Building Solutions with AI APIs and Gemini

Evaluating and selecting a model from Gemini Enterprise Agent Platform Model Garden (opens in a new tab) is the process of matching task requirements, evaluation evidence, and deployment constraints to an optimal low-code AI solution. The candidate model receives prompts and produces responses, the Gen AI evaluation service measures those responses, and the serving configuration determines whether latency, throughput, cost, and accuracy targets are met. A model is an optimal fit only when task type, input and output handling, and serving compatibility all align.

1.1Mapping Task Requirements to Model Garden Capabilities

Mapping task requirements to Model Garden capabilities means translating a use case into objective, input shape, and expected output, then checking them against model attributes. Summarizing news, answering customer inquiries, or assisting with code writing must be reduced to objectives such as classification, summarization, translation, or generation, with prompt datasets in BigQuery or Cloud Storage and batch outputs returned as BigQuery tables or Cloud Storage URIs. Model Garden organizes this match around pipeline tasks:

• text-generation for open-ended generation and conversational responses
• text2text-generation for transformations such as summarization and translation
• text-to-image for generating images from text prompts
• feature-extraction for producing embeddings for search and similarity
• sentence-similarity for comparing semantic closeness between texts
• image-text-to-text for vision-language tasks that combine image and text inputs

Serving compatibility is a hard constraint. The vLLM container supports only text-generation and image-text-to-text models, so other pipeline tasks cannot use the default one-click deployment path. When a desired model is missing, it is typically not a top trending model, not compatible with a supported container, or uses an unsupported pipeline task, and the fallback is a custom Colab notebook, a feature request, or waiting for a future addition.

1.2Validating Model Fit with Gen AI Evaluation

Validating model fit with the Gen AI evaluation service means defining criteria before selection and measuring candidate outputs against them. Criteria are dimensions such as conciseness, relevance, and correctness, while a metric is a single score for output against those criteria. Model-based evaluation moves data in order: first, input prompts flow from the evaluation dataset to each candidate model for response generation; then generated responses flow to the judge model for scoring; finally row judgments flow back to the evaluation service for aggregation with chain-of-thought explanations. Gemini is the judge for most uses, with MetricX or COMET for translation.

Within model-based metrics, pointwise scoring rates one response on a scale such as 0 to 5, while pairwise comparison picks the better of two models and is only supported with Gemini as judge. Pairwise quality checks also use AutoraterConfig with flip_enabled=True to reduce position bias and multi-sampling with sampling_count from 1 to 32 and default 4 to balance consistency against latency.

1.3Choosing Online, Batch, and Private Serving Paths

Choosing a serving path for a selected model decides how features reach the model and how predictions return to the application. Vertex AI online prediction receives instances and returns predictions in the response for near real-time use, while batch prediction scores data from a data lake on a schedule. Online callers first fetch features from a low-latency store such as the Feature Store online serving API, then call the endpoint; batch callers fetch from the data lake or Feature Store batch serving API, while streaming ingestion makes updated features available for online serving within seconds.

Custom prediction routines customize request handling, multiple models can share one endpoint for gradual replacement, and separate endpoints isolate testing from production. For online prediction, set automatic scaling with minimum and maximum nodes, with a minimum of two nodes to support a high availability service level agreement (SLA).

1.4Controlling Cost, Scaling, and Accelerator Capacity

Controlling cost and capacity for Model Garden serving means matching central processing unit (CPU) virtual machine (VM) types and graphics processing unit (GPU) types to model size and latency targets, then forecasting demand. Cloud Monitoring collects utilization and serving signals, Cloud Logging collects logs, and analysis in BigQuery or Looker Studio forecasts GPU and tensor processing unit (TPU) needs. The model observability dashboard exposes model queries per second (QPS), token throughput, and first token latencies, and load testing with Apache JMeter or LoadView validates behavior under high concurrent load.

