AI Services

Create and manage AI services for LLMs and embeddings using your configured providers

What Are AI Services?

AI Services are specific AI model instances that you configure for use in your workflows. While AI Providers establish connections to external AI platforms, AI Services define the actual models, settings, and configurations that power your AI features.

Prerequisites: You must have at least one AI Provider configured before creating AI Services. See the AI Overview for setup instructions.

Required Permission:

Types of AI Services

Elementum supports two types of AI Services:

• LLM Services — Language models for text generation, conversation, and analysis. Used for agents, automation actions, and content generation.
• Embedding Services — Embedding models for semantic search and similarity analysis. Used in AI Search to convert data into vector representations for semantic querying.

Prerequisites

Before creating services, you need at least one configured AI provider. Provider setup is covered on its own page per provider; once your provider is saved in Organization Settings → Providers, return here to create services.

OpenAI

Connect OpenAI as a provider

Anthropic

Connect Anthropic for direct Claude access

Snowflake Cortex

Connect Snowflake Cortex for LLM and embedding services

Google Gemini

Connect Vertex AI Gemini

Amazon Bedrock

Connect Bedrock-hosted models

Custom Provider

Connect any OpenAI-compatible endpoint (configured below)

Only verified domain users can configure AI Providers. Elementum employees cannot create or modify AI Providers or Models in any customer org. See the AI FAQ for compliance details.

CloudLink type matters. AI services are not supported on API CloudLinks. They require a data-warehouse CloudLink such as a Snowflake CloudLink (with key-pair authentication for Cortex features).

Configure a Custom Provider

Use the Custom provider type to connect any OpenAI-compatible endpoint, including LLM gateways, proxies, and self-hosted models. Once configured, a custom provider can be used across agents and automations just like any built-in provider.

1. In AI Services, click + Connect Provider and choose Custom
2. Enter a Name to identify the provider in Elementum
3. Enter the URL of the OpenAI-compatible endpoint
4. Select the connection type from the dropdown:
   • API Key — Provide a static API key issued by your endpoint
   • OAuth Credentials — Provide the OAuth Client Credentials (client ID, client secret, and token URL) used to obtain a bearer token
5. Enter the required credentials for the connection type you selected
6. Click Save

Once the custom provider is configured, click + Add Models on the provider to add any model identifier exposed by the endpoint. Custom-provider models then appear in the model dropdown when creating an LLM Service.

The endpoint must implement the OpenAI Chat Completions API contract. Capabilities available to a custom-provider model (such as structured output, multimodal input, or reasoning) depend on what the underlying endpoint supports.

Manage Providers

Once a provider is connected, click on it in the Providers tab to:

• View connection details and status
• Edit the provider configuration or credentials
• Delete the provider
• Click + Add Models to make additional models from this provider available to your services

Configure Provider Failover

Configure one or more backup providers so traffic automatically reroutes if the primary provider is unreachable. No user action is required during an outage.

Prerequisites:

• At least two configured AI Providers of compatible model families.
• Each provider must have active, tested credentials.

Configuration steps:

1. On the Providers tab, open the dropdown for the desired provider and click the Provider Details icon.
2. Click the Edit icon next to Backup Providers.
3. Select a provider to use in case of failover. Choose multiple to ensure several options are available.
4. Click Save.

Behavior notes:

• Failover is automatic — no user action is required during an outage.
• Only providers whose status is Active are eligible targets.
• Failover applies to all features consuming the primary provider (agents, search, summarization, and so on).
• When the primary provider recovers, new requests resume routing to it.

Migrate a Model Across the Organization

Replace any AI model with a different model in a single action. Every automation action and agent that references the source model switches over automatically, so you don’t need to update each one individually.

Configuration steps:

1. On the Providers tab, click the dropdown next to the service whose model you want to replace.

2. Click the Replace Model icon.

3. Select the new model from the dropdown.

   The popup lists every automation that uses the current model so you can review the impact before confirming.

4. Click Replace.

Behavior notes:

• The migration runs in the background and can take time when many automations reference the source model. Track progress under Background tasks.
• Agents and automation actions referencing the source model are updated in place — you don’t need to reopen and republish each one.

Create AI Services

To create a service:

1. Navigate to the AI Services page and open the Services tab.
2. Click + Service and choose the service type—LLM for language models or Embedding for AI Search.
3. Configure the fields described below for the service type you selected.
4. Click Save. New services appear in the Services tab and can be tested before assignment.

Create an LLM Service

LLM Services power conversational AI, text generation, and intelligent automation.

