The question I get asked most frequently these days is: should we build our own LLM infrastructure or just use an API? It’s a deceptively simple question with a complicated answer. I’ve seen teams spend hours/days setting up self-hosted infrastructure for use cases that would have been perfectly served by an API, and I’ve seen teams hit API rate limits and cost ceilings that make their product unviable. The decision isn’t just technical—it’s strategic, financial, and often existential for your product.
Let me be clear upfront: there’s no universal right answer. What makes sense for a startup validating an idea is different from what makes sense for an enterprise handling sensitive data. The trick is understanding when each approach aligns with your actual needs, not just your aspirations.
Understanding the Trade-Offs
Before we dive into when to choose what, let’s be honest about what you’re really comparing. This isn’t just about cost or control—it’s about a fundamental choice in how you want to operate. 2025-01-17-when-to-self-host-an-open-source-llm-vs- Self-hosting means: You own the infrastructure, the models, the data flow, and the responsibility. You get complete control but also complete complexity. You’re not just running software—you’re managing GPU clusters, optimising inference pipelines, handling model updates, and dealing with scaling challenges. I’ve watched teams underestimate this complexity and end up with a system that costs more and performs worse than just using an API.
Using an API means: You’re trading control for convenience. You get state-of-the-art models, automatic updates, built-in scaling, and someone else to handle the infrastructure headaches. But you’re also dependent on a third party, subject to their pricing changes, rate limits, and availability. Your data might leave your infrastructure, and you’re building on someone else’s platform.
The key is recognising that both are valid choices—just for different situations. The mistake I see most often is choosing based on ideology (“we must control our own tech”) rather than actual requirements (“we need X, Y, and Z”).
When to Self-Host
Self-hosting makes sense when the benefits of control and customisation outweigh the costs of complexity (tbf it is a lot easier these days!) and infrastructure. Here are the situations where I’ve seen self-hosting pay off:
Data privacy and compliance requirements: If you’re handling sensitive data — medical records, financial information, legal documents etc data—self-hosting gives you complete control over where your data lives and how it’s processed. In these cases, self-hosting isn’t a nice-t2025-01-17-when-to-self-host-an-open-source-llm-vs-o-have—it’s a requirement.
High-volume, predictable workloads: If you’re making millions of API calls per day with predictable patterns, the economics often tip in favour of self-hosting. API pricing is designed to be profitable for the provider, which means at scale, you’re often paying a premium. If you can amortise (the finance person in me loves this aspect!) the cost of infrastructure across enough usage, self-hosting can be significantly cheaper. But be realistic: you need to account for not just the hardware, but the engineering time to build and maintain (huge factor here!) the system.
Need for customisation and fine-tuning: Open source LLMs can be fine-tuned on your specific data, adapted for your domain, and modified for your use case. If you need models that understand your industry’s terminology, follow your company’s style guide, or integrate deeply with your existing systems, self-hosting gives you the flexibility to customise. Note - APIs offer some customisations though.
Latency requirements: For some applications, every millisecond counts. If you’re building real-time systems where API latency would create noticeable delays, self-hosting can give you better performance (if done correctly) by keeping everything in your own infrastructure. But this only matters if latency is actually a problem—most applications don’t need sub-100ms response times. Also, this is a tricky one, many providers who main business is offering the API has done a great job here already.
Cost predictability: This is the key one in my opinion. API costs scale with usage, which can make budgeting difficult. If your usage is unpredictable, API costs can spike unexpectedly. Self-hosting gives you fixed infrastructure costs (plus the cost of scaling when needed), which can be easier to budget for if you have predictable growth patterns.
2025-01-17-when-to-self-host-an-open-source-llm-vs- And a minor point - Reducing vendor lock-in: Building on APIs means you’re dependent on that provider. If they change pricing, alter terms, or shut down, you’re stuck. Self-hosting gives you more control over your technical destiny, though you’re still dependent on open source projects and your own ability to maintain the system. If you design your systems in such way you can make this plug and play when you move to self-hosting from API - so not a huge deal in my view.
When to Use an API
For most teams, most of the time, an API is the right choice. Here’s when it makes sense:
Speed to market (factor 1): If you need to ship quickly and validate an idea, an API gets you started in days, not months. Self-hosting requires significant engineering investment before you even get to your first inference. I’ve seen teams spend weeks building self-hosted infrastructure when they could have shipped an API-based prototype in few days and learned much more from real users.
Low to moderate volume (factor 2 in my view): If you’re making thousands or even tens of thousands of API calls per day, the cost of self-hosting infrastructure (hardware, engineering time, operational overhead) usually exceeds API costs. The break-even point varies, but for most startups, you’d need to be processing hundreds of thousands of requests daily before self-hosting becomes economically attractive.
Limited engineering resources: Self-hosting isn’t just about running models—it’s about building and maintaining an entire infrastructure stack. You need engineers who understand GPU optimisation, model serving, monitoring, scaling, and all the operational complexity that comes with it. If you’re a small team or your engineers’ time is better spent on product features, an API makes more sense.
Access to latest models: API providers constantly update their models, giving you access to the latest improvements without any engineering effort. With self2025-01-17-when-to-self-host-an-open-source-llm-vs–hosting, updating models requires you to download, test, and deploy new versions—work that adds up over time. If having the latest model capabilities is important, APIs give you that automatically.