Cost and obtainability controls trade commitment for flexibility. Committed use discounts (CUDs) lower cost for stable 24x7 inference, Spot VMs lower cost for fault-tolerant evaluation but can be preempted, and Dynamic Workload Scheduler (DWS) improves obtainability with Flex-Start mode for batch training up to seven days and Calendar mode to reserve H100 and H200 GPUs and Trillium TPUs up to three months. GKE Inference Gateway distributes traffic across instances with model-aware routing, while vLLM dynamically uses TPUs and GPUs, Cluster Director manages multi-GPU groups, and Ray on Vertex AI abstracts VMs and containers.

1.5Grounding Selected Models with Enterprise Data

Grounding selected models with enterprise data connects a generative model to retrieved documents so responses reflect current facts. RAG Engine uses Gemini Enterprise Agent Platform Vector Search 2.0 to store vector representations and retrieve by semantic similarity, while managing collections that remain visible in the project. RAG Engine corpora backed by Vector Search 2.0 are available only in us-central1, support Virtual Private Cloud Service Controls (VPC-SC) and customer-managed encryption keys (CMEK) handling at the service level, but do not support data residency and Access Transparency controls.

Choose RagManagedVertexVectorSearch for latest features with automated management, and choose RagManagedDb only when CMEK is a strict requirement. Pre-trained models are ready to use on generalized data, while user-trained models use Vertex AI AutoML or custom training for unique labels and language, with Gemini Enterprise Agent Platform Vision supporting AutoML vision models created in October 2022 or later.

Building applications with industry-specific AI APIs means matching Document AI (opens in a new tab), Vision, and Translate to the data type and task, then operating them reliably at scale. Functional fit for form parsing, object detection, or multilingual processing is only the starting point. The selected API must also be monitorable, scalable, and cost-efficient under growing traffic.

2.1Monitoring Deployment Health and Business Thresholds

Monitoring deployment health and business thresholds provides the signals that show whether an API integration stays healthy under real traffic. Infrastructure can include Compute Engine instances, Google Kubernetes Engine (GKE) clusters, and TPUs and GPUs, with Active Assist recommendations for optimization. Release state flows from Cloud Deploy to Cloud Monitoring for visibility of canary releases and rollbacks, while Security Command Center monitors threats separately from performance. Autoscaling behavior must be monitored to confirm that scaling responds correctly to traffic changes.

Custom alerting ties metrics to business goals so alerts fire only when business impact is at risk. Cloud Monitoring creates dashboards, defines custom metrics, and sends notifications to PagerDuty or Slack. The observability system combines five products:

• Cloud Logging collects, stores, and analyzes logs from all components
• Cloud Monitoring visualizes metrics and routes alerts to incident tools
• Error Reporting automatically captures errors and exceptions from production code
• Cloud Trace analyzes distributed performance and identifies bottlenecks between pipeline components
• Cloud Profiler continuously analyzes production code for CPU or memory bottlenecks

Log data flows to Cloud Logging, metric data flows to Cloud Monitoring dashboards, and trace and profiler data explain where latency and cost originate when an API must be tuned.

2.2Designing API Infrastructure for Scale and Peak Demand

Designing API infrastructure for scale ensures that the selected API and its infrastructure handle growing volumes without bottlenecks. Scalable design uses managed services, capacity planning, horizontal scaling, and quota management so the system stays responsive as load grows. Planning starts with stakeholders to project growth, then uses Cloud Monitoring to analyze historical utilization and project future needs, validated by load testing before peaks expose bottlenecks. Teams must know quotas for Compute Engine, Agent Platform, and Cloud Storage, request increases with forecasting justification, and alert when usage approaches limits.

Elastic capacity translates forecasts into cost controls. Teams rightsize resources, implement autoscaling, and use Spot VMs for fault-tolerant batch workloads, while a documented peak-event strategy with regular drills tests sudden spikes. Operational excellence sustains this over time through automation and standardization for repeatability, continuous learning for currency, accountability with clear roles and metrics, and AI ethics and safety checks for fairness, bias, and societal impact.

2.3Decoupling and Transforming API Workloads at Scale

Decoupling and transforming API workloads at scale separates producers from processors so quota limits and latency do not stop the system. Pub/Sub is an asynchronous messaging service where producers send work to a topic through Cloud Run functions, the Pub/Sub API, or the Google Cloud CLI, and processors later pull from the topic. Data flows from producer to topic to processor, so bursts queue durably instead of overwhelming Document AI, Vision, or Translate, and both sides scale independently.