Service Configuration

Service Name: Give your service a descriptive name (e.g., “Customer Support Bot”)

Provider: Select your configured AI Provider

Model: Choose from available models for your provider. See AI Models for a detailed comparison of capabilities, use cases, and pricing considerations across all providers.

Cost Per Million Tokens: Optional cost tracking (varies by provider)

Advanced Settings

Temperature: Controls randomness and creativity of responses (0.0–1.0). Lower values produce more deterministic, consistent outputs; higher values produce more varied, creative responses. Set to 0.0–0.3 for automation tasks like classification where consistency matters.

Reasoning Effort: Controls how much computational effort the model invests in internal reasoning before responding (minimal/low/medium/high)

• Use minimal for simple lookups and straightforward answers
• Use low for basic reasoning tasks and simple problem solving
• Use medium for moderate analytical tasks requiring multi-step reasoning (default)
• Use high for complex problem solving, mathematical proofs, multi-step logic, and detailed analysis

Max Tokens: Maximum response length

Top P: Controls diversity of responses (nucleus sampling)

Frequency Penalty: Reduces repetition in responses

Presence Penalty: Encourages topic diversity

Stop Sequences: Custom stop sequences for response control

For use-case-specific tuning recommendations (e.g., optimal temperature for classification vs. content generation), see the Temperature Settings and Performance Optimization sections on the AI Models page.

Create an Embedding Service

Embedding Services enable AI Search and semantic understanding.

Service Configuration

Service Name: Descriptive name (e.g., “Document Search Embeddings”)

Provider: Select your configured AI Provider

Model: Choose from available embedding models:

• Snowflake Arctic L V2.0 — Latest high-quality embeddings
• Snowflake Arctic L V1.5 — Reliable embeddings for production use

Dimensions: Embedding vector size (varies by model)

Configuration Options

Batch Size: Number of texts to process simultaneously

Chunk Size: Maximum text length per embedding

Overlap: Text overlap between chunks (for long documents)

Normalization: Whether to normalize embedding vectors

Encoding: Text encoding method (usually UTF-8)

Assign to Features

Assign AI models to Elementum features at the organization level so those features have a default model available across your workflows.

1. Navigate to the AI Services page and click the Features tab
2. Click + Assign Model next to a feature and select a model from the dropdown
3. Click Save

To update an existing assignment, click Change next to the currently assigned model, select a new model from the dropdown, and click Save.

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Test Services

Before using AI Services in production, test them from the Services list:

1. Click on a service name to open the testing interface
2. For LLM Services: enter sample prompts, review AI-generated responses, adjust parameters, and monitor response times
3. For Embedding Services: enter sample text, review generated embedding vectors, and test similarity calculations between texts

Manage and Optimize

Once your services are created and tested, keep the following in mind:

• Model selection — The right model depends on your use case. For recommendations by task type (agents, classification, content generation, semantic search) and guidance on balancing cost and performance, see the Model Selection Guide.
• Cost optimization — Right-size your model choices, write concise prompts, and set appropriate token limits to control spending. See Cost Optimization for detailed strategies.
• Multiple providers — You can configure services across different providers for redundancy or to use different model strengths for different tasks. See AI Providers for setup details.
• Feature-specific guidance — For details on how AI Services integrate with specific capabilities, see AI in Automations for automation actions, Building Agents for conversational agents, and AI Search for embedding-powered search.

Troubleshooting

Service Creation Failures

Symptoms: Cannot create new AI services

Common Causes:

• AI Provider not configured
• Invalid model selection
• Insufficient permissions

Solutions:

1. Verify AI Provider is properly configured
2. Check model availability for your provider
3. Ensure proper permissions are granted
4. Try creating with different model options

Poor Performance

Symptoms: Slow response times or quality issues

Common Causes:

• Inappropriate model selection
• Suboptimal configuration
• Network or provider issues

Solutions:

1. Review model selection for your use case
2. Optimize service configuration settings
3. Check provider status and network connectivity
4. Consider switching to different models

High Costs

Symptoms: Unexpected high token usage or costs

Common Causes:

• Inefficient prompts or queries
• Inappropriate model selection
• Excessive API calls

Solutions:

1. Review and optimize prompts
2. Use more cost-effective models where appropriate
3. Implement caching and batching
4. Monitor and analyze usage patterns

Next Steps

AI Models

Compare models across providers to choose the right one for your use case

Enable AI Search

Use embedding services to power semantic search across your data

Build Agents

Create conversational AI assistants using your LLM services

AI in Automations

Add AI-driven actions to your automation workflows