How to Pick Your AI Stack

TL;DR: Your AI tech stack should match where your product is today, not where you hope it will be in three years. Start with the model, add the tools your use case actually needs, and keep it cheap enough to change. Most founders over-engineer it before they have a single paying customer.

Choosing your AI tech stack is not an architecture exercise. It is a business decision. Pick tools that let you ship something real in weeks, not months.

Before you start drawing diagrams, answer one question: what does this AI feature actually need to do? Everything else follows from that.

What even goes into an AI tech stack?

A basic AI stack has a few parts:

• Model - the brain (OpenAI, Anthropic, Google, open-source)
• Orchestration - the code that calls the model and handles logic
• Storage - where your data lives (databases, vector stores)
• Infrastructure - where it runs (cloud, serverless, on-prem)
• Tooling - frameworks, SDKs, eval tools

You do not need all of these on day one. Most early-stage products need a model, a bit of orchestration, and somewhere to store data. That is it.

If you want the bigger picture on how these pieces fit together in a real product, the founders guide to building an AI application covers the full build arc.

How do you pick the right model?

Start with the task, not the benchmark.

GPT-4o is good at reasoning and general tasks. Claude Sonnet is strong for long documents and nuanced writing. Gemini Flash is fast and cheap for high-volume, lower-stakes calls. Llama 3 and Mistral work well when you need to run a model on your own infrastructure.

Test two or three models on your actual data before you commit. A model that scores well on public benchmarks might perform badly on your specific prompts.

Cost matters more than you think. At scale, a cheaper model with good prompting often beats an expensive model with lazy prompting. Run the numbers early.

When do you need a vector database?

You need a vector database when you want the AI to answer questions using your own content, not just general knowledge. This is called retrieval-augmented generation (RAG).

Good use cases:

• Customer support bots that need to know your docs
• Internal search across company files
• Product recommendations based on semantic similarity

Popular options include Pinecone, Weaviate, and pgvector (which sits inside Postgres, so no extra service to manage).

If you are not doing RAG, you probably do not need a vector database yet. Add it when the problem demands it.

Do you need an AI framework?

Frameworks like LangChain and LlamaIndex are useful once your orchestration logic gets complicated. Chaining multiple model calls, routing between agents, managing memory across a session - that is where they help.

For a single model call with a good prompt, a framework adds weight without adding value. Write the call yourself.

At Devwiz we have built across a range of frameworks. The pattern we see consistently: teams reach for a framework before they understand the problem. Then they spend weeks working around the framework's assumptions instead of solving the actual use case.

Start lean. Add structure when the codebase earns it.

Cloud, serverless, or on-prem - what should you choose?

For most early products, serverless is the right call. AWS Lambda, Google Cloud Run, or Vercel Edge Functions let you ship without managing servers. You pay per call, which keeps costs low until you have volume.

Move to dedicated compute when you have predictable, high-volume traffic. The per-call cost of serverless gets painful at scale.

On-prem or private cloud is worth considering when:

• You are handling sensitive data that cannot leave your control (government, health, legal)
• You need a specific compliance posture
• You are running a fine-tuned open-source model and cloud inference is too expensive

NSW Government and enterprise clients like Briometrix have different requirements to a startup building a consumer product. The stack should reflect that.

How do you know if the stack is too complex?

If a new developer cannot get the local environment running in under an hour, it is too complex.

Simplicity tests:

• Can you explain every component in the stack and why it is there?
• Does removing any component break something meaningful?
• Is each service earning its operational overhead?

Over-engineering is the most common mistake we see from founders who have read a lot about AI but have not yet shipped an AI product. They build for the version three architecture before version one exists.

You can always add complexity. It is much harder to remove it once a team has built habits around it.

What about evaluation and monitoring?

You need a way to know if the model is giving good answers. This is often the last thing founders think about and the first thing that bites them in production.

