flo2 - 2026 Pricing, Features, Reviews & Alternatives

flo2 is a developer-first large language model gateway, router and proxy software that provides unified access to multiple AI model providers through a single API endpoint. The platform serves development teams, AI powered applications and businesses that require cost-effective and reliable access to various models from providers such as OpenAI, Anthropic, Groq, Cerebras and DeepInfra. The solution operates on a bring-the-own-key model where users maintain direct payment relationships with providers while flo2 handles intelligent routing and orchestration. This architecture positions the software as a zero markup alternative to token resale services and enables high-volume processing in production environments where cost optimization and uptime are critical.

The platform provides smart model routing capabilities that allow administrators to configure one flo2 API key to access any combination of provider models with options to pin default models, restrict models to specific groups or enable full key collections. A fallback chain functionality automatically redirects requests to secondary models when primary options fail after a defined number of retries while allowing priority ordering through drag-and-drop controls. The parallel request feature executes simultaneous calls to multiple models with configurable head starts so that the fastest response is served while additional models continue to process in the background. An A B testing system captures concurrent responses from new models against live configurations and uses judge models to score output quality so that data-driven model selection can be achieved.

A cost transparency dashboard logs token usage, throughput and computed costs for every API call across all providers to deliver detailed observability and enable spend analysis and optimization. Drop-in compatibility with the OpenAI Chat Completions, OpenAI legacy Completions, OpenAI Responses and Anthropic Messages APIs is provided with streaming support and requires only a base URL modification for implementation. Response caching with user-defined time-to-live settings prevents repeat processing costs for identical requests. An upcoming prompt insight feature will analyze A B test results to generate concrete suggestions for prompt refinement based on comparisons between winning and losing responses.

Integrations with major LLM providers are achieved through direct API connections that require no proprietary software development kit installation and operate over standard HTTP requests using bearer token authentication. Users configure provider keys within the platform dashboard and assign per-million-token pricing for accurate cost accounting. A privacy-by-default architecture ensures that only metadata such as token counts, latency and cost metrics are recorded and that prompt content and model outputs are not stored unless explicitly enabled for testing or analysis features. Content retention windows are limited and data is automatically deleted after results are delivered. The bring-the-own-key approach ensures that all requests route directly to the selected providers without intermediate token resale or shadow data collection while supporting enterprise compliance and data sovereignty requirements.