Managed AI & Support: Reliable AI Operations at Scale
AI that ships is not AI that lasts. Models drift, token bills creep, hallucinations slip through, and quality erodes quietly until users notice. Opsio runs your AI in production so it stays accurate, governed, and cost-efficient: 24/7 monitoring, drift and quality evaluation, retraining, and AI analytics under firm SLAs.
Trusted by 100+ organisations across 6 countries
24/7
Follow-the-sun NOC coverage
<15min
Critical incident response target
99.9%
Operational uptime SLA
50+
Cloud & AI engineers
What's Included
Managed AI & Support covers the full operational lifecycle of AI in production, not a single tool or task. We combine LLMOps and MLOps engineering with round-the-clock monitoring, structured evaluation, cost discipline, and analytics into one accountable service backed by SLAs. Whether your stack runs on Amazon Bedrock, Azure OpenAI, Google Vertex AI, open-weight models on your own GPUs, or a mix, the operating principles are the same: make behaviour observable, make regressions catchable, make spend predictable, and make improvement continuous. The six capabilities below are the pillars of that service. Each is delivered by named engineers in our follow-the-sun model across Sweden and India, instrumented with industry-standard tooling, and reported back to you transparently so you always know how your AI is performing and what we are doing about it.
24/7 AI Monitoring & Support
Our follow-the-sun NOC watches your AI services around the clock, alerting on latency, errors, quality drops, and cost spikes. Critical incidents get a sub-15-minute response with defined escalation paths and on-call engineers who know your stack.
Model Drift & Quality Evaluation
We continuously score live outputs for accuracy, groundedness, and distribution shift using automated evaluation suites and LLM-as-judge methods. Drift is detected as a measurable signal, triggering review or retraining before users feel the degradation.
LLMOps & MLOps Engineering
We own the operational pipeline: versioned prompts and models, CI/CD for AI, registries, automated regression gates, and reproducible deployments. Every change is tested and traceable, so your AI evolves safely rather than through risky manual edits.
Cost Optimization (Token & GPU)
We profile token and GPU spend down to the request, then reduce it through model right-sizing, caching, prompt compression, batching, and tiered routing. Unit economics become a managed metric, keeping your AI affordable as usage grows.
Retraining & Lifecycle Management
We operate scheduled and trigger-based retraining pipelines, manage data versioning and labelling workflows, and validate each new model against your evaluation suite before promotion. Models stay current without manual firefighting or unplanned downtime.
AI Analytics & Reporting
We turn operational telemetry into dashboards and reports on model performance, usage, cost trends, and business outcomes. Leadership gets clear visibility into what AI delivers, and engineers get the signals they need to prioritize improvements.
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- Certified cloud engineers
Opsio's focus on security in the architecture setup is crucial for us. By blending innovation, agility, and a stable managed cloud service, they provided us with the foundation we needed to further develop our business. We are grateful for our IT partner, Opsio.
Jenny Boman
CIO · Opus Bilprovning
Production AI, operated like critical infrastructure
Most organizations succeed at building an AI proof of concept and then struggle to keep it healthy in production. A demo only has to work once; a production system has to work every minute, for every user, as data shifts and traffic spikes. Opsio's Managed AI & Support exists for that second, harder phase. We take ownership of your deployed models and AI applications, instrument them for observability, and run them against measurable service levels. The result is the difference between an impressive prototype and an AI capability your business can actually depend on, quarter after quarter, without your internal team being paged at 3 a.m. Running AI is genuinely different from running traditional software. The code can be unchanged while behaviour silently degrades because the world the model was trained on has moved. A pricing model trained last spring meets a new product line; a support assistant meets a slang term it has never seen; a retrieval system meets a document set that doubled overnight. None of these throw a stack trace. Opsio closes this gap by treating model behaviour itself as a monitored signal, not just CPU and latency. We watch accuracy, groundedness, and output distribution alongside conventional infrastructure metrics, so degradation is caught as data, not as a customer complaint.
Cost is the other quiet failure mode. Token consumption and GPU hours scale with usage in ways that are easy to ignore until the invoice arrives. Inefficient prompts, oversized context windows, retries, and the wrong model tier for the job can multiply spend several times over with no benefit to the user. Opsio continuously profiles where your AI budget actually goes and engineers it down: right-sizing models, caching, prompt compression, batching, and routing simpler requests to cheaper tiers. We treat unit economics as an operational metric to be managed, the same way we manage uptime, so your AI stays affordable as it scales rather than becoming a line item finance starts to question.
