Klaus – OpenClaw on a VM, batteries included
We are Bailey and Robbie and we are working on Klaus (https://klausai.com/): hosted OpenClaw that is secure and powerful out of the box.Running OpenClaw requires setting up a cloud VM or local container (a pain) or giving OpenClaw root access to your machine (insecure). Many basic integrations (eg Slack, Google Workspace) require you to create your own OAuth app.We make running OpenClaw simple by giving each user their own EC2 instance, preconfigured with keys for OpenRouter, AgentMail, and Orthogonal. And we have OAuth apps to make it easy to integrate with Slack and Google Workspace.We are both HN readers (Bailey has been on here for ~10 years) and we know OpenClaw has serious security concerns. We do a lot to make our users’ instances more secure: we run on a private subnet, automatically update the OpenClaw version our users run, and because you’re on our VM by default the only keys you leak if you get hacked belong to us. Connecting your email is still a risk. The best defense I know of is Opus 4.6 for resilience to prompt injection. If you have a better solution, we’d love to hear it!We learned a lot about infrastructure management in the past month. Kimi K2.5 and Mimimax M2.5 are extremely good at hallucinating new ways to break openclaw.json and otherwise wreaking havoc on an EC2 instance. The week after our launch we spent 20+ hours fixing broken machines by hand.We wrote a ton of best practices on using OpenClaw on AWS Linux into our users’ AGENTS.md, got really good at un-bricking EC2 machines over SSM, added a command-and-control server to every instance to facilitate hotfixes and migrations, and set up a Klaus instance to answer FAQs on discord.In addition to all of this, we built ClawBert, our AI SRE for hotfixing OpenClaw instances automatically: https://www.youtube.com/watch?v=v65F6VBXqKY. Clawbert is a Claude Code instance that runs whenever a health check fails or the user triggers it in the UI. It can read that user’s
FL score
out of 100
Verdict
high confidence
Competition
5
competitors found, emerging market, funded players
Trend
No signal yet
Klaus provides a secure, managed, and 'batteries included' hosting solution for OpenClaw, solving critical setup, security, and maintenance challenges for AI agent developers.
The pain
The gap
Build angle
Strengths
- Extremely specific and well-defined pain point for OpenClaw users.
- Clear differentiation from generic AI inference platforms through niche focus, security, and pre-integrations.
- Founders demonstrate deep technical expertise and have already built significant infrastructure (ClawBert).
- Strong demand signals (founders spent 20+ hours fixing issues for users).
- High potential for recurring revenue with a subscription model.
- Managed service directly addresses user frustrations about complexity and security.
Risks
- Reliance on the longevity and continued adoption of OpenClaw as a framework.
- Technical complexity of maintaining secure, isolated VMs and an AI-driven SRE system.
- Pricing sensitivity in a market accustomed to pay-as-you-go compute.
- Security risks inherent in hosting AI agents that access user data, even with strong mitigations.
- Scalability challenges as user base grows, especially with bespoke VM instances.
- Competition from broader cloud providers or other managed AI services if they decide to niche down.
Next steps
- Continue to gather direct user feedback on pricing expectations and desired features.
- Explore partnerships within the OpenClaw community or with related tool providers for distribution.
- Refine onboarding process to highlight security features and ease of use.
- Develop a clear roadmap for supporting other prominent AI agent frameworks to mitigate framework-specific risk.
- Invest in automated monitoring and incident response to reduce manual SRE efforts further.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 26
- Solution gap
- 19
- Willingness to pay
- 18
- Buildability
- 15
A highly specific, managed, and secure hosting solution for OpenClaw addressing significant setup and operational pain, with existing proof of concept and strong buildability.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong potential due to clear market pain, a compelling value proposition, a growing market, and defensible niche differentiation.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A strong idea for a solo builder with clear problem, good creator fit, and a valuable, targeted solution, but with a niche audience.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong micro-SaaS potential with a specific audience, clear value, and existing validation, mitigating many typical startup risks.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
High potential with clear demand, a superior solution to the status quo, and a strong product in a growing market.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Continue to gather direct user feedback on pricing expectations and desired features.
- Explore partnerships within the OpenClaw community or with related tool providers for distribution.
- Refine onboarding process to highlight security features and ease of use.
- Develop a clear roadmap for supporting other prominent AI agent frameworks to mitigate framework-specific risk.
- Invest in automated monitoring and incident response to reduce manual SRE efforts further.
Who is already there, emerging market
Replicate provides a cloud platform to run and fine-tune open-source machine learning models via API calls, abstracting away infrastructure management.
