Running large language models in production is expensive largely because of GPU memory — every ongoing conversation needs its "working memory" (the KV cache) held in fast, costly GPU RAM. OpenLake offers a way to offload that working memory to persistent storage instead, while keeping access fast enough that the model doesn't notice the difference, which can meaningfully cut the GPU memory bill. It's a free, open-source (Apache 2.0) storage engine written in Rust, built on io_uring and RDMA to achieve sub-millisecond latency and over a million IOPS within a 1-millisecond window. It reports up to 50% lower LLM inference costs via KV cache offloading and a 66x speedup on time-to-first-token when reusing a cached 128K-token context. It integrates natively with vLLM and supports petabyte-scale storage for ML/RL checkpoints, vector indexing, and agentic systems.
| # | Tool | Best for | Price |
|---|---|---|---|
| 1 | Développeurs et organisations qui déploient des LLM en production, du débutant à l'expert | — | |
| 2 | Développeurs | — | |
| 3 | Utilisateurs non techniques, équipes commerciales/marketing/RH cherchant à automatiser des workflows répétitifs | — | |
| 4 | Équipes développant des agents IA en production (copilotes, startups SaaS, remplacement de systèmes RAG legacy) | — | |
| 5 | Équipes d'ingénierie et entreprises unifiant l'IA à travers plusieurs outils (Slack, GitHub, Linear, Teams) | — | |
| 6 | Developers and enterprise teams with sensitive data (legal, healthcare, compliance) | — | |
| 7 | Developers building AI agent integrations from existing OpenAPI specs | — | |
| 8 | Enterprise teams needing secure, self-hosted workplace AI search | — | |
| 9 | Small and mid-sized law firms priced out of enterprise legal AI | — | |
| 10 | Développeurs construisant des systèmes multi-agents IA ayant besoin d'un point de coordination public léger | — | |
| 11 | Développeurs voulant intégrer la génération d'images Midjourney dans une app, un SaaS ou un pipeline de contenu | — | |
| 12 | Équipes dev voulant du codage assisté par IA gouverné et auditable | — |
Running a large language model efficiently at scale is a genuinely hard engineering problem — vLLM is the open-source engine that many AI companies quietly rely on to serve LLMs fast and cheaply, without you having to solve GPU memory management yourself.
Lets you build automations by describing them in plain English instead of dragging boxes around a workflow canvas, then connects them to tools like Slack, Notion or Google Sheets.
Gives AI agents a real memory and a private workspace so they stop forgetting everything between conversations and re-reading the same documents over and over.
Lets a company build one AI agent that lives inside Slack, GitHub, Linear and other tools it already uses, instead of juggling separate AI features bolted onto each app.
A privacy-first LLM gateway that routes requests across 600+ models without logging prompts, verified via a Trusted Execution Environment.
Turns OpenAPI specs into safe, typed tools that AI agents can call, without manual wiring.
Open-source enterprise AI platform connecting company knowledge across 50+ apps with permission-aware search.
Free, open-source legal AI platform for document review, drafting and legal research.
A public message board built for AI agents rather than humans — any agent can post a note, request, or result by hitting a URL, with zero setup, no SDK, and no account.
An unofficial REST API that lets developers generate Midjourney images from code instead of Discord, with webhooks, polling, and SDKs — since Midjourney itself doesn't offer a public API.
A policy-bound cloud coding agent that works on isolated, pinned environments and delivers changes as pull requests, with an encrypted audit trail.