Quality and safety are operational disciplines, not one-time checks. Hallucinations, prompt injection, data leakage, and off-policy outputs are ongoing risks that need ongoing controls. Opsio builds evaluation suites, guardrails, and automated regression tests into your pipeline so every model or prompt change is scored before it reaches users, and so live outputs are continuously sampled and judged. When a regression appears, we have rollback paths and incident procedures ready rather than improvised. This is how AI earns the trust of risk, legal, and security stakeholders, and how it keeps that trust as the system evolves over months and years of real use.
Finally, operating AI well produces a stream of insight that most teams leave on the floor. Every request, evaluation, and cost signal is data about how your AI and your business are actually performing. Opsio turns that telemetry into AI analytics: dashboards and reports covering model performance, usage patterns, cost trends, and business outcomes, so leadership can see what AI is doing and decide where to invest next. Managed AI & Support is therefore not just keeping the lights on. It is a continuous-improvement loop in which operation feeds measurement, measurement feeds optimization, and your AI gets more reliable and more valuable over time. Featured reading from our knowledge base: Enhance Engineering Operations with Reliable IT Support Solutions, We Offer Reliable Emergency IT Support Near Me, 24/7, and Managed Support Services: Your Questions Answered. Related Opsio services: MLOps Services — From Notebook to Production.
How Opsio Compares
| Capability | In-House Ad Hoc | Generic Cloud Support | Opsio Managed AI |
|---|---|---|---|
| AI quality & drift monitoring | Manual, sporadic | Infrastructure only | Continuous, automated |
| 24/7 coverage | Business hours | Ticket queue | Follow-the-sun, real engineers |
| Token & GPU cost optimization | Reactive | Not included | Actively managed metric |
| Retraining & lifecycle | When someone remembers | Out of scope | Scheduled & trigger-based |
| Incident response SLA | Best effort | Tier-dependent | Sub-15-min critical target |
| AI analytics & reporting | Ad hoc spreadsheets | Generic dashboards | Model, cost & business insight |
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What You Get
Pricing & Investment Tiers
Transparent pricing. No hidden fees. Scope-based quotes.
Essential
from approx. EUR 3,000/month
24/7 monitoring, incident response, and support for a small number of production models. Illustrative; varies by scope.
Professional
approx. EUR 8,000-15,000/month
Adds drift and quality evaluation, active cost optimization, guardrails, and monthly AI analytics across multiple models. Illustrative; varies by scope.
Enterprise
custom retainer
Full LLMOps/MLOps operation, strict SLAs, dedicated on-call engineers, retraining pipelines, and executive reporting at scale. Illustrative; varies by scope.
Transparent pricing. No hidden fees. Scope-based quotes.
Questions about pricing? Let's discuss your specific requirements.
Why Choose Opsio for Cloud Services
Plenty of vendors will build you a model. Far fewer will stand behind it in production, every hour, with their name on the SLA. Opsio is a cloud and AI operations partner first: we have run mission-critical workloads on AWS, Azure, and Google Cloud for years, and Managed AI & Support extends that same discipline to machine learning and generative AI. Our follow-the-sun delivery across Karlstad, Sweden and our ISO 27001-certified Bangalore centre means genuine 24/7 coverage by engineers, not just an after-hours answering service. We are an AWS Advanced Tier Services Partner with the AWS Migration & Modernization Competency, a Microsoft Partner, and a Google Cloud Partner, so multi-cloud AI is our home ground rather than a stretch. Here is what sets the service apart.
Genuine 24/7 Coverage
Follow-the-sun engineering across Sweden and India means real people watch your AI at every hour, not an escalation queue that wakes someone up late.
Multi-Cloud AI Expertise
We operate AI natively on Amazon Bedrock, Azure OpenAI, and Google Vertex AI, so we fit your stack instead of forcing a single-vendor path.
Operations-First DNA
We come from running production infrastructure under SLAs, so reliability, incident response, and observability are habits, not afterthoughts bolted on.
Security & Compliance Built In
Our ISO 27001-certified delivery centre and security-first architecture mean guardrails, access control, and auditability are part of the operating model.