A pull-based agent adds controlled consumption on the processor side. The agent in each VM subscribes to topics and runs an ordered sequence:

1. Listen: The agent subscribes to one or more assigned Pub/Sub topics.
2. Pull: The agent pulls the next message only when idle.
3. Start: The agent starts a pipeline in third-party software for that message.
4. Wait: The agent holds no additional work during execution.
5. Advance: The agent retrieves the next message after completion.

Cloud Data Fusion and Dataflow provide managed transformation around these queues. Cloud Data Fusion builds batch and streaming pipelines as a directed acyclic graph (DAG) in a graphical UI that executes as an Apache Spark job on Managed Service for Apache Spark, with Java Database Connectivity (JDBC) sources and BigQuery sinks. Dataflow runs Apache Beam jobs with windowing and session analysis in stream and batch modes via CLI, Java SDK, or Python SDK, with serverless scaling.

Choose Cloud Data Fusion when visual assembly and reusable plugins reduce effort, and choose Dataflow when custom windowing and unified Beam logic are needed for large-scale enrichment.

2.4Optimizing API Cost, Latency, and Response Quality

Optimizing API cost, latency, and response quality shapes requests, reuses stored results, and gates outputs on quality scores. Decoupled serving keeps synchronous work minimal: bursts queue in Pub/Sub, Cloud Run functions runs single-purpose serverless logic with automatic scaling, and Agent Platform with Vector Search lowers per-query latency for very large-scale retrieval. The serving path flows from model to BigQuery logging to responsible AI filtering to frontend delivery, so unsafe outputs are blocked without a second generation pass.

Caching and quality evaluation separate operational reads from offline analysis. Cloud Storage holds raw objects for grounding, AlloyDB for PostgreSQL holds configuration and evaluation prompts for low-latency reads by functions, and BigQuery holds responses and scores for analysis without re-invoking the model. The quality loop flows from Pub/Sub trigger to function configuration from AlloyDB for PostgreSQL to prompt fetch to scoring for factual accuracy and relevance to persistence in BigQuery. Scores let high-confidence responses go directly to users while low-confidence responses go to human review or a second retrieval pass. AlloyDB for PostgreSQL encrypts by default, supports CMEK and SSL-only connections, and with BigQuery supports regional data residency, while Cloud Storage uses IAM with bucket and project level control.

Building solutions with foundation models means selecting Gemini for text and multimodal reasoning, Imagen for images, Veo for video, and Model Garden (opens in a new tab) open models for specialized serving, then tuning and deploying them efficiently. Managed Model-as-a-Service (MaaS) APIs minimize operations, while dedicated deployments provide control over accelerators, scaling, and data location. Tuning customizes behavior when prompting alone cannot produce exact formats or domain language.

3.1Customizing Gemini with Supervised Fine-Tuning

Customizing Gemini with supervised fine-tuning (SFT) teaches a new skill by training on hundreds of labeled input-output examples. In Vertex AI, Gemini SFT is parameter-efficient tuning where base parameters stay frozen and only small adapter weights are learned, then combined with the original model for serving. SFT minimizes the difference between predictions and labels and is the correct choice when output can be clearly defined, deviates from general patterns, produces inconsistent results with prompting, is too complex to articulate, or needs shorter context for lower latency and cost. Starting with prompt design for prototyping, then moving to tuning when 100 or more high-quality examples exist and recurrent errors persist, is the documented pattern.

SFT is particularly effective for exact behavior:

• Classification: force a single label such as business instead of verbose explanation
• Summarization: enforce custom format such as #Person1 and #Person2 with personally identifiable information (PII) removed
• Extractive question answering: return an exact substring such as Last Glacial Maximum
• Chat and persona control: constrain the assistant to a role such as shopkeeper for purchases and shipping

Dataset preparation uses a labeled JSONL file in Cloud Storage matching production distribution, with validation data optional but required for generalization metrics. Limits are 131,072 maximum input and output training tokens, 1 GB maximum JSONL size, up to 10M text-only or 300K multimodal examples, and validation limited to 5000 examples or 30% of training when validation exceeds 1000 examples.

