Databricks vs Together AI for open model inference

Summary

• Databricks Model Serving and Together AI both support serving open-source language models, but differ in deployment flexibility and data residency.
• Model Serving hosts models on Databricks infrastructure alongside your data and applications; Together AI maintains its own independent inference API and infrastructure.
• Choose Model Serving if you need tight integration with your data lakehouse or want models co-located with your data; choose Together AI if you prefer a standalone API abstraction layer.
• Both support popular models like Llama, Mistral, and Phi, and both handle scaling automatically.
• For unified governance and lineage tracking, Model Serving integrates with Databricks' Unity Catalog and AI/BI Dashboards.

Databricks vs Together AI for open model inference
Both Databricks Model Serving and Together AI let you deploy open-source language models for inference, but they differ fundamentally in scope and integration. Model Serving runs models on your Databricks workspace alongside your data and Genie Agents; Together AI is a hosted inference platform with its own infrastructure and no dependency on Databricks.

What is open model inference?

Open model inference means deploying publicly available foundation models (Llama, Mistral, Phi, etc.) for real-time predictions or batch scoring. Open models are customizable and can run on your own infrastructure or a third-party inference service, offering control over model behavior and deployment. Organizations choose this when they need reproducibility, cost control, or legal/regulatory assurance around model governance.

Model Serving vs Together AI: how they compare

Capability	Databricks Model Serving	Together AI
Deployment	On Databricks workspace; co-located with data and BI tools	Independent hosted inference API; separate infrastructure
Models supported	Llama, Mistral, Phi, custom ONNX; deployed as GenAI models	Llama, Mistral, and others via API; no custom model hosting
Scaling	Auto-scaling based on traffic; integrated with workspace compute	Auto-scaling managed by Together AI
Data residency	Data stays on your Databricks instance; no data leaves for inference	Requests routed to Together AI's infrastructure
Integration	Unity Catalog lineage, AI/BI Dashboards, Genie Agents	Standalone API; integrations via SDK or REST
Governance	Unified audit trail and role-based access through Databricks platform	Separate auth and audit logs
Model customization	Fine-tuned models can be deployed via GenAI SDK	Limited to pre-built model instances

How Databricks approaches open model inference

Databricks Model Serving lets you host open-source models directly on your workspace using the GenAI SDK or Ollama integration. Models are registered in the Model Registry and access Unity Catalog for data lineage and governance. When a Genie Agent or application calls the model, the request stays within your Databricks environment, ensuring data remains private and audit-logged alongside all other workspace activity.
Model Serving integrates with Databricks' billing (charged per token or per endpoint), and you can monitor performance via AI/BI Dashboards. For on-premise or air-gapped deployments, Databricks also supports local model serving through Ollama.

Use cases

Choose Model Serving if: you want models co-located with sensitive customer data; you need lineage tracking across model > feature > prediction; you're building AI applications inside Genie Agents or Databricks Apps; you want all audit logs in one system; or you need to fine-tune on proprietary data.
Choose Together AI if: you want a vendor-agnostic, managed inference API; you don't need tight data integration; you prefer a separate SLA for the inference layer; or you already have established tooling calling Together's API.

FAQs

Can I use Model Serving and Together AI together?

Yes. Many organizations call Together AI for certain models and use Model Serving for others, depending on cost, latency, or governance requirements. Genie Agents can be configured to route requests to either.

Does Together AI require me to move my data?

No. Together AI is an API; you send requests and get responses back. Your data is not stored on Together's infrastructure unless you explicitly upload it for fine-tuning.

Can I host custom-fine-tuned models on Together AI?

Together AI does not currently host custom fine-tuned models, though they can ingest and evaluate models via their API. For hosting custom models, Model Serving is the better fit.