Transparent SLAs & Reporting
Defined response targets, uptime commitments, and regular performance reports keep the relationship measurable. You always know how your AI is doing.
Cost Accountability
We treat token and GPU spend as a metric we are responsible for, actively engineering it down rather than passing through whatever the bill says.
Not sure yet? Start with a pilot.
Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.
Our 4-Phase Delivery Process
Our onboarding is built to take ownership without disrupting what already works. We start by understanding your AI systems, their business purpose, and their current health, then instrument them for observability before changing anything. Only once we can see the system clearly do we begin to operate and optimize it. From there the engagement becomes a steady loop of running, measuring, and improving. The four stages below describe how a typical engagement moves from handover to a continuously improving operation, with indicative timelines for each.
Onboarding & Baseline
We map your models, data flows, and dependencies, capture a performance and cost baseline, and agree SLAs and runbooks. This establishes the ground truth everything else is measured against. Timeline: weeks 1-3.
Instrument & Observe
We deploy monitoring, tracing, and evaluation tooling across your AI stack, wiring up quality, drift, and cost signals alongside infrastructure metrics. Timeline: weeks 2-5, overlapping onboarding.
Operate & Optimize
We run the system under SLA: 24/7 monitoring, incident response, cost optimization, guardrail tuning, and scheduled retraining. The system gets steadily more reliable and cheaper to run. Timeline: ongoing from week 4.
Analytics & Improve
We deliver regular AI analytics and performance reviews, surface improvement opportunities, and feed them back into the optimization loop with you. Timeline: monthly cadence, continuous.
Key Takeaways
- 24/7 AI Monitoring & Support
- Model Drift & Quality Evaluation
- LLMOps & MLOps Engineering
- Cost Optimization (Token & GPU)
- Retraining & Lifecycle Management
Industries Served by Opsio
Managed AI & Support applies wherever AI has moved from experiment to dependency, but the operational priorities differ by sector. Regulated industries weight governance and auditability heavily; high-volume consumer businesses care most about cost-per-request and latency; others need rock-solid accuracy on narrow tasks. We tailor monitoring thresholds, guardrails, evaluation criteria, and reporting to each context. These four sectors are where we see the strongest demand for disciplined AI operations.
Financial Services
Banks, insurers, and fintechs need AI that is explainable, auditable, and tightly governed. We operate models with strict guardrails, evaluation logging, and reporting that satisfies risk and compliance teams while keeping fraud, underwriting, and service models accurate.
Healthcare & Life Sciences
Clinical and research AI demands high accuracy and rigorous safety controls. We run evaluation suites that catch hallucination and drift early, with auditable change histories and access controls suited to sensitive data and regulated workflows.
Retail & E-commerce
Recommendation, search, and support AI run at high volume where cost-per-request and latency matter as much as quality. We optimize token and GPU spend aggressively while keeping conversion-critical models accurate through seasonal demand swings.
Manufacturing & Industrial
Predictive maintenance, quality inspection, and operations AI must stay reliable against shifting sensor and process data. We monitor for drift continuously and retrain on schedule so models keep pace with changing plant conditions.
Managed AI & Support: Reliable AI Operations at Scale — FAQ
What is Managed AI / LLMOps?
Managed AI is an ongoing service in which a partner runs your AI systems in production on your behalf, rather than just building them. LLMOps (and MLOps for traditional machine learning) is the operational discipline that makes this possible: versioning prompts and models, monitoring quality and cost, evaluating outputs, managing retraining, and responding to incidents. Where DevOps keeps software running, LLMOps keeps model behaviour reliable, because AI can degrade even when its code never changes. Opsio's Managed AI & Support bundles these practices into one accountable service, covering monitoring, drift evaluation, guardrails, cost optimization, retraining, and analytics, all delivered under defined SLAs by named engineers.
How much does Managed AI & Support cost?
Pricing is a monthly retainer that varies by scope, because the right number depends on how many models you run, your traffic volume, the SLA tier you need, and whether GPU infrastructure is involved. As illustrative guidance, lighter engagements covering monitoring and support for a small number of models typically start in the lower thousands of euros per month, while comprehensive operations spanning many models, strict SLAs, and active cost and retraining work run higher. Importantly, the service usually pays for part of itself through cost optimization: by right-sizing models, cutting wasteful token and GPU usage, and routing efficiently, we often reduce AI spend meaningfully. We scope every engagement individually and give a clear, fixed monthly figure before you commit.
