AI Project Costs in Plain English: What an MVP Should Really Cost

Table of Contents

The 30-Second Answer (Executive Budget Box)

Typical AI MVP Budget Ranges:

A lean AI MVP can often be built for $25k–$150k, whereas a complex MVP with multiple integrations or custom models may run $150k–$300k+.*

Recent industry surveys show wide variations.

For example, many small enterprises reported spending around $300k just on initial GenAI proofs-of-concept in 2023. But that’s an average when tackling broad ambitions: you do not need to spend six figures to get value from an MVP.

By focusing on a narrow use case, leveraging existing AI services, and tight scoping, companies can pilot viable AI solutions on a five-figure budget. Complex projects, which might have multiple user flows, custom-trained models, and strict compliance needs will push into the higher end of mid-six figures.

(*All figures are for planning purposes – actual costs will vary. Always treat these as ballpark estimates, not guarantees.)

What Actually Drives Cost (and How to Control It)

Several key factors determine your AI MVP’s price tag – understanding these will help you control the budget:

Scope & Features

The breadth of what you’re building is the biggest cost driver. Every additional feature, user flow, or integration adds development time. Many AI initiatives falter by biting off too much at once – in fact, Gartner finds the top barrier to AI projects is an inability to define and prove value up front.

To stay on budget, start with one high-impact use case. Limit the MVP to a core workflow where AI can shine, rather than a grab-bag of ideas. A laser-focused scope not only contains build costs but also makes it easier to demonstrate ROI, making it likelier the project succeeds.

Data Readiness

Data-related work often consumes a huge chunk of AI project effort. If your data is messy, unstructured, or requires labeling, budget for significant prep time. Many AI pilots underestimate this – poor data quality or availability is a leading reason why up to 85% of AI projects fail to deliver value (per Gartner research).

To control costs, use existing data where possible and consider simple approaches, like starting with public/pre-trained models, if your proprietary data isn’t AI-ready yet. Investing early in data cleaning and integration will save costly rework later. Tip: for a generative AI MVP, sometimes you can begin with open data or synthetic data to prototype, then refine with real data once the concept is proven.

Model Choice (API vs. Custom)

Deciding between using a third-party AI API or training your own model has huge cost implications. Using a managed API service (e.g. OpenAI, Azure OpenAI, Anthropic) keeps upfront costs low – you pay per usage (pennies per call) with no big infrastructure outlay. It also speeds up development since you skip model training. Gartner notes that using an API with your own front end can be far more cost-effective initially than building or buying a whole custom solution. On the other hand, custom models, fine-tuning or open-source, involve sigificant investment in training time, cloud compute, and expertise.

For example, fine-tuning a GPT-4 level model can cost on the order of $25 per million tokens just for training compute – which adds up quickly with large datasets. Rule of thumb: start API-first for your MVP unless you have a compelling reason to build custom. You can always switch to a custom or open-source model later if usage grows and it’s more cost-efficient to bring it in-house. Using an API in MVP phase also helps validate the use case before you commit resources to custom model development.

Integration Complexity

How many systems need to connect to your AI MVP? Each integration, for example, hooking the AI into your CRM, database, or other software, requires development and testing. If your MVP needs to pull data from multiple enterprise systems or operate inside an existing application, budget extra for this integration work. You’ll spend time on APIs, data pipelines, access control, etc.

Conversely, a self-contained MVP (e.g. a standalone AI demo app) will be much cheaper. It’s wise to integrate minimally in the MVP – focus on the one or two integrations absolutely needed for the core use case. Simplify or even stub others out. This keeps costs down and speed up delivery. Our AI integration services can also help streamline this step with pre-built connectors.

UX & Launch Requirements

The polish and user experience level you target will influence cost. Building a simple demo UI or command-line interface is far cheaper than a production-grade web app with custom design, authentication, and multi-device support. Decide early: is this MVP an internal prototype or an external-facing pilot? If it’s just to prove functionality to stakeholders, you can keep the UI minimal.

