What Is AI Inference? Definition, Types, and Examples

Training builds the model, but inference puts it to work. This article explains what AI inference is, how it differs from training, the hardware and platforms that power it, and the optimization and governance strategies teams need when running models in production.

Key takeaways: AI inference

If AI training is the "learn it" phase, AI inference is the "use it" phase. These are the points worth remembering before getting into the weeds.

• Definition: AI inference is the production phase where trained models process new inputs and generate predictions, classifications, recommendations, or generated content.
• Distinction: Inference is separate from training. Training builds the model, inference puts it to work.
• Placement: This process happens after model development, integrating directly into applications, APIs, dashboards, AI agents, or automated workflows.
• Cost reality: Inference has ongoing compute costs, and at scale those costs often exceed the one-time cost of training. Gartner projects inference-focused Infrastructure as a Service (IaaS) spending alone will reach $20.6 billion in 2026.
• Operational focus: As deployments grow, teams focus on inference optimization (quantization, caching, batching) and on governance (guardrails, access controls, audit trails, and human-in-the-loop review) so outputs can be tested and trusted.

What is AI inference?

AI inference takes a trained machine learning model and uses it to generate outputs from new input data. Those outputs might be predictions, classifications, recommendations, or generated text and images. This is where a model actually does the job it was designed to do.

Training teaches the model what to look for. Inference applies that knowledge. Input data enters the model, passes through the network's layers (called the forward pass), and produces an output based on patterns learned during training.

You'll hear different terms for this same process depending on who you're talking to. Data teams often say model serving, prediction, or scoring. They all mean inference.

Enterprise deployments tend to include the "last mile" work around the model call: selecting which model handles which task (a proprietary model vs a third-party model vs a custom large language model), orchestrating the request, and applying guardrails so the output is safe and usable before it hits a dashboard or triggers automation.

For generative AI, the process splits into two phases. The prefill phase processes your entire input prompt at once. Then comes the decode phase, generating output tokens one at a time. A mechanism called the key-value (KV) cache stores intermediate computations so the system doesn't repeat work during decoding. That's why the first word of a chatbot response takes longer to appear than the words that follow.

AI inference vs training

Training happens once (or periodically). Inference happens continuously in production. This difference shapes everything.

Here's what surprises most teams: inference costs almost always exceed training costs over time. Deloitte predicts inference workloads will account for roughly two-thirds of all AI compute in 2026, up from one-third in 2023. Budget planning for AI can't stop at the training phase. Production serving costs need their own line item.

A model that costs $5,000 to train might cost $50,000 a month to serve at high volume. The math changes fast once you're handling thousands of requests per day.

Fine-tuning sits somewhere in the middle. It's a form of training that adapts a pre-trained model, but the resulting model still needs dedicated inference infrastructure to serve.

How AI inference works

Someone asks a chatbot a question, submits an image for classification, or requests a product recommendation. The system runs inference. Here's what happens.

The inference pipeline moves through four stages:

• Input preprocessing: Raw text, images, or structured data convert into the format the model expects. For example, tokenization for text and feature encoding for tabular data.
• Forward pass: The preprocessed input moves through the model's layers, with each layer applying learned weights to transform the data.
• Output generation: The final layer produces raw outputs, which might be probability scores for classification or token probabilities for text generation.
• Post-processing: Raw outputs convert back into usable results. Selecting the highest-probability class, sampling from a distribution, applying business rules, and (in many enterprises) running policy checks, redaction, or human-in-the-loop validation before the output is saved, shown to people, or used to automate a decision.

For large language models, the prefill phase processes the entire input prompt in parallel to build the KV cache. The decode phase then generates tokens one at a time, each new token requiring a forward pass but reusing cached computations from previous tokens.

Longer prompts increase initial latency. Longer outputs increase total generation time. Memory bandwidth often matters more than raw processing power, but it's easy to miss in day-to-day tuning work.

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Types of AI inference

How you deploy inference depends on your latency requirements, data volume, and where your data lives.

Real-time inference

Any scenario where someone is waiting for an immediate response requires real-time inference, think chatbots, search ranking, fraud detection, and recommendation engines.

Speed is the operational priority. Latency targets are typically set at the 95th percentile, meaning 95 percent of requests must complete under a threshold such as 200 milliseconds. You need autoscaling to handle traffic bursts, load balancing across endpoints, and timeout patterns for graceful degradation.

In enterprise settings, real-time inference also pulls in governance requirements. If an output influences a credit decision, routes a support case, or triggers an alert, teams often need role-based access controls (RBAC), logging and tracing, and an audit trail that records the model, version, and input data used for the result.

Real-time inference optimizes for latency at the cost of efficiency. You're often paying for idle capacity just to handle peak loads. Provisioning for average traffic instead of peak traffic leads to degraded responses exactly when demand spikes.

Batch inference

When results can wait hours and volume is high, batch inference makes more sense. Nightly customer churn scoring, weekly content classification, bulk document processing.

Jobs run during off-peak hours or trigger when data pipelines complete. The pipeline writes results to a data warehouse for downstream consumption. Teams often run these workloads on spot instances to cut costs significantly.

Batch inference sacrifices responsiveness for efficiency. If your use case can tolerate delay, batch processing typically costs a fraction of equivalent real-time capacity. That's it. That's the calculation.

Edge inference

Edge inference becomes necessary when data can't leave a device (privacy requirements), when connectivity is unreliable (field operations), or when latency must be minimal (autonomous systems).

