Portkey

Routes LLM API requests across multiple providers (OpenAI, Anthropic, Cohere, Azure, etc.) with automatic fallback logic when primary provider fails or rate-limits. Implements provider abstraction layer that normalizes request/response formats across heterogeneous APIs, enabling seamless switching without application code changes. Uses connection pooling and circuit breaker patterns to detect provider degradation and trigger failover within milliseconds.

Caches LLM responses using semantic similarity matching rather than exact string matching, so identical queries phrased differently return cached results. Uses embedding-based similarity thresholds (configurable cosine distance) to determine cache hits, reducing redundant API calls to LLM providers. Stores cache entries with provider cost metadata, enabling cost tracking and deduplication across identical semantic queries regardless of phrasing.

Provides language-specific SDKs (Python, Node.js, etc.) that intercept LLM API calls at the SDK level using middleware/decorator patterns, injecting Portkey functionality (routing, caching, logging, rate limiting) without modifying application code. Middleware chain allows composing multiple behaviors (e.g., cache → route → retry → log) in configurable order. Supports both synchronous and asynchronous request patterns.

Provides web-based dashboard visualizing LLM usage metrics (requests per time period, tokens consumed, latency distribution, error rates) and cost metrics (total spend, cost per user/feature/model, cost trends). Supports custom time ranges, filtering by provider/model/metadata, and drill-down analysis. Exports metrics as CSV or integrates with BI tools via API.

Automatically captures all LLM API requests and responses with structured metadata (latency, tokens, cost, provider, model, status codes) and stores them in queryable logs. Implements middleware-style interception at the SDK level to log without modifying application code. Provides structured query interface to filter logs by provider, model, latency, cost, error type, and custom metadata, enabling debugging and auditing of LLM interactions.

Tracks input and output token consumption per request, per model, and per provider, then calculates real-time costs using provider-specific pricing tables. Attributes costs to custom dimensions (user, organization, feature, environment) via metadata tagging, enabling granular cost allocation. Aggregates token and cost metrics across time periods and dimensions, providing dashboards and APIs for cost analysis and budget monitoring.

Automatically retries failed LLM API requests using configurable exponential backoff with jitter to avoid thundering herd problems. Distinguishes between retryable errors (rate limits, transient network failures, 5xx errors) and non-retryable errors (authentication failures, invalid requests), applying retry logic only to appropriate error types. Allows per-request retry configuration (max attempts, backoff multiplier, jitter range) and tracks retry metrics for observability.

Enforces rate limits and quotas on LLM API requests at the application level, preventing excessive usage before hitting provider limits. Supports multiple rate-limiting strategies (token-per-minute, requests-per-minute, concurrent requests) and quota types (daily, monthly, per-user, per-organization). Implements sliding window or token bucket algorithms to track usage and reject or queue requests that exceed limits, with configurable behavior (fail-fast, queue, or degrade).

Stores and versions prompts with metadata (model, temperature, max_tokens, etc.), enabling comparison of different prompt versions and configurations. Supports A/B testing by routing requests to different prompt versions based on user, session, or random assignment, with automatic metrics collection (latency, cost, quality scores). Provides rollback capability to revert to previous prompt versions without code deployment.

Allows applications to attach arbitrary key-value metadata to LLM requests (user_id, org_id, feature_name, environment, custom_field) which is propagated through the entire request lifecycle and available in logs, metrics, and dashboards. Metadata is used for cost attribution, filtering, debugging, and analytics without modifying the actual LLM request. Supports hierarchical metadata (nested objects) and automatic context propagation across async boundaries.

Validates LLM responses against configurable rules (output format, content policies, token limits, regex patterns) before returning to application. Implements guardrails that detect and filter unsafe content (profanity, PII, hallucinations) using pattern matching, keyword lists, or external validation APIs. Supports custom validation functions and can automatically retry requests that fail validation with modified prompts or parameters.

Emits webhook events at key points in the request lifecycle (request_started, request_completed, request_failed, cache_hit, retry_attempt) to external systems. Webhooks include full request/response context and metadata, enabling real-time integration with external monitoring, analytics, or workflow systems. Implements webhook retry logic with exponential backoff and dead-letter queue for failed deliveries.