AI Operations
Keep production AI running, measurable and under control. We run the cost tracking, performance tuning, monitoring, and incident response that keep production AI fast, affordable, and reliable for teams running models and agents at scale. So a system that's technically running doesn't quietly become expensive, slow, or wrong.
Running your own model infrastructure and managing GPU costs is a different service. This is about what happens after the system is deployed, not the deployment itself.
A Watched System, Not a Deployed One
Technically running isn't the same as working correctly. We build the cost tracking, quality monitoring, safe rollouts, and incident response that keep a production AI system from quietly becoming expensive, slow, or wrong after launch.
What is AI operations?
AI operations is the ongoing work of monitoring, tuning, and running production AI systems: tracking cost, speed, and quality, managing safe rollouts and backup providers, and responding to incidents. The goal is that a system that's technically running doesn't quietly become expensive, slow, or wrong. You may also see it called LLMOps or model operations.
Tools We Use
Cost and infrastructure control on the stack you already run: Datadog for visibility, Terraform and Pulumi to manage infrastructure through code, Kubernetes and Redis for scale and caching.
Recognize the symptoms
When You Need AI Operations
If two or more of these are already true, this isn't a tuning problem.
- A prompt or model change quietly makes answers worse, and a customer complaint catches it before any dashboard does
- Nobody can trace why the AI gave a specific answer
- Usage costs climb and nobody can tell which model, feature, or customer is driving it
- Response times slowly get worse, with nothing tracking the trend before it becomes a problem
- The same incident gets diagnosed from scratch every time because there's no playbook
Three problems, one engagement.
Expensive, slow, or unreliable AI is usually the same underlying gap: nobody's tuning it after launch.
Lower AI cost
LLM cost optimization and AI FinOps: cost traced by model, provider, agent, and feature, with token use and caching tuned so the bill matches what the system actually needs to do.
Faster responses
Speed tuning: response time and volume tracked against a target, with model choice, caching, and request routing adjusted so responses don't quietly get slower under load.
Managed AI operations
An ongoing managed service: monitoring, incident response, and maintenance run on a set schedule, so a production AI system keeps working correctly without your team watching it full time.
Cost has its own page: AI Cost Optimization.
Uptime doesn't tell you if the answer was right.
- AI Operations covers running AI applications and agents in production after they've been built and launched: safe releases, monitoring, quality checks, cost tracking, provider reliability, incident response, and steady improvement based on what actually happens in production.
- The same input can produce a different output next time, there may not be one correct answer, and a change can quietly improve one thing while making another worse. Standard monitoring tools aren't built to catch that.
- We track every model call, data lookup, and tool use, and break down cost by model and feature so you can see exactly where it's going.
- We add live quality checks and build incident playbooks for the ways AI systems specifically fail, instead of assuming a standard dashboard covers it.
What We Operate
Five parts, run as one loop, organized around what's actually live in production, not which monitoring tool's dashboard it shows up in.
Models
How the model and its provider behave in production: tracking versions, testing backups, handling rate limits, and catching quality drops if a provider quietly changes what a model version points to.
Agents
How agents behave once they're live: whether they call the right tools, when they hand off to a person, how many steps they're allowed to take, and a full record of what they did and why.
AI infrastructure
The health of the systems serving your models: speed, capacity, and how they hold up under real load, coordinated with the team that handles GPU costs.
AI platforms
How the shared platform holds up for every team building on it: whether the standard build path stays healthy, the model catalog stays clean, and shared infrastructure performs well under load.
Production workloads
The full loop for anything live: watching it, checking its quality in real time, tracking its cost, responding to incidents, and feeding what you learn back into your tests.
Model serving and GPU costs are handled by AI Infrastructure. Building test methodology and calibrating how quality gets scored is part of AI Evaluation. Defending against attacks and compliance controls are part of AI Security.
What Running Production AI Needs
Eight parts that run as one loop, not eight separate dashboards that don't talk to each other.
Tracing every request
A record of every model call, tool use, and data lookup, not just a sample of what's happening in production.
Tagging requests
Details attached to nearly every request: which app, environment, customer, feature, model, and version it came from.
Live quality checks
A live checker that scores real traffic for quality, safety, and accuracy, not just whether the system is up.
Cost tracking
Cost broken down by model, version, agent, tool, user, and customer, so you know exactly what's driving the bill.
Safe rollout
A safe way to roll out every prompt, model, or agent change: test it quietly first, or on a small share of traffic, before it reaches everyone.
Backup provider
A backup model or provider, tested for quality, ready to switch to if the main one fails.
Incident playbooks
Step-by-step playbooks for AI-specific incidents: a provider going down, rate limits piling up, a spike in made-up answers, or a spike in cost.
Learning from failures
A process that turns confirmed production failures into new test cases, so the same mistake gets caught next time.
How Cost, Quality, and Reliability Are Built In
Not bolted on after an incident.
Quiet test, then small-share rollout
A change runs silently next to the current version, or goes to a small share of live traffic first, so a regression shows up before everyone sees it.
Automatic switch to a backup provider
Requests move to a tested backup automatically once a provider's error rate or response time crosses a limit, instead of every request failing until someone notices.
Cost tags on every request
Every request is tagged with model, feature, and customer when it's made, so a cost spike can be diagnosed instead of just noticed on the invoice.
Reusing answers to similar questions
Responses reused for questions that mean the same thing, not just ones that match word for word, cutting cost and wait time on repeated question patterns.
What You Receive
AI Operations vs AI Optimization
In practice, they're the same engagement. You can't improve a production AI system's cost or speed without running the operations that measure it, and running operations without acting on what you find isn't optimization. Optimization is the result: a system that's cheaper, faster, and more reliable than before. Operations is how we keep delivering that result after the first round of changes ships.
AI Operations vs AI Evaluation
AI Evaluation defines what "good enough" means before something ships. It builds the test sets and pass or fail checks that block a bad release. AI Operations runs the system after that: monitoring, cost tracking, backup providers, and incident response in live production. Operations tells you something's wrong right now. Evaluation is why you had something to compare it against.
From our experts.
Writing on production cost, observability, and AI operations.
Our AI Operations Process
The same nine-phase process we use everywhere, with a checkpoint at every step. Watching and improving the system doesn't stop at handover: managed clients get a monthly review of incidents, cost, and quality.
Related Engineering Services
Common questions.
Bring us the system nobody's watching closely enough.
Whether it's a production AI feature nobody's tracking closely, or one that's already had an incident nobody had a playbook for, we'll look at where it stands before recommending anything.
No hype · No forced roadmap · Just a clear view of what the system needs next