Related options use different mechanisms: Reinforcement Learning from Human Feedback (RLHF) and preference tuning learn subjective preferences after SFT, distillation trains a smaller student to mimic a teacher, and grounding connects outputs to sources without changing weights.

3.2Running and Evaluating Tuning Jobs in Vertex AI

Running a tuning job in Gemini Enterprise Agent Platform executes a pipeline that reads data from Cloud Storage, learns adapter parameters, and deploys the tuned model and endpoint in the tuning job region. Supported bases include Gemini 3.5 Flash, Gemini 3.1 Flash-Lite, Gemini 2.5 Pro, Gemini 2.5 Flash, and Gemini 2.5 Flash-Lite. User data stays in the tuning region while compute may offload to other US or EU regions, billed training tokens equal dataset tokens multiplied by epochs, and quota is a global concurrent-jobs limit shared across regions. Adapter sizes are 1, 2, 4, 8, and 16, with some models supporting only 1, 2, 4, and 8.

The console flow in Agent Platform Studio runs in order:

1. Open tuning: Go to Agent Platform Studio and click Create tuned model.
2. Set model details: Enter a name up to 128 characters, select base model and region.
3. Set hyperparameters: Enter epochs, adapter size, and learning rate multiplier with default 1.
4. Set checkpoints: Optionally enable Export last checkpoint only.
5. Set training data: Choose upload or existing Cloud Storage JSONL file.
6. Set validation data: Optionally enable validation with Cloud Storage path.
7. Start and monitor: Click Start Tuning until Status shows Succeeded.

Evaluation combines tuning metrics for learning progress with validation metrics for generalization, plus optional integrated evaluation via evaluationConfig in Preview in us-central1 for gemini-2.5-pro, gemini-2.5-flash, and gemini-2.5-flash-lite. If validation degrades while training improves, reduce epochs, adapter size, or learning rate, improve validation coverage, or select an earlier checkpoint. Continuous tuning can resume from a tuned model or checkpoint with more epochs or examples.

3.3Deploying Tuned and Model-as-a-Service Endpoints

Deploying tuned and Model-as-a-Service endpoints places foundation models behind scalable inference APIs with retrieval and safety controls. Model Garden is the catalog for discovering models: Gemini (Vertex AI) handles text and multimodal prompts with Google Search grounding, Imagen (Vertex AI) generates images, Codey (Vertex AI) generates code, and text-embedding models generate vectors for search or clustering. Traffic flows from user to Cloud Load Balancing frontend to Cloud Run frontend to retrieval service to Vertex AI for embedding and generation. The retrieval service depends on Secret Manager for keys and Cloud SQL or AlloyDB for vector lookup, so credentials never live in code.

Grounding augments prompts with retrieved data before generation. The retrieval service converts the request to an embedding, searches for similar vectors, then sends passages plus prompt to Vertex AI. Vector storage selection depends on shape and latency:

Secure endpoints authenticate with Secret Manager credentials, encrypt AlloyDB for PostgreSQL by default with CMEK option, enforce SSL-only connections with IAM Auth Proxy over TLS 1.3 and 256-bit AES, and reduce exfiltration with VPC Service Controls. After generation and before frontend delivery, responsible AI filters screen responses, then prompts and logs flow to BigQuery for audit.

3.4Running Enterprise Ingestion and Grounded Serving

Running enterprise ingestion and grounded serving in Gemini Enterprise separates data preparation from query handling so new data becomes retrievable quickly. The ingestion pipeline runs in order:

1. Upload: Data engineers upload raw data to a Cloud Storage bucket.
2. Notify: Cloud Storage publishes completion to a Pub/Sub topic.
3. Process: The topic triggers a Cloud Run functions job that enriches data and writes JSON Lines (JSONL) metadata to a second bucket.
4. Notify: The function publishes completion to a second Pub/Sub topic.
5. Index: The topic triggers ingestion into the managed datastore, which parses, chunks, and embeds automatically.

