Self-Hosting LLMs on AWS vs Managed APIs for SaaS
Every SaaS team using large language models eventually runs into the same debate. Should we keep calling managed LLM APIs, or should we start self-hosting LLMs on AWS?
It sounds like a pricing question. It usually isn’t. The real tension is between flexibility, operational burden, and how predictable your workload actually is.
Why Managed LLM APIs Win Early
Most teams start with managed APIs from providers like OpenAI or Anthropic because they eliminate infrastructure decisions entirely. No GPUs, no autoscaling logic, no capacity planning. You pay for usage and move on.
Early-stage SaaS traffic is uneven. Features change quickly. AI usage grows in unpredictable ways. In that environment, usage-based pricing is forgiving.
For example, imagine a SaaS feature processing around 5 million tokens per day. Using a managed LLM API at roughly $10 per million tokens, that feature costs about $50 per day, or roughly $1,500 per month. There is no idle cost and no operational overhead.
For most teams early on, that tradeoff is worth it.
Where Managed APIs Start to Break Down
As the product matures, the limitations show up in different places.
Prompts get longer because domain knowledge lives outside the model. Latency increases as context windows expand. Costs scale directly with usage, and AI spend starts growing faster than revenue.
More importantly, the model never truly internalizes your business logic. It has to be reminded, every time, how your system works.
This is usually when teams start seriously considering self-hosting LLMs on AWS.
What Self-Hosting LLMs on AWS Actually Looks Like
Self-hosting does not mean training a massive model from scratch. In practice, teams run a fine-tuned or adapted mid-sized LLM behind an internal inference service on Amazon Web Services.
That model lives on GPU-backed infrastructure and must be available whenever traffic arrives. This shifts the cost model from usage-based to capacity-based.
Here’s a realistic example.
A common choice for LLM inference is a single GPU instance like a g5.2xlarge. On demand, this can cost roughly $1,200 per month. That cost exists whether the model is serving requests or sitting idle.
If that GPU handles 100 million tokens per month, your effective cost is $12 per million tokens, roughly comparable to API pricing. If it handles 300 million tokens, the cost drops to $4 per million tokens. At that scale, self-hosting clearly wins.
If usage dips, however, you still pay the full $1,200.
This is the tradeoff teams underestimate. Self-hosting rewards consistency and punishes variability.
Why Custom LLMs Are Often the Real Motivation
Cost alone rarely justifies the move. Product quality usually does.
When a model is fine-tuned on proprietary workflows, terminology, and constraints, behavior becomes more consistent. Prompts shrink. Latency improves. Edge cases decrease. The model starts reasoning the way the product expects, not the way a general-purpose assistant does.
At that point, self-hosting becomes less about saving money and more about owning behavior. Cost optimization follows later, once usage stabilizes.
The Pattern That Actually Works in SaaS
Very few successful teams go all-in on one approach.
Managed LLM APIs continue to handle general reasoning and user-facing interactions where reliability matters most. Self-hosted LLMs on AWS take over high-volume, domain-specific workloads where customization and cost control matter more than flexibility.
Over time, workloads shift gradually, not all at once. This avoids premature infrastructure commitments while still creating a path toward lower marginal costs.
Where Cost Visibility Becomes Critical
Once you mix managed APIs with self-hosted LLMs, cost clarity becomes non-negotiable.
GPU spend is buried in EC2 or EKS. API usage shows up separately. Fixed and variable costs blend together. Without proper attribution, teams struggle to answer basic questions about which features are driving AI spend.
This is where many teams lose control of margins without realizing it.
Spend Shrink helps teams understand the real infrastructure cost of self-hosting LLMs on AWS, so optimization decisions are based on actual data, not assumptions.
The Right Question to Ask
The wrong question is whether self-hosting LLMs is cheaper.
The better question is whether your SaaS has reached the level of scale and predictability where owning model infrastructure makes sense.
Managed APIs are usually the right starting point. Self-hosted LLMs become valuable when customization, consistency, and sustained volume justify the fixed costs.
Teams that get the timing right gain leverage. Teams that move too early usually trade one cost problem for a more painful one.