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Beam Cloud vs Letta

Beam CloudLetta

Bottom line: Beam Cloud for aI/ML engineers; Letta for teams that need persistent agent memory.

Beam CloudBeam Cloud

Serverless GPU runtime for AI inference, training, and sandboxes

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LettaLetta

Stateful AI agents with long-term memory (formerly MemGPT).

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Votes00
PricingFreemiumFreemium
CategoryAi InfrastructureAgent Frameworks
Tags
serverless-gpuinferencemodel-trainingopen-sourceusage-based
agentsmemoryopen-sourcestateful-agentsllm-framework
Best for
  • AI/ML engineers
  • Inference-heavy apps
  • Batch processing teams
  • Teams that need persistent agent memory
  • Builders of long-running assistants
  • Researchers exploring stateful agents
Pros
  • Per-second billing with scale-to-zero
  • Pythonic interface, minimal infra overhead
  • Single-command inference deployment
  • Task queues for high-volume jobs
  • Open-source runtime (beta9)
  • Purpose-built for the agent memory problem
  • Strong research pedigree (MemGPT paper)
  • OS-style tiered memory persists across sessions
  • Open-source core, free to self-host
  • Managed Letta Cloud with free and paid tiers
Cons
  • No always-free plan; usage-based costs accrue
  • GPU costs can add up at scale
  • Python-centric workflow
  • Cold starts possible when scaling from zero
  • Requires ML/infra familiarity
  • Narrower than general agent frameworks
  • Often used alongside other tooling, not a full stack
  • Renamed from MemGPT, some older docs use old name
  • Cloud pricing and tiers still evolving
  • Python-focused

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