rate limits)
Free trial available
CPU (Small): $0.000025/sec ($0.09/hr)
Nvidia A100 80GB: $0.0014/sec ($5.04/hr)
Nvidia H100: $0.001525/sec ($5.49/hr)
Free trial available
Core features
- ✦Omni multimodal model research and development
- ✦Real-time inference API (Infer) for enterprise use
- ✦Video tagging, search, and clipping infrastructure
- ✦Training data generation from egocentric and robotics footage
- ✦Conversational writing and editing
- ✦Code generation and debugging (Claude Code)
- ✦Data analysis and visualization
- ✦Web search plus memory across chats
- ✦Connectors and remote MCP integrations
- ✦Extended thinking for complex tasks
- ✦Access over 200 optimized models, including LLMs, image, video, and audio processing.
- ✦Achieve low-latency, high-throughput inference with SiliconFlow's self-developed acceleration frameworks.
- ✦Deploy models via serverless inference, dedicated endpoints, or reserved GPUs to suit various workloads.
- ✦Customize models to your data with built-in monitoring and elastic compute resources.
- ✦Ensure data privacy and business security with dynamic scaling and fault tolerance mechanisms.
- ✦Unified API access to 1000+ image/video/audio generation models
- ✦Pay-per-use pricing billed per image or per second of video
- ✦Includes chat/LLM model access (Claude, GPT, Gemini, etc.) priced per token
- ✦Account tiers unlock higher GPU limits and concurrency
- ✦CLI and desktop app for building workflows
- ✦Enterprise options with dedicated support and custom deployment
- ✦One-line API calls to run community and proprietary AI models
- ✦Support for image, video, speech, and LLM generation models
- ✦Fine-tuning and custom model deployment via Cog
- ✦Per-second usage billing on shared or dedicated hardware
- ✦Automatic scaling for high-traffic private models
- ✦Thousands of community-published models with production APIs
Use cases
- →Powering robotics perception with multimodal AI
- →Running large-scale video search and analysis via API
- →Sourcing specialized training data for frontier AI models
- →Drafting and refining written content
- →Building and debugging software
- →Analyzing datasets for insights
- →Research and learning support
- →Team and enterprise automation
- →Quickly deploy various AI models via a simple API, supporting tasks like text, image, audio, and video processing.
- →Utilize serverless GPUs to automatically scale AI applications, ensuring flexibility and cost-efficiency.
- →Access high-performance GPUs for demanding workloads, such as large-scale inference and video generation.
- →Deploy custom models with guaranteed performance and scalability, tailored to specific business needs.
- →Integrating AI image/video generation into an app via API
- →Building automated content pipelines needing multiple AI models
- →Testing and comparing many generative models from one account
- →Scaling AI media production with volume-based account tiers
- →Accessing both media-generation and LLM APIs from one platform
- →Developers embedding image/video/speech generation into an app via API
- →Teams deploying and scaling their own fine-tuned models
- →Builders comparing outputs from multiple AI models in one playground
- →Companies avoiding GPU infrastructure management for ML inference
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