AgentGates vs Beam
Detailed side-by-side comparison to help you choose the right tool
AgentGates
AI Infrastructure
A machine-first marketplace for confidential virtual machines and model inference paid per request with USDC, available through MCP.
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CustomBeam
🔴DeveloperAI Infrastructure
Beam is a developer-first serverless platform purpose-built for AI workloads. The pitch is direct: import a Python function, decorate it, push to Beam, and it runs on a GPU somewhere with the right model weights cached, scales to thousands of concurrent invocations, and shrinks back to zero when traffic stops — with cold starts measured in single-digit seconds rather than the minutes most generic serverless platforms take to load model weights. The team built the platform from the ground up for
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AgentGates - Pros & Cons
Pros
- ✓Starts at $0.08 per hour for an attested confidential VM
- ✓No conventional signup or permanent API key is required
- ✓Pricing and attestation are machine-readable
- ✓Prepaid balances cap delegated-key spending
Cons
- ✗Requires wallet, USDC, network, and payment-retry expertise
- ✗Prices vary with settlement costs and live catalogs
- ✗Balances are non-refundable and stopped machines are deleted after grace
- ✗Confidential hardware does not secure flawed code or model outputs
Beam - Pros & Cons
Pros
- ✓No billing during cold-start / container spin-up — only your code runs are charged
- ✓Storage is free — caching model weights does not add to the bill
- ✓$30 free signup credit makes serious evaluation possible without a card
- ✓Sandboxes give agents a safe place to execute their own generated code
- ✓Python ergonomics — no Dockerfiles or Kubernetes required for the happy path
Cons
- ✗Smaller community and integration ecosystem than Modal
- ✗Region availability is more limited than hyperscaler GPU offerings
- ✗Pro tier per-seat charge ($25) plus usage may add up for larger teams
- ✗Latency-sensitive workloads may still need always-on workers, costing more
- ✗Less mature enterprise governance (RBAC, audit logs) than legacy hyperscalers
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