But if real end-users or customers will use it, you may need to invest in friendly UX, onboarding, and support – which means more design and front-end development work. To control cost, many teams keep the initial UI very lean or use existing interfaces and only add polished UX design in later phases. You can engage specialized AI design help once the concept is validated, rather than upfront.

Compliance & Security

Requirements here can be hidden cost drivers, especially in regulated industries. If your MVP deals with sensitive data or falls under emerging AI regulations, you’ll need to implement proper security, privacy, and compliance measures. This could include data anonymization, access controls, model output filtering, audit logs, and documentation of AI system risks. Such safeguards are essential but do add effort.

For instance, the forthcoming EU AI Act will require rigorous risk assessments and documentation for “high-risk” AI systems. Compliance overhead could add roughly 17% to project costs on average according to the European Commission’s impact analysis. They estimated ~€400k in compliance cost for an initial high-risk AI system launch – a non-trivial amount. The takeaway: budget for compliance if it applies.

Engage your security/legal teams early to identify must-haves or consult standards like NIST AI Risk Management Framework so you bake these requirements in efficiently rather than retrofitting them.

Day-2 Operations (Monitoring & Logging)

Finally, consider the operational costs after you launch. In production, you’ll want monitoring for model performance, usage tracking, and error alerting. Setting up telemetry, dashboards, and log retention has a cost in tooling or developer time. Additionally, plan for some ongoing tuning and support – users will undoubtedly find new edge cases, and models can drift or produce unexpected outputs that need review. It’s wise to allocate a portion of the budget and team bandwidth for these post-launch tasks.

This ensures your MVP doesn’t just launch and get left unattended. Even at small scale, someone should be watching the outputs and costs. Gartner advises to continuously monitor your AI costs, especially if you are moving at an “AI-accelerated pace.” The good news: for an MVP, this Day-2 overhead is usually modest – often just logging and a part-time engineer managing the cloud usage. But don’t omit it entirely in your plans.

Budget Structure Your CFO Would Appreciate

When communicating the budget to executives, especially CFOs, it helps to break the costs into clear buckets. A simple, CFO-friendly structure for an AI MVP budget is:

One-Time Build Costs

This includes all upfront development expenses to design, build, and test the MVP. It’s largely people’s time and possibly some one-off vendor costs. For example, the effort to develop the model prompt/workflow, code the application, integrate data, and conduct initial testing falls here.

If you’re using external partners or an AI development firm, their project fee would sit in this bucket. Any specialized tooling or API fees during development can be included too, though these are often minor. This is essentially the capital expenditure to create the MVP.

Launch & Run Costs

These are the ongoing costs to deploy and operate the MVP for a given period. Often, teams budget 3–6 months of run costs as part of the MVP plan so leadership knows the total through initial launch. This includes cloud infrastructure and, notably, API usage costs if you’re calling AI services like OpenAI.

Unlike traditional software, usage-based AI fees can be significant if your user base or usage volume grows, so it’s crucial to account for them. We’ll detail run-rate estimates in the next section, but for now, this bucket tells the CFO what it costs per month to run the MVP post-launch. If the MVP will be scaled to more users or extended beyond the pilot phase, you’d project those costs here as well.

Contingency

It’s wise to include a contingency buffer, typically around 10–20% of the total, for unforeseen costs. AI projects often involve some R&D uncertainty – maybe you discover the need for additional data sourcing, or model performance issues require extra effort, or compliance review takes longer. A contingency line item prevents constant re-approvals if minor scope changes occur.

For a small MVP, this might be a flat amount: for example, $10k on a $100k project as contingency. The CFO will understand that innovation projects have unknowns; the key is signaling that you’ve planned for them. Importantly, if you don’t end up using the contingency, that’s great – you come in under budget. But it’s there as insurance.

You can express the above neatly as
Total MVP Budget = Build Cost + (Run-Rate * X months) + Contingency.

For example,

  • Development = $80k,
  • Initial Cloud/API costs for 3 months = $15k,
  • Contingency = $15k (approx 15%).
  • Therefore: total ~$110k.

