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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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ZenMux
✓ verifiedPaid
Enterprise unified API gateway giving one integration point to 100+ LLMs like Claude, GPT, and Gemini with reliability guarantees.
435K visits/mo11K saves
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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
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Deep Infra
✓ verifiedPaid
Low-cost inference cloud with developer APIs to run open ML models and on-demand GPUs, billed pay-per-use.
375K visits/mo
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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
Pricing
No public pricing
Starter: $0/mo + compute ($30 free credit)
Team: $250/mo + compute
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
Core features
- ✦Unified API for 100+ AI models
- ✦Intelligent request routing across models
- ✦AI Model Insurance for quality/reliability guarantees
- ✦Enterprise-focused LLM access layer
- ✦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
- ✦Hosted inference for many open models
- ✦Simple REST/OpenAI-compatible API
- ✦Pay-per-token or per-time billing
- ✦On-demand GPU rental
- ✦Broad catalog (Llama, DeepSeek, Qwen, Flux, etc.)
- ✦DeepStart and DeepCluster tooling
- ✦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
Use cases
- →Building applications that need failover across multiple LLM providers
- →Consolidating billing/access to many AI models under one API
- →Enterprises requiring guaranteed model output reliability
- →Deploying and scaling model inference
- →Fine-tuning and training models
- →Running batch/parallel AI jobs
- →Executing untrusted code in sandboxes
- →Serving open-source models via API
- →Building AI apps cost-efficiently
- →Renting GPUs for inference or training
- →Scaling inference up and down on demand
- →Serving open models in production apps and agents
- →Fine-tuning models on private data
- →Powering code assistants, chatbots and RAG at scale
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