Alternatives
Products that do what Make your Redis 17,000X Faster does
1.21 ns. 190,909x faster than Redis alone. 827M ops/sec.
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- 3AC
Multi-tier exact-match cache for AI agents backed by Valkey or Redis. LLM responses, tool results, and session state behind one connection. Framework adapters for LangChain, LangGraph, and Vercel AI SDK. OpenTelemetry and Prometheus built in. No modules required - works on vanilla Valkey 7+ and Redis 6.2+. Shipped v0.1.0 yesterday, v0.2.0 today with cluster mode. Streaming support coming next. Existing options locked you into one tier (LangChain = LLM only, LangGraph = state only) or one framework. This solves both. npm:…
Apr 2026
- 4AB
we've been working on a KV store for the past year or so which is 2-6x faster than Redis (benchmark link below) yet disk persisted! so you get the speed of in-memory KV stores but with disk persistence. To achieve this we've created our custom filesystem that is optimized for our special usecase and we're doing smart batching for writes and predictive fetching for reads. In addition to basic operations, it also provides atomic inc/dec, atomic json patch, range scans and a unique key monitoring mechanism (pub-sub) over WebSockets which essentially allows you to receive notification on…
2025 · hpkv.io
- 5RC
Hello HN! We're building a caching solution for LLMs (ChatGPT, Claude). By combining cutting-edge approaches, such as edge computing, prompt compression, vectorization, and others - it can reduce your AI bills by up to 10x and significantly lower response times. Key Features: - cost efficiency: our system stores frequent queries, reducing the number of upstream (paid) API calls - fast responses: with various nodes globally, we reduce latency by serving data from the nearest location - scalability: designed to handle increasing loads and data sizes without degrading performance. The cache…
2024 · edgematic.dev
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Sep 2025 · github.com
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While building my AI-powered dating app, I couldn't find a memory backend that was affordable,accessible and efficient. I built Redcache-ai to meet this need. Redcache-ai is also available as a Python package. Happy to receive feedback and answer questions. Note: I am not a native English speaker. Apologies for the typos and grammatical errors.
2024 · github.com
- 10DR
Hi HN! I built a distributed rate limiter using the token bucket algorithm with Redis backing. Key highlights: • 50,000+ requests/second throughput with <2ms P95 latency • Redis-backed distributed state for multi-instance deployments • 18 REST API endpoints for rate limiting, config, and monitoring • 265+ tests including load tests and integration tests • Docker/Kubernetes ready with comprehensive documentation Built with Java 21 + Spring Boot. Perfect for protecting APIs, microservices, or SaaS platforms from abuse. The pain point I solved: existing solutions were either too…
Sep 2025 · github.com
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Generative AI applications pose a unique challenge in production. They are computationally intensive and orders of magnitude slower than traditional data-intensive applications. Scaling these applications is further complicated by expensive hardware requirements and GPU shortages. Consequently, developers are scrambling to implement home-grown caching and rate-limiting solutions, which are error-prone and difficult to get right. FluxNinja Aperture delivers a production-grade experience with a purpose-built load management platform that provides rate & concurrency limiting, caching, and…
2024 · fluxninja.com
- 12RC
2016 · github.com
- 13HF
Hey HN, we’re Eliza and Xin. We’re working on Hurry, an open source drop-in tool that adds distributed build caching to Cargo with (almost) zero configuration. Wherever you run cargo build, you can run hurry cargo build instead, and expect around 2-5x (our best benchmark is 22x) faster builds. We built this because we were dissatisfied with the current build caching options available for Rust. Buck and Bazel require learning a new tool. GitHub Actions and swatinem are too coarse-grained (either the whole cache hits, or none of it does) and finicky to debug, and cargo-chef and Docker layer…
Dec 2025 · hurry.build
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2020 · iredis.io
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I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions. Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
Jun 2026 · apeg.dev
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2021 · github.com
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The weekend of last week I built chat.betterdb.com as a RAG over Valkey/Redis/Dragonfly docs. The goal was to eat our own dogfood and test publicly our caching libraries. It also saved me from having to come up with various demo/test scenarios, as I could extend the building in public to the demo. There is a tool-result cache sitting between the SDK and tools. Each call is normalized and then checked before executing. If it hits we return from the cache, and if not, we check the semantic cache, which embeds the prompt and checks with KNN via valkey-search. If the cosine…
May 2026
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Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across…
Jun 2026
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2023 · cachegrand.io
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2019 · github.com
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Cut OpenAI API costs by 20-40% with smart caching
Mar 2026 · ai-optimizer-landing.vercel.app
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2023 · github.com
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Hey HN, we’re the developers of OpenLake, an open source storage engine for offloading LLM KV caches from GPU memory into a shared tier of RAM and NVMe. We built OpenLake because KV caches are outgrowing GPU memory. A single 256K token conversation on Gemma 4 31B produces approximately 43GB of KV state, more than half the memory of an 80GB H100. The problem becomes even harder across a cluster: a prefix cached on one GPU host is unavailable when the next request lands on a different GPU, forcing the new GPU to repeat work the fleet has already completed. Once the KV cache is offloaded,…
Jul 2026 · github.com
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