This format makes it very clear how the money will be used over time. Your finance team can also decide whether to treat the run costs as OpEx (operational expense) separate from the build cost (CapEx); but presenting them together gives an all-in figure.

By structuring the budget this way, you make it easy for executives to adjust levers. They might say, “What if we only fund 2 months of run costs initially?” or “What happens if we skip contingency and handle overages case-by-case?” – and you can respond with the impact. It moves the conversation from “Why does this cost X?” to “Are we investing enough in the right areas?”.

Example Budget Scenarios

Let’s illustrate what an AI MVP might cost in a few different scenarios. These examples are hypothetical but grounded in real-world projects:

Lean MVP (Single Flow, Out-of-the-Box Model)

“Chatbot assistant for internal knowledge base”

Imagine a basic genAI chatbot that answers employees’ FAQs by retrieving info from a company wiki. It uses a pre-trained API (no custom model training), and it’s accessible via a simple web page. No fancy integrations beyond maybe Single Sign-On. Estimated cost: ~$25k–$75k. This covers a few weeks of development to wire up the chatbot using an API, like OpenAI or Azure OpenAI, basic UI creation, and testing with sample data. Because it’s one self-contained flow, the team might just be 2–3 developers for a month or two.

Integration effort is minimal; maybe just connecting to the wiki data, which could even be done with flat files. Using a hosted model API keeps costs low and predictable. You’re not building model infrastructure. In Gartner’s terms, this is buy where you can, leveraging existing AI services as much as possible.

Why so “low” in cost? It avoids expensive steps . It’s essentially a proof-of-value prototype with just enough custom code to solve the specific problem. Many internal AI pilots fall in this range by design – they aim to test one idea cheaply before expanding. Direct usage fees during development might be a few hundred dollars at most.

Standard MVP (2–3 Flows, Moderate Integration)

“AI-powered customer support triage”

Now imagine an MVP that not only answers FAQs with AI, but also integrates with two systems: it pulls customer info from a CRM and creates support tickets in a helpdesk system. It might use a fine-tuned model for better accuracy on company-specific terminology. And it has a proper UI for support agents with login, dashboards, etc. Estimated cost: ~$75k–$150k.

Here we’re paying for more moving parts: multiple user flows (answering questions, creating tickets, maybe escalating to human), integration effort with two external systems (CRM and helpdesk API work), and a more polished frontend for at least internal use. There’s also likely some data preparation to fine-tune or customize the model.

Designing a slightly richer UI/UX and ensuring the app works in the existing support workflow adds design and testing time. It’s easy to see how these additions increase the budget relative to the lean MVP. Still, by keeping it to 2–3 core flows, it stays around the low to mid six figures.

Controlling costs: The team might use low-code integration tools or APIs to speed up connecting to CRM/helpdesk. They might fine-tune a smaller model or use prompt engineering to avoid a fully custom model build. Also, focusing on support agents means you can get away with a utilitarian UI initially, deferring a slick customer-facing design to a later phase.

Many “standard” MVPs in companies fall in this bracket: a bit of integration, some customization, but not an entire enterprise overhaul. It’s an investment, but reasonable for a meaningful pilot with real users. For context, Gartner’s survey showed even small enterprises often spent around $300k on AI pilots.

Complex MVP (Multi-System + Compliance + Custom Model)

“AI-driven financial advisor platform”

Consider an MVP that ingests data from several sources, uses a custom-trained model, and must meet strict compliance. It has a full web app interface for end customers and perhaps mobile support. Estimated cost: $150k–$300k+. This is essentially at the boundary of an MVP and a full product, and the budget reflects the complexity.

Costs pile on from multiple angles: data pipeline integration from various systems, a substantial effort to train or fine-tune models and possibly experiment with open-source ones, which incurs compute costs and ML engineering time, rigorous testing/validation to comply with regulations, and a production-grade UX.