Running models on edge devices requires compression techniques. Quantization reduces precision from 32-bit to 8-bit. Distillation trains smaller models to mimic larger ones. Pruning removes unnecessary weights. The models run on specialized runtimes like TensorFlow Lite or Core ML.

Edge inference trades model capability for deployment flexibility. A quantized model on a phone will underperform the full model in the cloud, but it works offline and keeps data local. Teams sometimes apply aggressive quantization without validating accuracy for their use case, then discover in production that the compressed model fails on the edge cases that matter to the business.

AI inference hardware and accelerators

Raw compute power is rarely the bottleneck for inference. Memory bandwidth usually limits throughput. This is especially true for large language models where the KV cache dominates memory usage.

Different accelerator types serve different needs:

• GPUs: General-purpose, widely supported, strong ecosystem. Best for variable workloads and flexibility across model types. Higher cost per inference than specialized hardware.
• Tensor Processing Units (TPUs): Optimized for transformer architectures. Strong for high-volume, consistent workloads on Google Cloud. Less portable than GPU-based solutions.
• Inference-optimized chips: Focused on cost efficiency (AWS Inferentia, for example). Significant savings for supported architectures, but limited to specific cloud environments.
• Neural processing units (NPUs): Found in mobile devices and edge hardware. Optimized for on-device inference with power efficiency. Limited to smaller models.

GPUs aren't always necessary. For smaller models or lower-volume inference, CPU-based inference may be more cost-effective and simpler to operate.

AI inference platforms and how to evaluate them

Whether you're deploying a custom model or serving a fine-tuned open-source model, you need infrastructure to handle inference at scale.

When evaluating an AI inference platform, these criteria matter most:

1. Latency guarantees: Does the platform provide service-level agreements (SLAs) for 95th percentile latency? Can it meet your response time requirements under load?
2. Autoscaling behavior: How quickly does the platform scale during traffic spikes? What's the cold start latency for new instances?
3. Batching and optimization: Does the platform support dynamic batching or continuous batching to improve throughput?
4. Model format support: Can you deploy models in portable formats like Open Neural Network Exchange (ONNX), or are you locked into proprietary formats?
5. Observability: What metrics, logging, and tracing are available? Can you integrate with your existing monitoring stack?
6. Cost model: Per-request, per-token, or per-hour? How does cost scale with volume?
7. Governance: Role-based access controls, audit logging, data residency options, and practical guardrails like human-in-the-loop checkpoints for high-stakes outputs.

The landscape includes managed cloud services, open-source model servers like vLLM and NVIDIA Triton, and inference-as-a-service providers. Self-managed inference offers maximum control and potentially lower costs at scale, but requires operational expertise. Managed services reduce burden but may limit optimization options.

For teams trying to reduce custom integration cycles, another make-or-break question is how the platform connects inference to governed enterprise data. If every model needs a new hand-built connector or one-off pipeline, experimentation slows down and production risk climbs.

Per-token pricing models often look cheap during prototyping but become expensive in production. The Stanford HAI 2025 AI Index found per-token costs dropped roughly 280-fold between 2022 and 2024, yet total inference spending continues to climb as deployment volume scales. That paradox highlights why cost planning requires volume projections, not just unit economics.

AI inference use cases and examples

Inference powers every AI feature people interact with.

• Conversational AI: Real-time inference with streaming token output. Models process prompts and generate responses token-by-token. Key metrics include time to first token and tokens per second.
• Recommendation engines: Real-time inference triggered by page loads or cart updates. Models score items against user context and return ranked results. Latency targets typically sit under 100 milliseconds.
• Fraud detection: Real-time inference on transaction data with strict latency requirements. Decisions must complete before transaction approval.
• Document processing: Batch inference for high-volume workloads. Models classify, extract entities, or summarize documents in bulk. Cost per document matters more than speed.
• Predictive analytics: Batch inference for periodic predictions like demand forecasting or lead scoring. Results feed into dashboards and downstream automation.

In a lot of departments, inference shows up in less glamorous (but very ROI-friendly) places too. Invoice processing, monitoring for anomalies, routing work to the right team. These tend to benefit from two things that people don't always plan for upfront: consistent inputs from governed data and a clear way to review or override outputs when the decision is sensitive.

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How Domo supports AI inference workloads

Generating predictions is only half the challenge. Organizations need to connect inference outputs to the dashboards, workflows, and applications where business teams actually work.

Domo integrates with AI inference endpoints and model outputs, allowing teams to visualize predictions, anomaly scores, and AI-generated insights alongside operational data. Domo AI enables natural language interaction with data, using inference to let business people ask questions without writing queries.

For teams that need production-ready AI inference without governance tradeoffs, Agent Catalyst adds governed model orchestration. That includes flexibility in model selection (DomoGPT, third-party models, or custom models) plus guardrails that help keep inference outputs compliant and consistent.

Data and context matter here. Agent Catalyst can ground inference using retrieval-augmented generation (RAG) so models pull from governed Domo datasets, FileSets, and unstructured documents at query time. This helps reduce the "disconnected data source" problem, when different teams ask the same question against different, ungoverned sources and get different answers.

Automated workflows can trigger based on inference results, for example alerting teams when anomaly detection flags an issue or routing predictions to the right stakeholders.

When an output needs review, human-in-the-loop validation can sit inside the workflow. Audit trails also help IT and data leaders keep inference traceable across departments.

For organizations running inference at scale, operationalizing those insights is the real challenge. Domo routes model outputs into the places teams already work, including dashboards, alerts, and Domo Workflows, so predictions and flags can drive next steps without a separate toolchain. If you want to see what governed, production-ready inference looks like when it's wired into dashboards and workflows, watch a demo.