Keep model choice on your side.
Opal gives you model flexibility inside a governed operational platform so you can adapt quality, cost, and provider strategy without rebuilding your workflows, knowledge, or operating IP.
Most AI platforms quietly lock you into their stack.
Many platforms push you toward their preferred models, pricing, and roadmap. That creates rising token costs, limited negotiating power, and expensive transitions when a better model appears.
From setup to live workflow.
- Step 01
Connect providers
Bring your model providers and credentials into one workspace.
- Step 02
Pick defaults
Set defaults per use case. Reasoning, drafting, classification, coding, and more.
- Step 03
Route by task
Send each step in a flow to the model that handles it best.
- Step 04
Compare and switch
Evaluate quality, cost, and latency. Then switch without rewriting flows.
Model strategy that adapts with the market.
Keep your workflows, prompts, knowledge, processes, and connected systems in place while changing the underlying model strategy.
- 01Different use cases across workflows and teams
- 02Power for agents, prompts, and task execution paths
- 03Controlled deployment alongside governance and guardrails
- 04Adaptability as model capabilities and pricing evolve
- 05Reduced lock-in risk and protected operational IP
What teams actually see.
Right model for each task
Use the strongest model where it matters, the cheapest where it does not.
Lower cost without quality loss
Smart routing reduces spend without slowing teams down.
Future-proof model changes
Swap providers as the landscape evolves. Flows keep running.
Works with the rest of the platform.
See the full platformBuild on the right models for the job.
See how Opal helps you combine model choice with operational control.