Additionally, you’d invest in robust infrastructure from day one, whereas smaller MVPs might get by with basic setups. Compliance efforts could include things like bias and robustness evaluations, model documentation, encryption of sensitive data, etc., which drive up both engineering and possibly legal consultation costs. It’s no surprise a complex MVP can reach a few hundred thousand dollars.

In fact, for large enterprises, multi-phase MVP programs can cross seven figures – though they might not call those “MVPs” at that point. The key is that every requirement adds significant work streams. If you’re heading into this territory, it’s wise to break the project into smaller milestones or “internal MVPs” – ensure each major component is proving value before proceeding.

Otherwise, you risk spending big without a guarantee of product-market fit. A common trap at this level is over-engineering: teams pre-build for scale and compliance that might not be needed if the core idea doesn’t fly. Strong governance is needed to keep the MVP focused.

As the above examples show, cost grows with complexity. It’s not uncommon for a well-scoped $50k pilot to evolve into a $300k project once additional features or requirements creep in. Being aware of these ranges helps set realistic expectations with stakeholders. It’s perfectly fine to start small and then scale up investment as the project demonstrates value. In fact, that’s preferred.

Many successful AI products today began as a scrappy MVP built for under $100k which, after proving its worth, received further funding to expand.

Operational Costs: The Run-Rate You Can Expect

Building the MVP is one side of the coin – running it in practice is the other. AI systems can incur ongoing costs that scale with usage, unlike traditional software which, once built, might be cheap to operate. Let’s break down the typical “run-rate” costs and how to estimate them:

API Token Costs

If your MVP uses a large language model via API, like OpenAI, Azure OpenAI, Google Vertex AI, Anthropic Claude), you’ll be billed per token (which is roughly ¾ of a word) generated or processed. Prices vary by provider and model power.

Batch & Caching Strategies

A critical way to reduce run-rate costs is through batching and caching. Many AI providers incentivize efficient usage. For example, OpenAI’s new Batch API offers ~50% cost savings on token fees for batched requests, and Anthropic reports up to 90% cost reduction with prompt caching .

In practice, this means if your app can queue up requests and send them in one go, or avoid repeated calls for the same question, you’ll spend significantly less. An MVP should absolutely employ these tactics if possible.

One common approach: embedding caching. You store vector embeddings of user queries and documents so that you don’t re-embed the same text repeatedly. Likewise, if user asks something the system has seen before, you can return the stored answer instead of calling the LLM again. These optimizations can often cut the ongoing bill by a large factor, especially as usage scales.

Cloud Infra & Other Services

Beyond the AI model calls, remember the typical cloud services that any app uses. Hosting your application server (on AWS, Azure, GCP, etc.), databases to store user data or conversation history, authentication services, monitoring tools – all incur monthly fees. The good news is that for an MVP with light usage, these are usually minor.

A small cloud VM might be <$100/month, or using serverless functions might even be in free tier; a managed database could be $50–$200/month depending on size. Make sure to include them in your run-rate estimate though. Vector databases, like Pinecone, Weaviate, or Azure Cognitive Search if you use it for embeddings charge by data volume and queries.

But again, at MVP scale this might be tens or low hundreds of dollars a month. As your user counts grow, you’d project these costs accordingly.

The takeaway for budgeting

Estimate your expected usage, like requests per day, tokens per request, and multiply by the provider’s pricing to get a monthly run-rate. It’s wise to forecast a range. That way the CFO isn’t surprised if the AI bill is higher in month 2 than month 1.

It will also force discussions about scaling costs: If the cost per user is too high, you’ll need strategies to optimize. Fortunately, as we’ve noted, there are plenty of knobs to dial down the ongoing costs without severely hurting quality – it just requires conscious planning.

Finally, keep in mind operational cost vs value. If your AI MVP is delivering significant business value, like automating tasks, generating leads, improving customer satisfaction, a few thousand a month in usage cost is usually trivial in ROI terms.

The danger is when you don’t monitor these costs and suddenly a surprise bill comes that overshoots the value but by reading this, you’re already a step ahead on avoiding that!

