Self-Hosted LLM vs Public API: A Practical Comparison

The choice between a self-hosted LLM and a public API is one of the most critical architectural decisions teams face when building AI products. Both options provide access to language models that can power chatbots, analysis tools, automation systems, and internal assistants. But in production environments, the difference goes far beyond the possibility of generating answers. The choice affects cost structure, latency, data control, security, maintenance complexity, team skill requirements, and the long-term evolution of the product. In this paper, we outline a practical decision framework for selecting between self-hosted LLMs and public APIs. We break down the most critical trade-offs, so teams can make the right choice before committing to an architecture that will define their product for years.

Hi there! This paper is talking about a single practical decision: to run your own LLM or rely on a public API. We skip theory and focus on production reality. The sections below break down the key dimensions that actually matter when this decision is made: data privacy, infrastructure ownership, cost structure, latency and performance, scalability, model lifecycle management, portability, and operational risk. For each section you will see the main pros and cons for both the self-hosted model and public API.

If you want a broader view on building production-grade chatbots, this paper is a great starting point - ChatBot Application Guidelines.

Data Privacy

Data privacy is one of the key factors in selecting an LLM for production system. Usually, the comparison between a self-hosted model and a public API can be reduced to two questions: do you need full control over the data lifecycle, and are you willing to trust a third-party provider with sensitive inputs?

Self-hosted LLM

A self-hosted LLM gives you ownership over the data pipeline within your own infrastructure. This approach is typically required in environments handling PII (Personally Identifiable Information), PHI (Protected Health Information), financial or transactional data, or operating in heavily regulated industries such as banking, insurance, or government.

Pros

All data remains inside your infrastructure, allowing the control over logging, tracing, and retention policies. Privacy and compliance requirements can be implemented strictly according to internal and regulatory standards, without relying on third-party storage or risking unintended data usage for external model training.

Cons

Full ownership also means full responsibility. Your team must design and continuously maintain the entire security perimeter, including access controls, network isolation, and data protection. Incident response, audits, and compliance verification are entirely internal, and any misconfiguration or oversight becomes your direct risk.

Public API

Public LLM APIs offer strong security guarantees, but require sending data to an external provider. Even with encryption and no-training policies, the trust model ultimately depends on vendor assurances and contractual terms. This approach is usually acceptable for non-sensitive product data, marketing or support content, internal productivity tools, prototyping, and early-stage development where strict data control is not critical.

Pros

Using a public API removes most internal security and infrastructure responsibilities. Providers typically hold mature compliance certifications, reducing legal and operational overhead.

Cons

Data leaves your security perimeter and is processed by a third party. Logging, telemetry, and retention policies are defined by the vendor and may not be fully transparent or configurable. This can complicate compliance with strict regulatory, regional, or data residency requirements, especially in sensitive or highly regulated domains.

Infrastructure

Infrastructure directly defines cost, flexibility, and operational overhead. The gap between self-hosting and public APIs is significant - not only technically, but also in terms of organizational complexity and required expertise.

Self-hosted LLM

A self-hosted LLM requires full ownership of the entire infrastructure lifecycle. This includes provisioning and operating GPU resources, configuring load balancing, caching layers, and inference servers, as well as managing the model.

Pros

Self-hosting provides maximum flexibility and control over the infrastructure stack. You can tune hardware utilization, inference performance, scaling strategies, and deployment topology to match your workload precisely, without external constraints or vendor-imposed limits.

Cons

This approach comes with substantial operational overhead. You are responsible for GPU capacity planning, cluster orchestration, monitoring, failover, and ongoing maintenance. Infrastructure misconfigurations or scaling mistakes directly impact availability, performance, and cost.

Public API

Using a public LLM API drastically reduces infrastructure complexity. There is no need to provision hardware, manage GPUs, or operate DevOps pipelines - scaling, latency, uptime, and availability are handled by the vendor. The pricing model is usually usage-based, enabling fast iteration and low initial cost, but also introducing the risk of unpredictable expenses as usage grows. Fine-tuning and deeper infrastructure-level customization are typically limited or controlled by the provider.

Pros

Public APIs enable rapid onboarding with minimal operational effort. Teams can focus on product development instead of infrastructure, while benefiting from vendor-managed scaling, reliability, and global availability.

Cons

Infrastructure behaviour and optimization are hidden and vendor-controlled. You have limited influence over scaling strategies, performance tuning, and cost optimization, which can become a constraint as systems grow in size or complexity.

Cost

Cost is one of the most misleading factors because both approaches include visible and hidden expenses. The option that looks cheaper on paper can easily become more expensive in real production.

Self-hosted LLM

From a cost perspective, self-hosting shifts spending from usage-based pricing to fixed or semi-fixed infrastructure and operational costs. Instead of paying per token, teams invest in hardware, GPUs, and engineering capacity, taking ownership of cost efficiency, optimization, and financial risk.

Pros

At scale, a self-hosted LLM can become more cost-efficient once the infrastructure is stable. Hardware costs are typically flat or amortized over time, there is no per-token vendor margin, and teams can optimize performance and resource usage for their specific workloads. This makes cost growth more predictable for established products with consistent traffic.

