Memory layer for AI agents — long-term, structured memory that survives across sessions and conversations.
The agent memory layer most teams should adopt
Mem0 gives AI agents structured long-term memory in a package that integrates cleanly with OpenAI, Anthropic, LangChain, and CrewAI. Open-source for self-hosting, hosted SaaS for everyone else.
Fast, cheap inference for open-source LLMs — Llama, Mixtral, Qwen, DeepSeek served at sub-second latencies.
The fast inference layer for production OSS models
Fireworks AI serves Llama, Mixtral, Qwen, and DeepSeek at low latency through an OpenAI-compatible API. The right pick when you've decided to run open-source models in production and want one less thing to operate.
Production-grade model serving for custom and open-source models — autoscaling GPU inference.
Where ML teams ship models without operating Kubernetes
Baseten gives you autoscaling GPU inference for custom or fine-tuned models without managing the underlying infrastructure. The right pick for ML teams shipping their own models to production.
GPU cloud for AI training and inference — H100, H200, B200 instances at competitive on-demand prices.
GPU cloud for actual training workloads
Lambda Labs sells H100/H200/B200 capacity to AI labs at competitive prices. The right answer for teams doing real model training; not a serverless inference platform.
GPU cloud with serverless inference — pay-per-second GPU access from $0.20/hr for community-tier hardware.
The cheapest GPU access on the market — with the caveats that implies
RunPod's Community Cloud gives you RTX 4090s for $0.34/hr and A100s for $1.19/hr — far cheaper than anyone else. Reliability varies; production teams should use Secure Cloud or look elsewhere.
Not sure which alternative fits?
Describe your situation. The advisor reads your goals, constraints, and existing stack — then names 3 of the above with honest tradeoffs.
Get my 3-tool shortlist →