Build vs Buy for Your First AI Feature

One strategic decision you’ll face is whether to build components in-house or leverage existing solutions; essentially, “build vs buy”. For AI projects, this often comes down to using out-of-the-box AI services versus custom-developing your own models and infrastructure.

The answer isn’t binary; many times the best approach is a blend. Gartner analysts put it well: “Buy where you can, build where it differentiates, and integrate everything for speed, scale, and compliance.”

In other words, use pre-built components for the generic parts and save your custom-building for the secret sauce that sets you apart.

For an MVP, the bias should be toward buying/using existing tools.

“API-First” Approach (Buy/Lease)

Starting with an API like OpenAI, Azure, AWS Bedrock, or Google’s models is usually the fastest path to a working product. You essentially rent a world-class model for a per-use fee, instead of spending months and $$$ developing your own. This makes eminent sense when time-to-market is critical and your team might not have deep ML engineering expertise.

The first AI feature can often be delivered via prompt engineering on a pre-trained model. The upside: you get results immediately and can focus on the business logic and UX. The downside: usage costs and potential dependency on an external vendor. But at MVP stage, the benefits far outweigh those drawbacks. If the MVP succeeds and usage soars, you can later weigh the cost trade-off of continuing to pay per call vs. building an in-house model.

When to Consider Custom Models or Open Source (Build)

There are scenarios where a custom-built approach is justified even early on. For instance, if your AI solution needs to operate on sensitive data that cannot leave your environment, using an external API might be a non-starter. You might need to use an open-source model on your own infrastructure. Or if the domain is highly specialized, a generic model might not perform well and custom training is required for acceptable results.

Another reason could be cost at scale: maybe you project that at full product scale, using the API would cost millions per month, so you want to invest early in a more efficient custom model. Building custom also gives you more control. However, be aware that building your own AI is a complex endeavor; it requires ML talent, data pipelines, and time.

A famous stat from Forrester: three-quarters of organizations that try to build advanced AI (like “AI agents”) in-house will fail and end up seeking outside help or pre-built solutions. The point isn’t that your team isn’t smart. It’s that AI tech is evolving fast and it’s hard to reinvent the wheel under time pressure. So proceed with caution on a pure DIY approach for your first outing.

The “Blend” Strategy

In practice, many successful AI projects combine both. For example, you might “bring your own AI” by using an open-source model, but host it on a managed service for convenience. Or use a pre-trained model’s API for core capabilities while augmenting it with some custom code or rules for your specific domain, sometimes called a hybrid or composite AI approach.

Another blended tactic is using a smaller local model for cheap routine tasks and calling a big expensive model only for the hardest questions, thereby balancing quality and cost. Gartner’s model of buy/build/blend basically suggests: use off-the-shelf for standard needs, build custom only for truly unique differentiators. That’s sound advice to maximize ROI.

In summary, for an MVP lean toward existing services, you get to demo value quickly. As you move beyond MVP, re-evaluate the build-vs-buy decision based on what you learned:

– Did the off-the-shelf model meet your needs? Great, stick with it.

– Did you hit limitations (accuracy, latency, data privacy)? That might justify investing in a custom model next.

– Are usage costs becoming too high? Perhaps a fine-tuned model you host could be cheaper long run but remember to factor in development and maintenance costs!.

The first AI feature is about learning. Don’t be afraid to start with “buy” and change course to “build” later if neede. It’s much easier to swap out a working MVP’s backend than to delay everything to perfect a custom model upfront with no real-world input.

Practical Ways to Stay Inside Budget

However big or small your budget is, it’s always good practice to minimize waste and get the most value per dollar. Here are some practical tips to keep your AI MVP on-budget without sacrificing quality:

Cache Results Aggressively

If the same or similar requests happen frequently, implement caching. For instance, if one user asks “What is our refund policy?” and the AI fetches an answer, store that response. The next user with the same query can get the answer instantly with no API call cost.