Cons

Hidden costs are often underestimated. They include upfront hardware purchases or ongoing cloud GPU rentals, continuous model updates, security patching, and operational maintenance. Self-hosting also requires a larger engineering investment, including ML engineers, MLOps, and DevOps roles. Any performance degradation or downtime directly translates into internal productivity loss and operational risk.

Public API

With a public LLM API, costs are directly tied to usage rather than infrastructure ownership. This approach minimizes upfront investment and operational responsibility, shifting most complexity to the vendor, but introduces long-term exposure to pricing dynamics and traffic growth.

Pros

Public APIs significantly lower the barrier to entry. There is no need to invest in GPUs, infrastructure, or dedicated operations teams, and costs scale directly with usage. This makes expenses easier to manage in early stages and enables faster experimentation, iteration, and product launches without long-term infrastructure commitments.

Cons

Hidden costs emerge as usage grows. Consumption-based pricing can scale unpredictably with traffic volume, prompt length, or output size. Vendor pricing changes can directly impact unit economics and margins with limited control on your side. Low-latency models, higher throughput limits, or premium endpoints often come with significantly higher fees.

Note

A high-quality LLM accessed via a public API typically costs around $10 per million tokens (input and output combined). Deploying a self-hosted LLM, including infrastructure and ongoing operational overhead, usually starts at around $5000 per month for real-time access. From a purely hardware and operations cost perspective, switching from a public API to a self-hosted model becomes economically reasonable only after usage reaches approximately 500 million tokens per month. Of course it is just rough numbers - only one is important - you need to do these calculations for the specific case.

Latency & Performance

Latency and performance directly affect user experience, perceived system intelligence, and responsiveness of LLM-powered applications. While both self-hosted models and public APIs can deliver acceptable performance, the way latency is achieved and controlled differs fundamentally.

Self-hosted LLM

With self-hosted LLMs, latency is primarily a function of infrastructure design and operational maturity. Teams have control over where models are deployed, how inference is optimized, and how traffic is routed, but they also fully own the consequences of these decisions.

Pros

A self-hosted LLM can achieve very low latency when deployed close to users, such as on-premises or in regional edge environments. Highly optimized inference can significantly reduce response times. Full control over hardware and scaling strategies allows performance to be tuned precisely for specific workloads.

Cons

Performance depends entirely on hardware choices and tuning expertise. Cold starts, memory pressure, GPU contention, or poor batching strategies can quickly degrade latency. Achieving stable, API-level consistency requires continuous monitoring, optimization, and operational effort.

Public API

Public LLM APIs abstract performance management away from the application team. Latency characteristics are defined by the provider’s global infrastructure, routing, and traffic policies rather than by internal system design.

Pros

Public APIs typically deliver stable and predictable latency due to highly optimized cloud infrastructure. Vendor-side optimizations such as distributed serving and caching help maintain consistent response times under load. Global scaling is handled transparently, without provisioning or managing hardware across regions.

Cons

Latency is influenced by network conditions and regional availability. Response times may fluctuate due to provider traffic, throttling, or outages. Public APIs can be not always suitable for use cases requiring extremely low or tightly bounded latency.

Scalability & Maintainability

Scalability and maintainability determine whether an LLM-based system can grow sustainably over time. The core question is not only whether the system can handle increased traffic today, but whether it can scale predictably without introducing operational instability or uncontrolled costs.

Self-hosted LLM

In a self-hosted setup, scalability and maintainability are tightly coupled to infrastructure design and engineering discipline. Teams control how the system grows, but they also fully own the complexity that comes with it, the same like with latency.

Pros

Self-hosted LLMs provide full control over scaling strategies, including vertical scaling with more powerful hardware and horizontal scaling through additional nodes or GPUs. The inference stack can be optimized using quantization, batching, and continuous batching. This approach enables predictable performance growth and long-term maintainability aligned with workload characteristics.

Cons

Scaling requires significant operational effort, including GPU provisioning, cluster orchestration, load balancing, and autoscaling policy design. Failure recovery, monitoring, and patch management are fully your responsibility. Every model update or infrastructure change introduces integration risk and potential downtime if not carefully managed.

Public API

With public LLM APIs, scalability is largely abstracted away from the application team like in case of latency. The provider handles infrastructure growth and system evolution, allowing teams to focus on product development rather than platform operations.

Pros

Traffic spikes are handled automatically by the provider, with no need to provision hardware or manage clusters. Model upgrades and performance optimizations are delivered transparently, reducing long-term maintenance effort and operational burden.

Cons

Visibility into capacity planning is limited, and vendor-imposed rate limits or throttling can constrain growth. Teams depend on external SLAs, meaning outages or performance degradation are outside their control. At high usage levels, costs scale linearly with traffic, which can make long-term scalability expensive.

Model Quality, Updates & Experimentation

Model quality and the ability to iterate quickly are critical when building competitive AI products. The trade-off here is control versus convenience: owning the model lifecycle versus delegating it to a vendor.