Handling complexity you don’t want: Running LLMs at scale is genuinely complex. There’s model optimisation, GPU management, batching strategies, caching, monitoring, error handling, and a dozen other concerns. APIs handle all of this for you, which is valuable if you’d rather focus on building your product than becoming experts in LLM infrastructure.
Variable or unpredictable usage: If your usage patterns are spikey or unpredictable, APIs handle the scaling automatically. With self-hosting, you either over-provision (wasting money on idle resources) or under-provision (failing under load). APIs scale with your needs without you having to think about it.
Experimentation and prototyping: When you’re still figuring out what you’re building, APIs let you experiment quickly and cheaply. You can try different models, test various approaches, and pivot based on what you learn. Once you’ve validated your approach and understand your requirements, you can always migrate to self-hosting if it makes sense.
The Hidden Costs
One of the biggest mistakes I see is teams only comparing the obvious costs—API pricing vs hardware costs—and ignoring the hidden costs that make self-hosting more expensive than it appears.
Operational overhead: Self-hosting means you’re responsible for uptime, performance, debugging, and all the operational concerns that come with running infrastructure. This creates ongoing operational burden that APIs handle for you. If your team is already stretched thin, adding this operational load might not be worth it.
Engineering time: The biggest hidden cost of self-hosting is engineering time. Someone needs to set up the infrastructure, configure the models, build monitoring, handle scaling, and maintain everything. This is time not spent on product features. I’ve seen teams spend 30% of their engineering capacity maintaining self-hosted LLM infrastructure when an API would have freed that time for product work. 2025-01-17-when-to-self-host-an-open-source-llm-vs- Opportunity cost: Every hour spent on infrastructure is an hour not spent on building features that drive your business. If you’re a startup trying to find product-market fit, spending months on infrastructure optimisation might mean missing the window for your product. APIs let you focus on what matters for your business.
Model updates and improvements: With self-hosting, you need to actively track, test, and deploy model updates. APIs handle this automatically. The gap between what’s available via APIs and what you’re running can grow quickly if you’re not actively maintaining your self-hosted setup. This is a huge issue and should be factored in when making the call.
Scaling complexity: Scaling self-hosted LLMs isn’t just about adding more GPUs—it’s about optimising inference pipelines, managing load balancing, handling failures, and all the complexity that comes with distributed systems. APIs abstract this away, which is valuable if you don’t want to become experts in distributed LLM serving.
A Practical Decision Framework
Over time, I’ve developed a simple framework for making this decision:
Start with APIs if:
- You’re validating an idea or building an MVP
- Your team is small or engineering time is limited
- Your usage is low to moderate (under ~100K requests/day)
- You want to ship quickly and iterate fast
- You don’t have dedicated ML infrastructure expertise
Consider self-hosting if:
- You have strict data privacy or compliance requirements (you can work around this even with API)
- Your usage is very high (millions of requests/day) and predictable
- You need custom models or deep customisation
- API costs are becoming a significant portion of your expenses (this is key here!)
- You’ve validated your product and understand your requirements
The hybrid approach: Sometimes the answer is both. Start with APIs to validate and learn, then migrate to self-hosting for production workloads that make economic sense. I’ve seen teams use APIs for development and experimentation, then self-host for production systems that need customisation or scale. 2025-01-17-when-to-self-host-an-open-source-llm-vs-
Real-World Examples
When API make sense: Building a customer support tool that used LLMs to draft responses. They can start with an API, ship in two weeks, validate the concept with real customers, and only later you need to consider self-hosting when they reached significant scale. By that point, they would have understood their actual requirements and could make an informed decision.
When self-hosting make sense: Building a legal document analysis tool for a regulated industry. They need to process sensitive documents, couldn’t send data to third-party APIs (due to contractual requirements), and required models fine-tuned on legal terminology. Self-hosting wasn’t just cheaper—it was the only option that met their requirements.
When the decision is wrong: Building self-hosted infrastructure for a product that is not market tested. If they use an API, they could validate the pivot in weeks and save weeks of engineering time.
The pattern I see is: teams that succeed start with the simplest solution that works, validate their approach, and then optimise based on real requirements rather than hypothetical ones.
Key takeaways
The choice between self-hosting and APIs isn’t about which is objectively better—it’s about which aligns with your specific situation. Most teams should start with APIs if they can, validate their product, understand their actual requirements, and only then consider self-hosting if it makes economic or technical sense.
Self-hosting gives you control but adds complexity. APIs give you simplicity but reduce control. The right choice depends on your priorities: if you need to move fast and learn, APIs win. If you need control and customisation, self-hosting might make sense—but only if you’re ready for the operational complexity that comes with it.
You can always migrate!! Starting with an API doesn’t lock you into it forever. Many successful products started with APIs and moved to self-hosting when they reached scale or developed specific requirements. The key is making the decision based on your current situation, not your aspirational future.
The best LLM infrastructure decision is the one that lets teams focus on building products users want rather than becoming experts in LLM operations. Sometimes that means self-hosting. More often, it means using an API and spending your engineering time on what makes your product unique. Choose based on what you actually need, not what sounds impressive.