As noted earlier, caching can cut costs by up to 90% for repeat queries. This also improves responsiveness (answers served from cache are faster). Many teams use a simple in-memory cache or a small database for this. The key is identifying what can be safely cached. Even caching partial results helps. Bottom line: Pay for a computation once, reuse it whenever you can.

Batch Your AI Calls

This is a more technical tip. If your application architecture allows, send tasks to the AI in batches rather than one by one. For example, instead of 10 separate API calls, you send 1 call with 10 questions. Providers like OpenAI and Google often charge less for batched requests because it uses resources more efficiently on their end. In some cases, it’s literally half the cost per token.

Batching might introduce a tiny delay, but for many backend processes that’s fine. Similarly, if you’re doing embedding of text, send a list of texts in a single request (OpenAI’s embed endpoint allows this) instead of looping one by one. It’s a simple optimization that can substantially lower your monthly bill.

Use Managed Services (for Infrastructure)

Don’t waste time or money re-inventing plumbing. Need a vector database for semantic search? Use a managed one rather than rolling your own on a cluster of VMs. Need to orchestrate prompts and post-process outputs? Services and libraries exist for this; for instance, OpenAI offers function calling, and there are tools like LangChain. By using these, you save development effort and often get scalability out-of-the-box.

Managed services might seem to add cost, but for MVP scale they’re usually cheap or free-tier, and they prevent costly engineer-hours on undifferentiated work. In essence, leverage the cloud. Our team often integrates clients’ MVPs with existing cloud AI tools. For example, using a ready-made AI integration pipeline for data prep or an existing logging service for monitoring – so we can focus on the unique parts of the solution.

Prioritize One Flow (Gold-Plating Later)

Keep the MVP’s functionality as simple as possible. It’s tempting to add “nice to haves” – a few extra analytics, multi-language support, a fancy UI element but each adds cost. A practical approach is define a single “happy path” user flow that delivers the core value. Build that first, get it in users’ hands, and only then consider adding features if absolutely needed. This prevents budget bleed on features that might not even matter to users.

It also means you can direct more resources to optimizing that one flow instead of splitting effort across many half-baked features. Remember, an MVP by definition is minimum viable product. Gold-plate it later when you have proof it’s worth investing more. This not only saves money but often leads to a better product, because a focused experience is easier to get right.

Keep UX and GTM Lean

Yes, a pretty design and big launch event are exciting but they are budget-eaters. For MVP, use basic UX. You can even use tools like Figma prototypes or simple Bootstrap templates to simulate parts of the UI rather than fully coding them. Users in a pilot are usually forgiving if the interface is spartan, as long as it solves their problem. Similarly, hold off on expensive GTM campaigns. There’s no sense spending big on marketing an AI MVP until you are confident in its results.

Prefer a soft launch to a targeted group, gather feedback, iterate, and gradually expand. If you need to show some marketing presence, use low-cost channels rather than paid campaigns. In short, validate first, amplify later. By keeping the UX and marketing lean, you allocate budget to what truly matters in an MVP – the AI’s functionality and learning from real use. When you’re ready to scale, you can involve AI-focused design for UI polish and ramp up AI marketing efforts – but those can be Phase 2 line items.

Automate Testing and Monitoring Early

This might sound like an extra task but it ultimately saves cost. Catching bugs or model errors early will prevent costly fixes or bad outcomes down the line. Set up basic automated tests for your AI pipeline. Also implement monitoring from day one: track how often users engage, where failures happen, and how much you’re spending.

These metrics can alert you if something is going off track; for example, if a new version of the model suddenly doubles your token usage. Knowing that quickly lets you rollback or adjust before running up a huge bill. It’s much cheaper to tweak things in-flight than after a month of runaway costs or user complaints.

By following these practices, you’re effectively applying lean startup principles to AI development. The goal is maximizing learning per dollar. Many of the above boil down to reducing redundant work. Collectively, they can easily cut an AI MVP budget requirement by 30–50% compared to a naive approach and often result in a better outcome.

FAQs

How much does a 90-day proof-of-value (PoV) project cost?