What SLAs do you offer?
We offer tiered SLAs aligned to how critical your AI is to the business. Our standard operational commitment targets 99.9% uptime, with a sub-15-minute response target for critical incidents and longer windows for lower-severity issues. SLAs cover response and restoration times, monitoring coverage, and reporting cadence, and are backed by our 24/7 follow-the-sun NOC across Sweden and India so coverage is genuine at every hour. Higher tiers add tighter response targets, dedicated on-call engineers, and more frequent reviews. Every SLA is documented in the engagement agreement with clear severity definitions and escalation paths, so expectations are explicit rather than implied, and performance against them is reported to you regularly.
How do you detect model drift?
We treat model behaviour as a monitored signal, not a black box. In practice we track several things continuously: the statistical distribution of inputs and outputs, automated quality scores on sampled live outputs (often using evaluation suites and LLM-as-judge methods), and, where ground truth becomes available, accuracy against it. When any of these moves outside an agreed threshold, an alert fires. For generative systems we also score groundedness and hallucination rates against source data. This catches the silent degradation that traditional infrastructure monitoring misses entirely, because a model can return fast, error-free responses that are quietly becoming less accurate. Detection then triggers a defined response: review, guardrail adjustment, or retraining, depending on the cause and severity.
What AI analytics do I get?
You get dashboards and regular reports across four areas. Model performance: accuracy, quality scores, drift indicators, and evaluation results over time. Usage analytics: request volumes, patterns, top use cases, and adoption trends. Cost analytics: token and GPU spend broken down by model, feature, and team, with trend lines and optimization impact. Business-performance insight: where we can connect AI activity to outcomes you care about, such as resolution rates or conversion. The aim is to give leadership a clear view of what AI is actually delivering and give engineers the signals they need to prioritize. Reports come on a monthly cadence by default, with live dashboards available continuously and deeper reviews at agreed intervals.
How often do you retrain models?
There is no single right cadence; it depends on how fast your data and environment change. We operate two complementary approaches. Scheduled retraining runs on a fixed cycle, monthly or quarterly being common, to keep models current with recent data. Trigger-based retraining fires when our drift monitoring detects that quality has moved beyond an agreed threshold, so the model is refreshed when it actually needs to be rather than on an arbitrary calendar. Every retrained model is validated against your evaluation suite and compared to the incumbent before promotion, and we keep rollback paths ready. For some stable models, infrequent retraining is correct; for fast-moving ones, more frequent cycles are warranted. We set the policy with you during onboarding.
Which clouds and AI platforms do you support?
We are multi-cloud by design and operate AI natively across the three major platforms: Amazon Bedrock on AWS, Azure OpenAI Service on Microsoft Azure, and Google Vertex AI on Google Cloud. We also operate open-weight and custom models running on your own GPU infrastructure or managed endpoints. For observability and operations we use industry-standard tooling such as Datadog for monitoring, MLflow for experiment tracking and model registry, and LangSmith for LLM tracing and evaluation, integrating with whatever you already run rather than forcing a rip-and-replace. As an AWS Advanced Tier Services Partner, Microsoft Partner, and Google Cloud Partner, we can operate your AI wherever it lives, including hybrid setups that span more than one cloud.
How do you keep AI outputs safe and on-policy?
Safety is handled as a continuous operational control, not a one-off review. We build guardrails into the request path, including input and output filtering, prompt-injection defenses, and policy checks, and we sample live outputs continuously to score them against your acceptable-use and quality criteria. Every prompt or model change passes through an automated evaluation and regression gate before it reaches users, so quality cannot quietly regress through an edit. Access controls, logging, and auditability come from our ISO 27001-certified operating model, giving risk, legal, and security stakeholders the evidence they need. When something does go wrong, we have rollback paths and incident procedures ready, so issues are contained quickly rather than handled by improvisation under pressure.
Still have questions? Our team is ready to help.
Keep your production AI reliable, governed, and cost-efficient
Talk to Opsio about taking your deployed models under 24/7 managed operations with drift evaluation, cost optimization, retraining, and AI analytics under firm SLAs.
Managed AI & Support: Reliable AI Operations at Scale
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