Pricing: Replicate uses a pay-as-you-go model. Public models are billed by hardware and time or input/output, with setup and idle time being free. Private models on dedicated hardware are billed for all online time (setup, idle, active). Hardware pricing examples: CPU (Small) $0.000025/sec, Nvidia A100 (80GB) $0.001400/sec, Nvidia H100 $0.001525/sec.
RunPod offers a globally distributed GPU cloud platform for developers to deploy custom full-stack AI applications for training, deploying, and scaling AI models.
Pricing: RunPod uses a pay-as-you-go, per-second billing model for compute, with different rates for Community Cloud (third-party hosts, cheaper) and Secure Cloud (RunPod managed, better SLAs). On-demand GPU instances range from $0.17/hour to $3.99/hour. Examples: RTX 4090 at $0.39/hour, A100 80GB at $1.89/hour, H100 80GB at $2.99/hour on the community cloud. Storage costs are separate: Container Disk is $0.10/GB/month while running and $0.20/GB/month when stopped; Network Volumes are $0.07/GB/month for the first TB, then $0.05/GB/month.
Baseten is an AI inference company that provides a platform for deploying and running machine learning models in production applications with a focus on high performance and reliability.
Pricing: Baseten offers pay-as-you-go pricing, only charging for the compute used (no idle time). They have Basic (dedicated deployments, model APIs, training, fast cold starts), Pro (priority GPU access, dedicated compute, higher API limits, dedicated support), and Enterprise (custom SLAs, self-hosting, on-demand flex compute, use existing cloud commitments) plans. Pricing for GPUs per minute: T4 at $0.01052, L4 at $0.01414, A100 at $0.06667, H100 at $0.10833, B200 at $0.16633. The AWS Marketplace offering starts around $5,000/month for enterprise.
Cerebrium is a serverless AI infrastructure platform that enables teams to build, deploy, and scale multimodal AI applications without managing traditional infrastructure.
Pricing: Cerebrium uses a pay-per-second billing model, where users only pay for the compute they use (CPU, GPU, memory), with no cost for idle GPUs. CPU pricing starts from $0.0002/sec, and GPU from $0.0005/sec. Example GPU costs: Nvidia A10 is $0.000306/second. They offer Hobby (free + compute, 3 users, 3 deployed apps, 5 concurrent GPUs), Standard ($100 + compute/month, 10 users, 10 deployed apps, 30 concurrent GPUs), and Enterprise (custom pricing, unlimited apps/GPUs, dedicated support) plans.
Modal Labs provides a serverless AI infrastructure platform designed for building and scaling AI models, applications, and services, offering programmable building blocks for infrastructure.
Pricing: Modal offers a free tier with $30/month in credits and pay-as-you-go pricing from $0.05/GB-hour (CPU). They abstract away server management, providing instant autoscaling and access to GPUs from Oracle Cloud Infrastructure.
What they charge
Recent news
Baseten Raises $300M at a $5B Valuation to Power a Multi-Model Future
Business Wire, January 23 2026
Modal Labs AI Inference Startup Nears $2.5B Funding Round | 2026
IndexBox, February 11 2026
Cloud AI Inference Chips Market Outlook 2026-2034
MarketWatch, March 01 2026
AI Inference Market Size, Share | Global Growth Report [2034]
Fortune Business Insights, March 02 2026
AI Data Center Market worth $2,023.52 billion by 2032 - Exclusive Report by MarketsandMarkets™
MarketsandMarkets, March 18 2026
Market signals
The AI inference market is a rapidly growing market, projected to reach between $254.98 billion and $312.64 billion by 2030-2034, growing at a CAGR of 12.98% to 19.3%. This growth is fueled by the increasing adoption of generative AI and large language models, driving demand for real-time AI deployment and the expansion of specialized cloud infrastructure. Recent significant funding rounds for companies in this space underscore strong investor confidence and market expansion.
What frustrates people
- Difficulty predicting costs accurately with traditional cloud providers due to fragmented billing for numerous services (GPU instance, data egress, container registry, API gateway, logging).
- High data egress fees on traditional cloud platforms, which can significantly increase costs for data-heavy AI workloads.
- Managing GPU capacity and variable demand on legacy cloud infrastructure can be challenging and expensive.
- Cold starts and scaling limits can be an issue with shared hardware pools when using public models.
- The need to integrate and manage various ancillary services (databases, API servers, CI/CD, monitoring) when using platforms primarily focused on GPU compute.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Klaus – OpenClaw on a VM, batteries included”.