It varies, but often on the order of $50k–$100k for a well-scoped 3-month pilot by a small team. We’ve seen “garage style” MVPs done for less, if leveraging lots of existing components, and conversely some corporate PoVs hitting $150k+ when extra integration or compliance is involved.

As context, Gartner’s data shows small enterprises spent around $300k on average for GenAI proofs-of-concept in 2023 – but those likely included broader scopes than a single 90-day use case.

  • The key is scope: a focused PoV to test one hypothesis (e.g. can an AI summarize our reports accurately?) can be done lean.
  • Allocate budget for ~2–3 developers and some design/PM support for 2–3 months, plus cloud/API fees. That typically lands in low six figures or below.
  • Always include a bit of contingency because experimentation is involved. If the PoV requires custom model training or heavy data prep, it will skew higher. Conversely, if it’s mostly orchestration of existing AI services, it could be done closer to the lower end.

In summary: many 90-day PoVs are achievable in the tens of thousands of dollars range, which is a reasonable “dip the toe in” investment for most mid-to-large organizations.

What’s the average LLM monthly bill for a pilot project?

For a pilot with light usage, the monthly AI API bill can be surprisingly low – often only a few hundred dollars or less.

For example, a pilot handling ~200 queries a day with GPT-3.5 might cost under $10 a month in OpenAI fees. With GPT-4 at that usage, maybe around $200/month. As usage increases, costs rise linearly. A more active MVP with ~1,000 queries/day could run ~$1k/month on GPT-4.

So, the “average” really depends on scale: many initial pilots we see have negligible costs (under $500/mo) because user counts are small during testing. The variability is high: it’s usage-based. Always monitor your token consumption. We recommend setting up cost alerts with the provider. For example, OpenAI allows you to set hard spending limits.

Also, consider that if your pilot is using other services (vector DB, cloud hosting), those add to the run-rate, but usually not nearly as much as the LLM if usage is heavy. By using caching and batch calls, teams often keep the pilot phase costs well under control.

In short, expect maybe hundreds of dollars per month in the pilot stage, unless you have an unexpectedly large user base or an extremely chatty application. That’s a small price to pay for validation. If it starts creeping into thousands, that likely means your pilot is successful, at which point, you’ll have budget to optimize or scale since value is proven!

Can I reduce the model/API costs without hurting quality?

Yes – there are several tactics to trim costs while maintaining good performance:

– Use the right model for the job: Not every task needs the most expensive, state-of-the-art model. For many use cases, GPT-3.5 or a smaller model can achieve ~90% of GPT-4’s quality at a fraction of the cost. You can also adopt a tiered approach. For example, try with a cheap model first, and only if the response is unsure/confident score low, then call the expensive model. This “fallback” strategy ensures quality where needed but saves money on easy queries.

– Optimize prompts and response size: Shorter prompts and limiting the response length can drastically cut tokens used. Ensure your prompts are concise and the model isn’t rambling in output. If you only need a summary, don’t let it output a novel. This doesn’t hurt quality of the result because you’re explicitly focusing it. Prompt engineering is key here – a well-crafted prompt can get the model to produce the desired output in fewer tokens.

– Leverage caching (again): Reducing repeated work as discussed will not hurt quality – it’s delivering the same quality, just without paying twice. Similarly, if you pre-compute certain things (like embeddings for your knowledge base), you pay once upfront instead of repeatedly.

– Monitor and iterate: Watch where your costs are going. You might find 5% of queries are using 50% of tokens, perhaps someone asked the AI to analyze a huge document. Those are opportunities to impose limits or handle differently. By curbing outlier cases, you keep quality for the typical use case high while not letting unusual use cases drive up cost.

– Batch calls: as noted, this doesn’t hurt quality – it’s an invisible change to users.

