The visible costs of building agents (API fees, frameworks) are dwarfed by the hidden costs: state management, auth, observability, and the infrastructure to run reliably in production. See what the runtime handles for you →
Open source frameworks are free. The LLM APIs they call charge per token. What nobody warns you about is the third cost category - the infrastructure needed to run agents reliably in production. This infrastructure cost is invisible until you start building, then dominates your engineering budget for months. Understanding where the money actually goes helps you make informed decisions about building versus buying agent capabilities.
The Visible and Hidden Costs
When teams evaluate agent projects, they typically account for the obvious costs. Model API fees are straightforward - you pay per token based on published pricing. Framework licenses are usually free for open source options. Developer time to write agent logic feels manageable since prototypes come together quickly.
The hidden costs live in the gap between prototype and production. A prototype runs on a developer laptop with careful supervision. Production means running without supervision, handling failures gracefully, maintaining visibility into what agents do, managing authentication for integrations, and scaling to serve real traffic. Each of these requirements translates into infrastructure that someone must build, deploy, and maintain.
These hidden costs catch teams off guard because they do not show up in the prototype phase. Everything works when you are running locally, watching the agent, and manually restarting when things go wrong. The moment you need unattended operation, the missing infrastructure becomes painfully apparent.
Breaking Down the Build Cost
If you choose to build agent infrastructure yourself, here is what you are actually committing to.
State persistence is the first requirement. Agents need to maintain conversation history and working memory across requests and restarts. Building this means designing a state schema, choosing a database, implementing serialization, handling migrations when the schema changes, and managing cleanup of abandoned sessions. Expect one to two weeks of engineering time initially, plus several days per year maintaining and evolving the system.
Failure handling comes next. In production, network calls fail, APIs rate-limit, model providers have outages, and processes crash. Robust agents need retry logic with exponential backoff, circuit breakers to avoid hammering failing services, fallback behaviors when tools are unavailable, and graceful degradation rather than complete failure. This typically requires two to three days to implement basic handling, plus ongoing maintenance as you encounter new failure modes.
Observability becomes urgent after your first production incident where you cannot determine what went wrong. You need to trace the complete reasoning chain from user request through every tool call to final response. This means integrating tracing tools, instrumenting your code at every decision point, building or configuring dashboards, and setting up alerting. Expect one to two weeks initially if using existing observability platforms, more if building custom tooling. Ongoing costs include the platform subscription plus several days per year maintaining dashboards and alerts.
Authentication management is surprisingly complex. If your agent integrates with external services on behalf of users - sending emails through their accounts, accessing their calendars, posting to their social media - you need OAuth flows, secure token storage, automatic token refresh, and handling of expired or revoked credentials. Each integration takes one to two weeks. A typical agent might need five to ten integrations.
Execution infrastructure must support agent workload patterns. Agents are neither pure request-response handlers nor batch jobs. They make bursts of API calls punctuated by waiting. They might run for seconds or hours. Traditional infrastructure assumptions do not fit well. You need to design execution patterns, handle scaling, manage resource allocation, and deal with deployment of running agents. Two to four weeks initially, with ongoing operational burden.
Add these up and you are looking at two to three months of engineering time before your agent is production-ready, plus three to four weeks of annual maintenance. For a typical engineering team, this represents substantial opportunity cost - time spent on infrastructure is time not spent on agent capabilities.
inference.sh eliminates this build cost. Durable execution, managed auth, and observability are included in the runtime. You pay for agent work, not infrastructure engineering. Start building today →
The Ongoing Operational Burden
Initial build costs are only part of the story. Infrastructure requires ongoing attention.
Security patches arrive regularly. Each component in your infrastructure - databases, message queues, containers, orchestration tools - has security advisories. Someone must track these, evaluate their relevance, test patches, and deploy updates.
Scaling challenges emerge as usage grows. What worked for ten users might not work for ten thousand. Databases need tuning. Queues need capacity planning. Execution infrastructure needs right-sizing. These are not one-time decisions but ongoing adjustments.
Integration maintenance accumulates. OAuth providers change their APIs. Third-party services deprecate endpoints. New authentication requirements appear. Each integration is a liability that occasionally needs attention.
Debugging infrastructure issues competes with debugging agent behavior. When something breaks, determining whether the problem is in your agent logic or your infrastructure requires investigating both. The infrastructure complexity adds to your debugging surface area.
Teams that build their own infrastructure often find that maintenance consumes more ongoing effort than they anticipated. What seemed like a one-time build becomes a persistent operational burden.
The Buy Alternative
The alternative to building is using a platform that provides agent infrastructure as a service. Instead of building state persistence, you use a runtime where state is managed for you. Instead of building observability, you use a platform with built-in tracing. Instead of managing authentication, you connect integrations through the platform's credential management.
The trade-off is straightforward. Building gives you maximum control at the cost of engineering time and ongoing maintenance. Using a platform trades some flexibility for dramatically faster time to production and lower operational burden. Most teams find that their differentiation lies in what their agents do, not in the infrastructure running them.