At a minimum:

• Log every prompt and response in development
• Build a test set of real examples with expected outputs
• Check the outputs manually before you ship

Tools like LangSmith, Braintrust, and Weights and Biases help at scale. In the early days, a spreadsheet of good and bad examples is enough.

Monitoring in production should track latency, cost per call, and error rate. Set alerts. Surprises in an AI system are usually expensive.

Making the final call

Pick a stack that your team can reason about, that fits the budget you have today, and that does not lock you in before you know what the product actually needs.

If you want help thinking through the right AI stack for your product, our AI programs are built for exactly this. We work with founders and teams from scoping through to build.

For more on what goes into an AI product at the technical level, James Killick covers the strategy and build process in depth.

Devwiz has been building apps and platforms since 2015 - over 200 of them. The clients we work with, from Huskee to Vivid to NSW Government, all had different stack requirements. There is no universal right answer. But there is a right answer for your product, your team, and your timeline. Start there.

When you are ready to talk specifics, reach out through our tech for founders page and we can look at your use case together.

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FAQ

What is an AI tech stack?

An AI tech stack is the set of tools, models, and infrastructure you use to build and run an AI-powered product. It typically includes a language model, orchestration code, a database, and cloud infrastructure. The right stack depends on what your product needs to do and how much complexity your team can manage.

Do I need to use OpenAI to build an AI product?

No. OpenAI models are popular and well-documented, but they are one option among many. Anthropic Claude, Google Gemini, and open-source models like Llama 3 are all production-ready. The best model for your product depends on the task, your latency needs, and your cost budget. Test a few before you commit.

How much does an AI tech stack cost to run?

It varies widely. A simple product with occasional model calls might cost $50 to $500 per month. High-volume applications can run into thousands per month on model inference alone. The main cost drivers are tokens per call, call volume, and infrastructure. Start with serverless and measure the actual costs before over-investing in infrastructure.

When should I use an agent framework like LangChain?

Use a framework when your logic requires chaining multiple model calls, routing between different tools, or managing conversation memory across a session. For a single-call feature with a straightforward prompt, skip the framework and write the call directly. Frameworks add value at complexity, not at simplicity. Many teams adopt them too early and spend more time fighting the framework than building the product.

Can I change my AI tech stack later?

Yes, and you should expect to. Most mature AI products swap out components as they learn more about what the product actually needs. Switching models is usually straightforward. Switching orchestration frameworks or databases is more costly. Keep your interfaces clean and avoid tying business logic directly to third-party SDK methods, so you have room to change what is underneath without rewriting everything.

Frequently asked questions

What is an AI tech stack?

An AI tech stack is the set of tools, models, and infrastructure you use to build and run an AI-powered product. It typically includes a language model, orchestration code, a database, and cloud infrastructure. The right stack depends on what your product needs to do and how much complexity your team can manage.

Do I need to use OpenAI to build an AI product?

No. OpenAI models are popular and well-documented, but they are one option among many. Anthropic Claude, Google Gemini, and open-source models like Llama 3 are all production-ready. The best model for your product depends on the task, your latency needs, and your cost budget. Test a few before you commit.

How much does an AI tech stack cost to run?

It varies widely. A simple product with occasional model calls might cost $50 to $500 per month. High-volume applications can run into thousands per month on model inference alone. The main cost drivers are tokens per call, call volume, and infrastructure. Start with serverless and measure the actual costs before over-investing in infrastructure.

When should I use an agent framework like LangChain?

Use a framework when your logic requires chaining multiple model calls, routing between different tools, or managing conversation memory across a session. For a single-call feature with a straightforward prompt, skip the framework and write the call directly. Frameworks add value at complexity, not at simplicity. Many teams adopt them too early and spend more time fighting the framework than building the product.

Can I change my AI tech stack later?

Yes, and you should expect to. Most mature AI products swap out components as they learn more about what the product actually needs. Switching models is usually straightforward. Switching orchestration frameworks or databases is more costly. Keep your interfaces clean and avoid tying business logic directly to third-party SDK methods, so you have room to change what is underneath without rewriting everything.