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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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Gpu Cloud Rental Inference Providers Hardware Infrastructure Coding Agents Model Hosting Inference AI Agents AI Agents Infrastructure

Kiro AI

✓ verifiedFreemium

Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.

3.8M visits/mo

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

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

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

Pricing

Free: $0/mo (50 credits)

Pro: $20/user/mo (1,000 credits)

Pro+: $40/user/mo (2,000 credits)

Pro Max: $100/user/mo (5,000 credits)

Power: $200/user/mo (10,000 credits)

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

Team: $250/mo + compute

No public 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

Core features

✦Spec-driven development (requirements, design, tasks)
✦Parallel agents, local or cloud
✦Property-based and correctness testing
✦Works in IDE, CLI, web and mobile
✦Multiple models (Claude, open-weight, Auto)
✦Headless CLI for CI/CD
✦Context from tools like Figma and Terraform

✦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

✦Unified API for 100+ AI models
✦Intelligent request routing across models
✦AI Model Insurance for quality/reliability guarantees
✦Enterprise-focused LLM access layer

✦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

Use cases

→Turning prompts into maintainable, spec-matched code
→Catching bugs unit tests miss
→Reviewing PRs and fixing bugs in CI/CD

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

→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

→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

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