The calculation depends on your specific situation. If you have unusual requirements that no platform satisfies, building makes sense. If you have an experienced platform team with available capacity, building may be practical. If control over every infrastructure detail is more valuable than engineering velocity, building is the right choice.
For most teams, the math favors using a platform. The infrastructure requirements for production agents are well understood. Building standard infrastructure from scratch provides little competitive advantage while consuming significant resources.
Pay-Per-Execution Economics
Traditional deployment models charge for reserved capacity. You provision servers, containers, or functions and pay for them whether they are busy or idle. For always-active web applications, this makes sense. For agent workloads, it is often wasteful.
Agent activity is typically bursty. A user sends a message, the agent does work for seconds to minutes, then sits idle until the next message. If you have provisioned capacity to handle peak load, you are paying for that capacity during all the idle time. If you provision for average load, you cannot handle spikes.
Pay-per-execution pricing aligns costs with actual work. You pay when agents run, not when they wait. Idle time costs nothing. Spike handling is automatic because you are not limited by pre-provisioned capacity. This model particularly benefits agents with unpredictable or irregular usage patterns.
The economic difference is significant for many workloads. Consider an agent that handles requests during business hours with occasional after-hours activity. Traditional deployment means paying for 24/7 capacity to handle 8 hours of actual usage. Pay-per-execution means paying for the 8 hours.
Making the Decision
The build-versus-buy decision for agent infrastructure depends on several factors.
Engineering capacity matters most. Building infrastructure requires engineers who can do it well and time not spent on other priorities. If your engineering team is small or fully committed to product work, infrastructure building competes directly with feature development.
Requirement uniqueness affects whether platforms can serve you. Standard requirements - state persistence, observability, auth management, reliable execution - are well served by platforms. Unusual requirements might not be.
Time constraints influence the practical choice. Building takes months. Platforms take days to start using. If you need to ship agent capabilities quickly, the platform path is more realistic.
Operational appetite determines sustainability. Building means committing to ongoing operations - security, scaling, maintenance. Some teams enjoy this work. Others would rather focus elsewhere.
The honest assessment for most teams is that agent infrastructure is not differentiating. Users care about what the agent does, not what state storage backend it uses. Investing heavily in infrastructure that provides no competitive advantage is hard to justify when platforms offer equivalent capabilities.
Calculating Your True Cost
If you are evaluating the build path, do the math realistically.
For initial development, estimate the time for each component and multiply by your fully loaded engineering cost. Do not forget the learning curve - first implementations of complex infrastructure typically take longer than estimates suggest.
For ongoing maintenance, budget three to four weeks of engineering time annually per major infrastructure component. Security updates, scaling adjustments, and bug fixes are not optional.
For opportunity cost, consider what else your engineers could build in those months. Features, improvements, and new capabilities that directly serve users often have higher value than infrastructure that merely enables operation.
Compare this total to the cost of using a platform. Platform pricing is typically predictable - some combination of subscription fees and usage charges. The comparison should include not just dollar costs but time to production and ongoing operational burden.
For teams that want to focus engineering effort on agent capabilities rather than infrastructure, inference.sh provides the runtime layer with durable execution, built-in observability, managed authentication, and pay-per-execution pricing. The infrastructure is handled so you can concentrate on making agents that create value.
The real cost of agent infrastructure is not the framework download or the API key. It is the months of engineering and years of maintenance that turn a prototype into a production system. Understanding this cost upfront leads to better decisions about where to invest your resources.
FAQ
How do I justify the cost of a managed platform to stakeholders who see open source as free?
Open source frameworks are free to download but not free to operate. Frame the comparison around total cost of ownership rather than sticker price. Calculate the engineering time for building infrastructure - typically two to three months at your fully loaded engineering rate. Add ongoing maintenance - three to four weeks per year. Include opportunity cost - what features could be built instead. Compare this total to platform pricing over the same period. For most teams, the platform is cheaper even before accounting for faster time to market and reduced operational risk. The open source approach is only cheaper if engineering time has no cost, which is never true.
What are the biggest hidden costs teams encounter when building agent infrastructure?
Authentication management is consistently underestimated. Each OAuth integration involves understanding the provider's flow, implementing secure token storage, handling refresh cycles, and dealing with edge cases like revoked permissions. Teams typically estimate one to two days and discover it takes one to two weeks per integration. Observability is another surprise - instrumenting code is straightforward, but building useful dashboards and alerts requires understanding failure modes you have not encountered yet. Finally, the operational burden accumulates. Each component needs security updates, performance tuning, and occasional debugging. Teams budget for building but underestimate maintaining.
At what scale does building custom infrastructure start to make sense?
Scale alone does not justify building. The question is whether your requirements differ from what platforms provide. If you have genuinely unusual needs - specialized compliance requirements, unique execution patterns, or integration with proprietary internal systems - custom infrastructure might be necessary regardless of scale. If your needs are standard, platforms handle scale through their own infrastructure investment. The scale question is really about amortizing build costs: can you spread the infrastructure investment across enough value that it becomes efficient? For most teams, the answer is that platform pricing scales better because platforms spread infrastructure costs across many customers. Very large deployments might achieve similar efficiency through custom builds, but even large organizations increasingly favor managed services for standard capabilities.