– Fine-tuning or custom model (in some cases): It sounds counter-intuitive but if you fine-tune a model on your specific domain, you might achieve the needed quality with a smaller model or shorter prompts, which then reduces per-call tokens. For example, a fine-tuned model might require less explanation in the prompt to do what you want. This is a more advanced strategy and only worth it if you see a clear win, but it’s one way some teams reduce long-term costs. OpenAI’s pricing for fine-tuning is reasonable in some cases, and the result might let you use GPT-3.5 with quality close to GPT-4 for your task.

Overall, smart engineering can typically cut a large chunk of the “naive” cost. Gartner encourages exploring pricing model options and continuously monitoring usage to find savings. In our experience, by applying the above, teams often save 30%+ on cloud AI fees with no loss of quality or coverage. It’s about being efficient, which your finance team will appreciate.

How fast can we launch an AI MVP?

Potentially in as little as 8–12 weeks – if you have a focused scope and an experienced team (or partner).

We’ve delivered initial AI pilots in a 2-3 month timeframe when the stars align: data is available, stakeholders are decisive, and using mostly existing tech.

A 90-day roadmap for an MVP is aggressive but achievable for a single use case. That said, many organizations take longer in practice. Gartner research found that on average, it’s around 8 months to move an AI project from prototype to production. The slower pace often comes from internal processes, extensive testing, or unclear requirements that cause iteration.

To launch fast, you should cut red tape and focus on the core: spin up a small, cross-functional tiger team, empower them to make decisions, and limit the project to the MVP definition. Using cloud services, as opposed to procuring new on-premise hardware, also accelerates timelines significantly. If compliance review is needed, involve those stakeholders from day 1 so it doesn’t become a last-minute holdup.

In summary, best-case: ~3 months to get an MVP in users’ hands. We’ve seen it done, and it’s our standard offering for AI product development. Typical-case: 4–6 months accounting for organizational overhead. Anything beyond that and you’re likely working on more than an MVP. It might be a sign to revisit scope. The goal is to learn quickly, so we advocate aiming for that ~12-week launch if at all possible. Speed is a competitive advantage in AI right now.

Building an AI MVP can feel like navigating uncharted waters, especially when it comes to budget and planning.

Let’s recap the key points to anchor your AI strategy

Cost Ranges: A realistic AI MVP can cost anywhere from $25k on the very lean end to $300k+* for complex versions. Keep it lean if you can – prove value, then invest more. Many successful projects start small and then scale budget as needed.

Key Cost Drivers: Scope creep, poor data readiness, unnecessary custom builds, and lack of integration planning are common budget busters. In contrast, tight focus, using existing models, and planning for compliance/ops from the start will keep costs in check. Remember that more spending doesn’t guarantee success – spending smart does. Half of organizations that stumble with AI do so because of cost-related missteps. Avoid the trap of “AI science projects.” Tie spending to clear use-case goals.

Run-Rate Awareness: Don’t launch and pray the bills are fine. We showed how to estimate token costs and use simple math to project monthly expenses. Typically, they’re very manageable at MVP scale. But as usage grows, ensure you have cost monitoring and optimization strategies (batch, cache, model selection) in place. The cost of AI should be treated as a design parameter of your solution, not an afterthought.

Common Traps:

  1. Over-engineering: pouring money into a perfect system before confirming the idea.
  2. Underestimating integration: thinking the model alone is the product, when the real work is making it useful in business workflow.
  3. Neglecting users: blowing budget on tech but not on user feedback cycles. Being aware of these will help you reallocate budget to what matters most for success – delivering business value.

With proper planning, an AI MVP can be a high-impact investment, not a gamble. By using the guidance above, you’re stacking the odds in your favor: delivering a solution that is both cost-effective and valuable.

Ready to take the next step? We’re here to help translate this advice into action for your project:

Book a 30-min AI Project Scoping Call with High Peak.

Embarking on an AI MVP is exciting! And with the right preparation, it can also be highly economical. We look forward to hearing about your vision and, if you need a development partner, helping you bring it to life on time and on budget. Here’s to building something amazing!

(*Disclaimer: All figures are directional and for planning purposes only. This content is for general informational use and isnotfinancial or legal advice.)