Adaptive - 2026 Pricing, Features, Reviews & Alternatives

Adaptive is an enterprise platform for building, owning, and deploying specialized large language models (LLMs) via reinforcement learning. Deployed on-premises or in a private cloud, it keeps all data within the customer's environment while optionally integrating with external inference providers (OpenAI, Anthropic, Gemini, Azure OpenAI) through a unified inference management interface. Target industries include telecommunications, financial services, insurance, and legal technology, with customers such as AT&T, SK Telecom, Manulife, and Aikan.

The platform unifies model inference, fine-tuning, evaluation, and production deployment under a single infrastructure called Adaptive Harmony, which combines inference, training, and reinforcement learning in one codebase. Engineering and data science teams can apply RL algorithms including GRPO, GSPO, PPO, DPO, and RLOO through pre-made or customizable training pipelines. Synthetic data generation bootstraps fine-tuning from document corpora without large human annotation workloads. A customizable AI judge workbench evaluates models against business-specific KPIs, while A/B testing in production validates model changes against a subset of live users before full rollout. Multi-adapter serving runs hundreds of fine-tuned adapters on a shared model backbone, and inference autoscaling operates on Kubernetes with custom kernels optimized for A100, L40S, H100, and H200 GPUs.

Continuous learning is supported through an interaction store that captures and annotates production completions, feeding real-world signals and business feedback back into model training. Structured output generation enforces token-level constraints during decoding for JSON Schema and Pydantic-compatible outputs. Integrations include MLflow, Weights and Biases, Tensorboard, and Grafana for experiment tracking and observability. Access is available via a Python SDK and REST API. Forward Deployed Engineers are available to support customer deployments.