Self-hosted LLM

A self-hosted LLM gives teams the possibilities on model selection and its customization. You can freely switch between checkpoints, experiment with different open-source models, or fine-tune the existing model. Models can be adapted to internal datasets and domain-specific tasks while keeping training data private. There are also no forced upgrades - the model remains stable until you decide to change it, which helps avoid unexpected regressions in systems that depend on consistent behaviour.

Pros

Full control over model choice and upgrades, including the ability to fine-tune and experiment with new architectures on your own schedule. Training data remains private, and model behaviour stays stable unless intentionally changed.

Cons

Teams are fully responsible for model lifecycle management, including updates, security patching, and long-term maintenance. Open-source models may lag behind state-of-the-art commercial systems in reasoning, planning, coding reliability, or enterprise features. Experimentation is also more expensive, as every new idea requires GPUs, infrastructure, and mature MLOps workflows.

Public API

Public LLM APIs provide instant access to state-of-the-art models without the need to manage training, alignment, or infrastructure. Providers handle inference optimization and continuous improvements, allowing teams to benefit from cutting-edge capabilities with minimal effort.

Pros

Immediate access to frontier models, fast experimentation across multiple providers, and automatic improvements in reasoning, safety, and context handling with little engineering overhead.

Cons

Limited control over model updates and behaviour. Silent changes can cause prompt regressions or quality drift. Customization options are often constrained, and teams become dependent on the vendor’s model roadmap.

Vendor Lock-In & Portability

Vendor lock-in is one of the most strategic and often underestimated factors of comparing between a self-hosted LLM and a public API. Lock-in is not inherently bad, but it reduces optionality, which directly impacts negotiation power, architectural freedom, and long-term adaptability of the system.

Self-hosted LLM

With a self-hosted LLM, teams retain full ownership of the model, data layer, and infrastructure. This makes it easier to switch between open-source models using unified runtimes and standardized inference layers. Over time, this approach provides greater architectural flexibility and control over long-term costs.

Pros

Full ownership of the stack, higher portability across models and runtimes, and reduced dependency on a single vendor’s roadmap or pricing decisions. Architectural choices remain under your control as the system evolves.

Cons

Achieving true portability requires deliberate engineering effort. Modular pipelines must be designed and maintained, and switching models may still involve migrating prompts, embeddings, or fine-tuning artefacts.

Public API

Public LLM APIs enable fast onboarding with minimal architectural friction. Teams can integrate advanced models quickly without managing infrastructure, and benefit from a unified ecosystem for deployment, tooling, and monitoring. New features, performance improvements, and model upgrades are delivered automatically, reducing ongoing engineering effort.

Pros

Fast integration, low setup cost, and a cohesive vendor-managed ecosystem. Continuous improvements arrive with little to no operational overhead, allowing teams to focus on product development rather than platform maintenance.

Cons

Lock-in increases over time as prompts, embeddings, fine-tuning workflows, and business logic become tightly coupled to vendor-specific APIs. Migration costs grow accordingly, while pricing changes and feature roadmaps remain outside your control.

Operational Risk & Business Continuity

Operational risk and business continuity determine how resilient your system is when things go wrong - hardware failures, traffic spikes, outages, or external dependencies. The key difference between self-hosted LLMs and public APIs lies in who owns these risks and how much control you have over failure scenarios.

Self-hosted LLM

Teams are responsible for hardware reliability, scaling, patching, backups, and disaster recovery. This provides maximum control but also concentrates operational risk entirely within the organization.

Pros

Full control over uptime strategies, redundancy, failover mechanisms, and recovery procedures. No dependency on third-party availability or policy changes, making it easier to design strict business continuity and disaster recovery plans for mission-critical systems.

Cons

All operational failures directly become your problem. Hardware issues, misconfigurations, or scaling mistakes can lead to downtime or degraded performance. Maintaining high availability requires monitoring, on-call rotations, and regular disaster recovery testing, increasing operational complexity and cost.

Public API

Public LLM APIs shift much of the operational responsibility to the provider. Infrastructure reliability, scaling, and availability are managed under an SLA, reducing internal maintenance effort and operational load.

Pros

Lower operational burden and faster time to reliability. Providers absorb infrastructure failures, capacity planning, and many operational risks, allowing teams to focus on product development rather than uptime management.

Cons

Business continuity becomes partially dependent on an external vendor. Outages, rate limits, throttling, or changes in service terms can disrupt your application without warning. Recovery options are limited to what the provider exposes, requiring fallback strategies and careful monitoring on the client side.

As you can see, choosing between a self-hosted LLM and a public API is ultimately about trade-offs, not absolutes. Each approach offers advantages and constraints across cost, performance, control, and operational complexity, and the right choice depends on your product goals and team maturity. In many cases, a hybrid strategy delivers the best results - combining the speed and simplicity of public APIs with the control and cost efficiency of self-hosted models where it matters most.

Thanks for reading this paper, and I hope this information was helpful to you and you will find the best way to use LLMs! See you soon on new papers in Data Science Factory.