AI · alternatives · 2026
24 alternatives to I built a tool showing how AI providers (should) throttle their models
OP here: this project was born out of the frustration/paranoia that AI providers are throttling their models when their server load is too high. So, I set out to model and study the problem mathematically to understand what was happening, what I found was quite surprising. The idea seems natural: as the data center demand increases momentarily through the day, throttling their models (either using quantized versions, reducing the context window or lowering the tier of the model to a smaller one) seems appealing as the replacement model in principle uses less electricity. The problem is…
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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Opper AI▲229Hey HN! After the Car Wash Test post got quite a big discussion going (400+ comments, https://news.ycombinator.com/item?id=47128138), I spent the past few weeks building a tool so anyone can run these kinds of questions and get structured results. No signup and free to use. You type a question, define answer options, pick up to 50 models at a time from a pool of 200+, and they all answer independently under identical conditions. No system prompt, structured output, same setup for every model. You can also run a debate round where models see each other's reasoning and get a…
Jul 2026 · opper.ai · its alternatives →
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Leaping AI▲221AI agents that call and text in multi-day campaigns
Jul 2026 · leapingai.com · its alternatives →
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Anthropic and OpenAI's publicly available models are explicitly guard-railed so that they refuse offensive tasks. And their cyber-focussed models are gated for enterprises. This leaves SMEs and mid market open to major vulnerabilities. AI can be used as both an adversarial and defensive tool in the world of cyber. A worst case outcome is if only the adversaries have access. Meanwhile, most existing AI cyber tools are just wrappers. The problem is that they still have all the guardrails on from the foundation model where they will inherit its refusals. For this project we've post-trained a…
Jun 2026 · argusred.com · its alternatives →
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Credyt▲100Hey HN! I'm the founder of Credyt (https://credyt.ai). If you're running an AI product that burns $0.50+ per interaction, you need to bill customers as usage happens, not at the end of the month. Otherwise you're a bank for your customers, and you'll run out of money before they do. Why invoice-based billing doesn't work for AI: Most usage-based billing tools (Stripe Billing, Orb, etc.) were built for SaaS. They meter usage all month, then send an invoice. That works fine when your COGS is 5%. It's a disaster when you're paying your AI vendor upfront for every request and your…
Nov 2025 · credyt.ai · its alternatives →
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RaptorCI▲99Hello! With more and more AI coding agents popping up and companies expecting higher output have you noticed more issues in production or other bottlenecks? I’m a principal engineer at a leading cybersecurity company and we have a very lean engineering team and the expectations have shifted to us using AI to increase productivity instead of hiring. The result has been production becoming the most unstable it has ever been and bottlenecks around testing and releasing as we don’t have the resources to cope. I spent a few weeks of thinking of ways that AI could help our full SDLC and one thing…
Apr 2026 · getraptorci.com · its alternatives →
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Unprompt▲75Your AI usage in dollars and liters of water
23d ago · apps.apple.com · its alternatives →
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The Emotion Engine has 32 MB of RAM total, so the trick is streaming weights from CD-ROM one matrix at a time during the forward pass — only activations, KV cache and embeddings live in RAM. This means models bigger than the RAM can still run, they just read more from disc. Had to build a custom quantized format (PSNT), hack endianness, write a tokenizer pipeline, and most of the PS2 SDK from scratch (releasing that separately). The model itself is also custom — a 10M param Llama-style architecture I trained specifically for this. And it works. On real hardware.
Mar 2026 · github.com · its alternatives →
- 10RL
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 · its alternatives →
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I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com · its alternatives →
- 12TC
Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think!
Jun 2026 · atomprophet.io · its alternatives →
- 13AU
I run 3mins.news (https://3mins.news), an AI news aggregator built entirely on Cloudflare Workers. The backend has 10+ cron triggers running every few minutes: RSS fetching, article clustering, LLM calls, email delivery. The problem: Workers Paid Plan has hard monthly limits (10M requests, 1M KV writes, 1M queue ops, etc.). There's no built-in "pause when you hit the limit", CF just starts billing overages. KV writes cost $5/M over the cap, so a retry loop bug can get expensive fast. AWS has Budget Alerts, but those are passive notifications, by the time you read the email,…
Mar 2026 · its alternatives →
- 14IB
I built a web page that aggregates data about data center buildup, sovereign fund investments into AI and bottlenecks. The objective is to predict AI race cooldown by looking at a potential decrease of activity involving these elements. The website looks at the quarterly forms from the 5 biggest hyperscalers and adds their CapEx into the mix, calculating a composite index in the end showing how likely it is for the AI race to slow down. Enjoy!
Jul 2026 · laurentiugabriel.github.io · its alternatives →
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Argmin AI▲32Hey, HN community! We've built Argmin AI after shipping LLM features where the demo worked, then the bill and latency got unpredictable in production. Prompts expanded, context grew, retrieval got noisy, retries appeared, and agent workflows added loops. Argmin AI optimizes LLM-related expenses as a system: 1. prompt and context efficiency 2. model selection and routing 3. RAG inefficiencies and caching opportunities 4. agent workflows (tool calls, retries, loop control) Changes are validated with evals and guardrails (tests, gates, judges), tailored to your quality definition and goals.…
Jul 2026 · argminai.com · its alternatives →
- 16AB
Hi HN, Can current AI systems actually run a business? There’s a growing belief that LLM agents can already manage entire teams, replace the entire software stack or even act as an AI CEO. So we built a controlled, measurable environment to evaluate this premise. Why did we build this benchmark? A modern enterprise operates in a dynamic environment with high uncertainty and incomplete information. The CEO has to deal with delayed consequences, staffing/resource tradeoffs and death by a thousand cuts of failure modes. If we ever want AI systems that can meaningfully make operational or…
Nov 2025 · its alternatives →
- 17C5
Hi HN community! My name is Kane and I'm on the product team at www.usage.ai , a cloud cost optimization company. After honing our product on AWS, I'm excited to announce our availability for customers on GCP and Azure! At a high level, Usage insures committed use-discounts (CUDs) to reduce commitment risk and enable higher savings for companies on the cloud. With traditional CUDs, you commit to a certain level of usage over a specified period in exchange for discounted rates from the cloud provider. However, if your actual usage falls short of the committed amount, you may not fully realize…
2024 · its alternatives →
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Know exactly which AI agent is burning your budget.
Apr 2026 · agensights.com · its alternatives →
- 19WB
Hey HN: Kaveh here, founder of https://www.usage.ai/ We help companies drive down AWS, GCP, and Azure spend. Why? Because the way it's done now is a pain. DevOps and Software Engineers end up spending time managing costs rather than focusing on business problems. I have been building Usage AI for almost 4 years now (4 year anniversary in 1 month from now!) with an incredible group of founding people. We started as a product just to help lower AWS EC2 costs, and now we do all major AWS services (such as RDS, OpenSearch, ElastiCache, and Redshift with more on the way) and other…
2024 · its alternatives →
- 20IN
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 · its alternatives →
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We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com · its alternatives →
- 22IB
Hi everyone, I've been working on a side project over quarantine called Usage.ai and I finally feel comfortable enough to launch it. We're a service that plugs directly into AWS, automatically finds savings, and applies those savings at the press of a confirmation button all without ever needing to go to an AWS console. I'd love to get HN's thoughts on it! Demo: https://www.loom.com/share/2a6f1c8e4c214914a1cdd88c6fdec4ac Link: https://www.usage.ai/
2020 · its alternatives →
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Know what your AI will actually cost to run.
Aug 2026 · howmuchtorunai.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →