Your $0 AI Stack Will Cost You Everything

The real architecture for companies that actually have customers to serve. You can build a production AI system for literally nothing. You can also cross the Atlantic on a lilo. Technically possible. Not what we'd recommend. The $0 AI stack is a useful starting point for indie developers and side projects. But if you're running a 50-to-500 person company, or selling AI products into that market, building on a $0 stack is like fitting out a restaurant kitchen with camping gear. Functional until it's very, very not. Here's the honest version. The SMB and midmarket AI stack for 2026. What it costs, what you actually need, and where the free version falls dangerously short.

Ghita El Haitmy

Software Engineer @ techbible.ai

Your $0 AI Stack Will Cost You Everything

The Stack

Frontend Layer - Next.js on Vercel. If you want something more ambitious, Vercel's Open Agents template is worth studying — it's an open-source three-layer system (web UI → durable agent workflow → sandbox VM) that shows exactly how production coding agents are architected. Retool if non-technical teams need to build their own views. Streamlit for data-heavy interfaces.

Agent Orchestrator - LangGraph for stateful, complex workflows if you want full control. But the more important development here is Anthropic's Claude Managed Agents — now in beta and a genuine paradigm shift. Instead of building your own agent loop, tool execution layer, and runtime infrastructure, you get a fully managed environment where Claude can run shell commands, read and write files, browse the web, and execute code inside a secure cloud container. You define the agent once, configure the environment, and launch sessions. It handles the rest including prompt caching, context compaction, and persistent session state. For most SMB use cases, this removes an entire layer of custom engineering that teams used to build themselves.

LLM Layer - Cloud models only, no local inference at this scale. The current working tier: Claude Sonnet 4 and Opus 4 for reasoning-heavy and multi-step tasks, GPT-4.1 for high-volume throughput workloads, Gemini 2.5 Pro where long-context or multimodal capability is the primary requirement. Add Azure OpenAI if the client needs data residency or enterprise compliance. Pay-per-token is not a failure mode, it's a pricing model that scales linearly with the value you deliver.

RAG Pipeline - LlamaIndex for retrieval orchestration. Pinecone serverless scales to zero and is the cleanest choice for most SMBs. Supabase pgvector if you want to consolidate the stack. Chunking strategy and metadata filtering matter far more than vector DB choice at this scale.

Memory Layer - Mem0 or Zep for persistent agent memory across sessions. Non-negotiable for anything customer-facing or multi-session. Skip it and your agents have the recall of someone who woke up with no idea who you are. Your users will notice before you do. Note: Claude Managed Agents has a memory feature currently in research preview worth tracking as it matures.

Tool Use via MCP - GitHub, Slack, Google Workspace, your CRM. At SMB scale the integrations are the product. MCP is now the right long-term bet for interoperability and Claude Managed Agents has native MCP server support built in, which significantly reduces the plumbing work.

Data Layer - Supabase as the default, Postgres, Auth, and Storage in one. Snowflake if the client is already using it for analytics. Bin SQLite entirely. You need multi-user concurrency from day one.

Auth and Security - Supabase Auth or Clerk. Row-level security on all data. This is the thing that gets skipped and kills enterprise pilots. It takes an afternoon to implement and there is no excuse.

Observability and Evals - Langfuse from day one prompt versioning, tracing, and evals in one open-source tool. Helicone as a lighter-weight alternative. You need to know when your agents are hallucinating before your clients do.

Deployment - Vercel for anything Next.js and their Sandbox and Workflow SDK products are now purpose-built for durable agent workloads, not just static sites. Railway or Render for backend services. Docker containers throughout so you're never locked in.

Where the Free Stack Breaks

Local LLMs don't scale. Running a 70B model locally is an engineering achievement. Running it for 200 concurrent enterprise users is a completely different problem. Cloud LLMs with SLAs exist for a reason.

No memory means no product. Every session starting fresh isn't an AI assistant, it's a very expensive search box. Persistent memory is table stakes.

No auth is a single-player game. No auth layer, no row-level security fine for a personal project, career-ending for anything in front of a B2B client.

Observability bolted on later is observability that never ships. You cannot improve what you cannot see. You cannot defend what you cannot trace. It has to be in from day one.

The Question to ask

It's not "can we build this cheaply?" Most competent engineering teams can. It's "do we know what we're running, what it's costing, and whether it's actually working?"

That answer is almost never yes.

That's the gap ELI was built to close. Not the architecture — that's an engineering problem with engineering solutions. The intelligence layer above it: the context graph that maps what your company actually runs, how it's being used, and where the spend, redundancy, and automation opportunities are hiding in plain sight.

Build for where you're going. Not for where you can start for free.

Rough monthly cost at SMB scale: $300–$1,500/month depending on LLM token volume. That's the honest number. The $0 stack is for prototyping. This is for shipping.