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Getting a single AI call to work is easy. Making AI reliable in production is not. Teams that ship AI into real products quickly run into the same set of problems: you’re locked into one provider’s SDK, switching models means rewriting integration code, there’s no visibility into what the AI is actually doing, and quality is unpredictable. As you add more models and more use cases, the complexity compounds — separate credentials, different APIs, no shared observability, and guardrails bolted on as an afterthought. Opper exists to solve this. It sits between your application and the models it uses, giving you a single place to connect, control, and observe all your AI interactions.

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Gateway — multi-model, multimodal generation

The gateway is your single connection to AI models. One API, one key, 200+ models across all major providers — OpenAI, Anthropic, Google, Mistral, and more.
  • Any model, same API. Route to any supported model without provider-specific SDKs or credentials. Switch models with a config change, not a code rewrite.
  • Multimodal by default. Text, images, audio, embeddings — the same API handles all modalities. Build applications that work across media types without juggling separate integrations.
  • Model independence. Models change fast. New ones launch, pricing shifts, capabilities improve. Your code shouldn’t have to change with them. With Opper, the model is a parameter — everything else stays the same.
  • Drop-in compatibility. Use the Opper Multimodal API, or connect through OpenAI, Anthropic, and Gemini compatible endpoints. Migrate incrementally without rewriting your stack.

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Control Plane — making AI reliable

The control plane is where you go from “it works” to “it works reliably.” It handles observability, quality steering, guardrails, and memory — the things that matter once AI is in production.
  • Observe. Every call produces a trace with full context — input, output, latency, cost, and model used. Traces link together across multi-step workflows. Attach metrics and evaluations to track quality over time and catch regressions early.
  • Steer. Improve quality through feedback loops. Save good outputs as examples, build datasets for evaluation, and let the platform use real data to guide model behavior. Quality compounds — every interaction makes the next one better.
  • Guard. Enforce guardrails at the infrastructure level — PII removal, content filtering, and budget limits. Protection happens before data reaches the model, not scattered across your application code.
  • Memory. Store and retrieve knowledge through semantic indexes. Give your AI access to custom context without managing vector databases yourself.

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Security

Opper is a secure intermediary between your application and external models. All traffic is encrypted and authenticated, and the platform is designed so that your data stays under your control.
  • Infrastructure. Opper deploys regionally on AWS. All data except model calls is contained within the deployment — vector databases, routing, tracing, and caching stay in-region. Currently available in Stockholm, Sweden.
  • Authentication. SSO is supported with major providers (Google, GitHub). All API calls are authenticated with API keys. Users get their own account and can be part of a multi-user organization.
  • Compliance. Opper follows European data protection directives and security standards, including GDPR. For compliance details, contact support@opper.ai.