The serving subsystem runs in order:

1. Receive: A Cloud Run frontend receives the query from UI or app.
2. Preprocess: The frontend forwards the query to a centralized backend that builds search filters.
3. Invoke: The backend sends the request to the Gemini Enterprise API endpoint.
4. Retrieve and generate: Gemini Enterprise embeds the query, performs semantic search, augments the prompt, and generates the response.
5. Return: Gemini Enterprise sends the response to the backend, which returns it to the frontend.

Quality evaluation runs asynchronously: Pub/Sub triggers a function, the function loads configuration and evaluation prompts from AlloyDB for PostgreSQL, scores factual accuracy and relevance, and loads scores to BigQuery for retuning or index adjustments.

3.5Synthesizing Video and Serving Open Models

Synthesizing video with Veo and serving open models from Model Garden covers two specialized serving patterns. Veo is a long-running model where the request flows from client to Veo endpoint, the service starts an operation, and video flows to the Cloud Storage bucket in output_gcs_uri or returns inline as bytes. The client must poll until operation.done is true, then read generated_videos[0].video.uri. Veo 3 generates 4, 6, or 8 second clips with default 8, Veo 2 generates 5 to 8 seconds with default 8, and first-frame and last-frame images constrain start and end composition.

A Veo image-to-video request follows an ordered workflow:

1. Build request: Supply model as veo-3.1-generate-001 or veo-3.1-fast-generate-001, a motion prompt, and a first-frame image as image/jpeg or image/png in Cloud Storage.
2. Add controls: Optionally supply last_frame, aspect_ratio such as 16:9, output_gcs_uri, sampleCount from 1-4, and seed.
3. Start operation: Call client.models.generate_videos or endpoints.predict to get an operation handle.
4. Poll to completion: Re-fetch with client.operations.get(operation) until done and read the output URI.

Serving open models trades operational overhead for control. MaaS keeps compute on the provider side behind a shared API, Model Garden deployment provisions a dedicated Vertex AI endpoint in the customer project with chosen GPUs or TPUs for residency and VPC control, and custom vLLM containers provide full throughput tuning. The serving stack determines accelerator use: vLLM, SGLang, and TensorRT-LLM for high-throughput decoding, Text Embeddings Inference (TEI) for embeddings, PyTorch Inference for general Hugging Face models, and Hex-LLM for TPU-optimized Llama serving.

Optimizing Gemini-based applications means aligning model choice, prompt design, inference mode, and resiliency so cost, latency, throughput, and availability targets are met together. Traffic flows from clients to global load balancing to healthy Vertex AI Agent Platform endpoints, while telemetry flows from serving infrastructure to monitoring and analysis tools that drive capacity decisions. Resiliency depends on multi-region routing, replicated state, reserved throughput, and continuous observability.

4.1Serving Across Regions with Redundant Dependencies

Serving across regions with redundant dependencies keeps inference, retrieval-augmented generation (RAG), and agent state available when a zone or region fails. Regional endpoints keep data in specific regions, while the global endpoint pools Google-managed capacity across regions or countries.

Each layer replicates independently so one zone loss does not stop serving:

• Google Kubernetes Engine (GKE) regional clusters replicate control plane and node pools across zones
• Cloud Storage multi-regional or dual-region buckets replicate datasets and checkpoints
• Spanner provides globally consistent storage for shared agent metadata
• Cloud SQL read replicas provide highly available transactional state
• Vector databases for RAG remain multi-zonal or multi-regional so retrieval is not a single point of failure

Resilient agents separate stateless execution from durable state, with multiple instances behind load balancing and state flowing to Memory Bank, Memorystore for Redis, or Cloud SQL for graceful restart. Accelerator faults are handled in order: monitor for Xid errors, isolate by resetting GPU or instance, then replace hardware with gcloud when resets fail.

