I’m trying to map the AI builder stack people actually use, not just tools with loud launches.

Here’s my rough list:

Local dev

• Ollama
• llama.cpp

Inference / serving

• vLLM
• SGLang
• TensorRT-LLM

Routing / model gateway

• LiteLLM
• OpenRouter
• Portkey
• Cloudflare AI Gateway

Agents

• LangGraph
• LangChain
• CrewAI
• Pydantic AI

RAG

• LlamaIndex
• LangChain
• pgvector
• Qdrant
• Weaviate
• Pinecone

Evals / red teaming

• Promptfoo
• Ragas
• DeepEval
• Braintrust

Observability

• Langfuse
• LangSmith
• Arize Phoenix

GPU hosting

• RunPod
• Modal
• Vast
• Lambda
• Replicate

What would you add, or remove?

I've been developing an AI product using LLM APIs (from OpenRouter) but want to deploy an open-source LLM in my own Prod env. which I can control.

Few reasons behind this are:

- I wanna own the complete stack around my product.

- Second I wanna fine-tune the model around my usecase.

So, what's the most affordable but a good platform for this? I'm not an AI engineer so don't wanna stuck in CUDA or Transformers hell, anything which can give me a straight path towards my private deployment.

Thanks,

I've been developing an AI SaaS product using LLM APIs (from OpenRouter) but now want to deploy an open-source LLM in my own Prod env. which I can control.

Few reasons behind this are:

- I wanna own the complete stack around my product.

- Second I wanna fine-tune the model around my usecase.

So, what's the most affordable but a good platform for this? I'm not an AI engineer so don't wanna stuck in CUDA or Transformers hell, anything which can give me a straight path towards my private deployment.

Thanks,

I've been developing an AI product using LLM APIs (from OpenRouter) but want to deploy an open-source LLM in my own Prod env. which I can control.

Few reasons behind this are:

- I wanna own the complete stack around my product.

- Second I wanna fine-tune the model around my usecase.

So, what's the most affordable but a good platform for this? I'm not an AI engineer so don't wanna stuck in CUDA or Transformers hell, anything which can give me a straight path towards my private deployment.

Thanks,

JetBrains released Mellum2, a 12B-parameter Mixture-of-Experts (MoE) model with 2.5B active parameters per token, targeting coding assistants, tool use, routing, RAG pipelines, and private enterprise deployments. The model is available under the Apache 2.0 license.

Technical highlights

• 64 experts with 8 active experts per token
• Native Multi-Token Prediction (MTP) for speculative decoding
• 128K context window via layer-selective YaRN
• Grouped-Query Attention with sliding-window attention
• FP8 hybrid-precision pretraining across approximately 10.6 trillion tokens
• RLVR post-training for Instruct and Thinking variants

 

A lot of us solo builders can get an LLM running locally or on a GPU box. But we often ignore the difficult part of making it stable and secure for our users.

Here’s the checklist I’d use before exposing a self-hosted LLM endpoint publicly:

1. API compatibility

• Does it support OpenAI-compatible routes?
• Can your app swap between hosted APIs and self-hosted models?
• Are streaming responses working?

2. Auth

• Is the endpoint protected by API keys?
• Are keys scoped per app/user/environment?
• Can you revoke keys quickly?

3. Rate limits

• Per-user request limits
• Token limits
• Max concurrent requests
• Timeout rules

4. Cost controls

• Max output tokens
• Context length caps
• Request logging
• Alerts for unusual usage
• Separate staging and production endpoints

5. Latency monitoring

• Time to first token
• Tokens per second
• Queue time
• Failed requests
• GPU utilization

6. Failure handling

• Retry policy
• Fallback model/API
• Clear error messages
• Health checks
• Auto-restart on crash

7. Prompt and input safety

• Input size limits
• Tool-use restrictions
• No shell/database access without approval
• Logging for agent actions
• Guardrails for external user content

8. Deployment sanity

• Reproducible config
• Model version pinned
• Rollback path
• Separate dev/prod configs
• Basic load test before launch

What would you add to this list?

