Prediction Guard

Enables deployment of large language models within customer-controlled infrastructure (on-premise or private cloud) rather than sending requests to third-party API endpoints. The architecture isolates model inference to customer-owned compute resources, implementing network-level access controls and data residency guarantees through containerized model serving with optional air-gapped deployment patterns.

Abstracts differences between multiple LLM providers (OpenAI, Anthropic, open-source models, private deployments) behind a single standardized API interface. Routes requests to the appropriate backend based on configuration, handling provider-specific parameter mapping, response normalization, and fallback logic transparently to the application layer.

Implements configurable content filtering rules that intercept and evaluate both user inputs and model outputs against compliance frameworks (HIPAA, GDPR, PCI-DSS, SOC2). Uses pattern matching, PII detection, and semantic analysis to identify and redact sensitive data, block prohibited content, and enforce organizational policies before data reaches the model or leaves the system.

Constrains LLM outputs to conform to predefined JSON schemas or structured formats, using techniques like constrained decoding or output validation to ensure responses match expected data structures. Validates outputs against the schema and either rejects non-conforming responses or automatically retries with schema-aware prompting to increase compliance.

Monitors and aggregates token consumption across all LLM API calls, attributing costs to specific users, projects, or cost centers based on configurable allocation rules. Provides real-time dashboards and historical analytics showing cost trends, model efficiency metrics, and per-user/per-project spending with support for budget alerts and usage quotas.

Captures and stores complete audit logs of all LLM interactions including prompts, responses, model parameters, user identifiers, timestamps, and compliance filter actions. Implements immutable logging with tamper detection, supports log retention policies aligned with regulatory requirements, and provides query interfaces for incident investigation and compliance audits.

Tracks quality metrics for LLM outputs including latency, token efficiency, error rates, and user satisfaction signals. Implements automated anomaly detection to identify degraded model performance, compares quality across different models or providers, and surfaces insights for model selection and optimization decisions.

Enforces configurable rate limits and usage quotas at multiple levels (per-user, per-project, per-API-key, global) to prevent abuse and control resource consumption. Implements token bucket or sliding window algorithms with graceful degradation (queuing, backpressure) and supports different quota policies for different user tiers or use cases.