4.2Selecting Models, Optimizing Prompts, and Caching Context

Selecting models, optimizing prompts, and caching context are the three primary levers for Gemini cost and latency. Model selection sets baseline price and capability: test iteratively starting with the most cost-efficient model and move up only until quality is met, creating baselines for queries per second (QPS) and tokens per second (TPS) to plan capacity and decide when reserved throughput is needed. Prompt optimization reduces input and output tokens with short, direct prompts and concise instructions such as summarize in 2 sentences, which lowers per-request charges and generation latency. The Agent Platform prompt optimizer automates rewriting and adapts prompts when the model tier changes.

Vertex AI context caching stores repeated high-token content once and reuses it across requests. Without caching, full input tokens flow from client to model on every request; with caching, the prefix flows to a cache on the first call and later requests flow as a cache reference plus new delta, reducing billed input tokens and time-to-first-token. The benefit grows on cache hits for long-context applications sharing system instructions or documents with high QPS.

4.3Choosing Inference Modes and Provisioned Throughput

Choosing inference modes in Gemini Enterprise Agent Platform (opens in a new tab) decides how compute is allocated, billed, and scaled. Provisioned Throughput reserves guaranteed throughput for a commitment, the PayGo family shares capacity without commitment, and batch inference processes queued jobs offline.

A 1-year Provisioned Throughput commitment is priced at a 26% discount to 1-month, while Flex PayGo cuts costs by about 50% and batch is about 50% cheaper than sequential Standard PayGo. Provision to cover baseline and highest-priority traffic, not peaks, because provisioning for peaks lowers utilization. Traffic above the TPM limit, called spillover, flows to Standard PayGo by default, or to Priority PayGo when overflow needs more reliability at premium price.

4.4Controlling Latency, Scaling, and Throttling in Production

Controlling latency, scaling, and throttling in production keeps interactive Gemini workloads responsive under variable load. Response streaming with streamGenerateContent sends tokens from model to application before completion, improving perceived latency measured as time to first token (TTFT) for first output and time to last token (TTLT) for full response. TTFT matters most for streaming responsiveness, while TTLT grows with generated tokens. Controls that reduce TTFT and TTLT include selecting smaller models such as Flash-Lite, reducing prompt size and placing common content first for cache hits, limiting output with max_output_tokens, limiting thinking budget on Thinking models, using Provisioned Throughput for consistency, and sending similar prefixes in a short window for caching.

Elastic compute and availability controls absorb spikes and regional limits signaled by 429 resource-exhausted errors. In GKE, cluster autoscaler and Horizontal Pod Autoscaler adjust nodes and Pods on CPU, memory, GPU, TPU, or requests per second, with node affinity permitting fallback from N2 to N2D, C2, or E2, from A2 A100 GPUs to L4 or T4, and from TPU Ironwood to Trillium or v5e or v5p. In Cloud Run, services autoscale between minimum and maximum instances and scale to zero when idle. Proactive practices include exponential-backoff retries for 429 errors, hybrid Provisioned Throughput plus PayGo for critical apps, monitoring TPM consumption before launches, routing with the global endpoint for global capacity, and shifting deferrable work off-peak. Telemetry flows from Cloud Monitoring and Cloud Logging to BigQuery or Looker Studio for forecasting, validated by JMeter or LoadView tests, with the observability dashboard exposing QPS, token throughput, and first-token latency.

Key Facts

• The vLLM container supports only text-generation and image-text-to-text models, so other Model Garden pipeline tasks cannot use the default one-click deployment path.
• Vertex AI online prediction requires a minimum of two autoscaling nodes to support a high availability SLA, and a 429 error means request volume exceeded allocated capacity.
• RAG Engine corpora backed by Vector Search 2.0 are available only in us-central1, and only the RagManagedDb backend backed by Spanner supports customer-managed encryption keys (CMEK).
• Supervised fine-tuning in Vertex AI freezes Gemini base parameters and learns only adapter weights with sizes 1, 2, 4, 8, and 16, with limits of 131,072 training tokens and 1 GB JSONL files.
• Provisioned Throughput reserves guaranteed throughput for 1-week to 1-year commitments and handles spillover above tokens-per-minute with Standard or Priority PayGo, while Flex PayGo and batch inference cut costs by about 50% for latency-tolerant work.