Especially curious from people running vLLM, SGLang, TensorRT-LLM, or Ollama-backed endpoints in production.

AI coding tools help me move way faster, but I’m noticing a weird side effect: I sometimes come back to a feature 2 weeks later and barely remember how it works.

Not because the code is broken. It usually works fine, until it is not. But I approved a lot of AI-generated logic without fully absorbing it. As a solo builder, that feels dangerous because there’s no other engineer who understands the system. If I don’t understand it, nobody does.

How are you avoiding this? Do you review every line carefully, write notes, ask AI to explain the code, or just accept that this is part of building faster now?

Trying to put together a practical, no-hype list of what solo AI builders are actually running in production (or close to it). Not what's trending on Twitter — what's holding up in your real stack.

If you're building with LLMs, drop your setup. Here are the categories to riff on:

• LLM APIs — OpenAI, Anthropic, Gemini, Groq, Together, Fireworks, OpenRouter
• Open-source deployment — vLLM, SGLang, TensorRT-LLM, llama.cpp, Ollama
• Agent frameworks — LangGraph, CrewAI, AutoGen, OpenAI Agents SDK
• RAG — LlamaIndex, LangChain, Haystack
• Vector DBs — Qdrant, Weaviate, Pinecone, Milvus, pgvector
• Observability — LangSmith, Helicone, Langfuse, Arize Phoenix
• Evals — promptfoo, Ragas, DeepEval
• GPU hosting — RunPod, Lambda, CoreWeave, Modal, Vast ai

Two questions I'm most curious about:

1. What's the one tool you'd recommend to another solo builder?
2. What's the one you'd tell them to avoid (and why)?

Bonus points if you mention rough monthly cost and how many users you're serving — context makes the recs way more useful.

👋 Welcome to r/SoloAIBuilder — build alone, never feel alone

This is a home for solo founders, indie hackers, and one-person teams turning ideas into real products with AI. No co-founder, no big team, no permission needed — just you, your laptop, and AI doing the heavy lifting.

Whether you're shipping your first landing page or running a profitable micro-SaaS solo, you belong here.

What this community is for

• Sharing what you're building (in progress or shipped)
• The tools, stacks, and AI workflows that actually work for you
• Wins big and small — first user, first dollar, first "it finally works"
• Honest lessons, failures, and the stuff nobody warns you about
• Feedback, accountability, and the occasional reality check

Good things to post

• "Building in public" updates and demos
• "What's your stack?" and tool recommendations
• Questions when you're stuck (technical, growth, or motivation)
• Teardowns and feedback requests for your project
• Milestones worth celebrating

A few house rules

1. Be helpful and kind — we're all solo, so we lift each other up.
2. Self-promo is welcome when it adds value — show the build, not just the link.
3. No spam, no engagement bait, no "DM me for the secret."
4. Share real experience over hype. Specifics > vibes.

Introduce yourself 👇

Drop a comment with:

• What you're building (or want to build)
• Your favorite AI tool right now
• One thing you're stuck on or proud of this week

Glad you're here. Now go ship something. 🚀

We’re looking for an AI Tools Research and Marketing Intern to join us.

This role is ideal for someone who is curious about AI, enjoys exploring new tools, and can turn research into useful content for online audiences.

What you’ll do:

• Research the latest AI tools and understand their features, use cases, and limitations
• Track fast-moving trends in AI tools, productivity, automation, content creation, and related areas
• Use ChatGPT/Claude to help draft articles, guides, comparisons, and social posts
• Publish and repurpose content across different platforms
• Help build a consistent content pipeline around AI tools and trends

What we’re looking for:

• Strong interest in AI tools and emerging technology
• Good research and writing skills
• Ability to understand what a tool does and explain it simply
• Comfortable using ChatGPT, Claude, OpenCode, and similar AI tools
• Self-driven, curious, and consistent with updates

We’ll provide access to ChatGPT/Claude subscriptions where needed.

This is a great opportunity for someone who wants hands-on experience in AI research, content marketing, and trend tracking.

Interested candidates can DM with a short intro, LinkedIn profile, resume and any relevant writing/research samples.