Compare tools
Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
⇄ Comparison dimension — pick the market you're actually shopping in
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Runpod
✓ verifiedPaid
Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.
2.3M visits/mo
✕
portkey.ai
✓ verified
AI gateway and observability suite for governing and optimizing LLM apps; strong dev-tool traffic.
266K visits/mo
✕
ApX Machine Learning
✓ verifiedFreemium
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
355K visits/mo
✕
Higress
✓ verifiedFreemium
Open-source AI-native API gateway for routing, protecting and caching LLM/agent traffic, with a paid managed cloud.
29K visits/mo
Pricing
Pods A40 48GB: $0.44/hr
Pods RTX 4090 24GB: $0.69/hr
Pods A100 SXM 80GB: $1.49/hr
Pods H100 SXM 80GB: $2.99/hr
Pods H200 141GB: $4.39/hr
Pods B300 288GB: $7.39/hr
No public pricing
Basic: $0/mo (free forever)
Pro: $19/mo
Pro+: $59/mo
No public pricing
Core features
- ✦On-demand GPU pods across 30+ GPU types and 31 regions
- ✦Serverless GPU endpoints with sub-200ms cold starts
- ✦Zero idle cost billing for inference workloads
- ✦Multi-node clusters for distributed training
- ✦Persistent network storage for full pipelines
- ✦Real-time logs, monitoring and autoscaling from 0 to hundreds of workers
- ✦AI Gateway for reliable LLM routing
- ✦Prompt Engineering for collaborative prompt management
- ✦Guardrails for enforcing reliable LLM behavior
- ✦Observability Suite for monitoring costs, quality, and latency
- ✦MCP Client for building AI agents with real-world tool access
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- ✦Unified proxy and protocol conversion across 100+ LLMs
- ✦Model-level fallback and routing
- ✦Semantic and exact-match AI caching
- ✦Token tracking and quota controls
- ✦Content-safety and data-protection filtering
- ✦MCP service hosting and plugin marketplace
Use cases
- →Renting GPUs for model training and fine-tuning
- →Deploying low-latency real-time inference APIs
- →Running AI agents that need to scale instantly
- →Processing compute-heavy batch or distributed workloads
- →Monitor costs, quality, and latency of AI applications.
- →Route to 250+ LLMs reliably with a single endpoint.
- →Streamline and scale prompt engineering.
- →Enforce reliable LLM behavior with guardrails.
- →Build agents with access to real-world tools.
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Centralizing access to multiple LLM providers
- →Building and governing AI agent/MCP services
- →Controlling token spend across teams
- →Adding caching and safety to LLM calls
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