Govern LLMs · OpenAI-compatible · Native API & SDK
TokenSaver is the control plane for all your LLM traffic: enforce policies, access, and budgets; shrink token bills with semantic cache, RAG, and compression; and keep data safe with PII controls — in one predictable pipeline with full observability. Use the web console, point OpenAI-compatible clients at TokenSaver, or integrate with the native HTTP API and Python SDK— same pipeline, metering, and policies on every path.
Early Adopter Program — free plan to explore governed LLM at scale · One proxy · RAG, cache, and anonymization modules · Policies & spend controls · Works with OpenAI, Anthropic, Google, and more · Open-source Python SDK on PyPI (free for the community)
Early Adopter Program: request free access to discover TokenSaver — your feedback helps steer the product roadmap.
How you connect
Whether your teams live in chat UIs, IDEs, or custom backends, you land on the same TokenSaver path: cache, RAG, compression, PII, then the model — with org-scoped keys, quotas, and dashboards. Pick the surface that matches your stack; no duplicate policy layers.
https://api.tokensaver.fr/openai/v1Chat Completions, Responses API, models, embeddingshttps://api.tokensaver.fr/api/v1Pipelines, RAG, chats — Python SDK & custom appsNo-code · Chat tools
Connect LibreChat, Open WebUI, and other tools that accept a custom OpenAI base URL. Set base URL to …/openai/v1, use your TokenSaver API key as Bearer, and optional X-Tokensaver-* headers to tune cache, RAG, compression, and PII per request — no fork of your client required.
Console · Teams
The hosted app for operators and builders: governed chat, dashboard and spend views, model pricing, API keys, pipeline defaults, and in-product docs. Ideal when you want clicks, not config files — still the same pipeline the APIs run.
Open the consoleDevelopers · Code
POST /api/v1/pipelines/run, typed tokensaver-sdk on PyPI, and the same reference your workspace shows after sign-in. Best when you need fine-grained requests, agents, or CI — governance and metering match the console and OpenAI-compatible traffic.
Same governance, optimization, and security layer in front of OpenAI, Anthropic, Mistral, Google Gemini, and more — route and compare models without duplicating policies or losing cost visibility.
Built for the move from pilot to production: one control plane as usage, providers, and internal stakeholders grow.
OpenAI
Anthropic
Mistral AI
Google GeminiPlatform
Four capabilities that work together — not a pile of disconnected APIs. Configure once, override per request when you need to.
Product teams get business value fast; platform and security teams keep governance, spend control, and reliability as adoption scales.
A single workspace to tune cache thresholds, RAG depth, compression level, and PII policies — aligned with what the API actually runs.
Learn moreInstrument LLM traffic with OpenTelemetry-friendly traces and metrics across every pipeline step. Expose the same signals to Prometheus and Grafana for alerting, SLOs, and cost or latency dashboards your platform team already runs.
Learn moreIngest PDFs and retrieve grounded chunks with multimodal-ready storage — context that stays scoped to the right user and workspace.
Learn moreData sensitivity by design: automatic PII detection and configurable anonymization or redaction on every exchange, so regulated or personal content is stripped before it reaches model APIs. Harden data flows against accidental leakage to LLMs and downstream logs while you scale usage with confidence.
Learn moreIntelligent routing
Every call flows through the same modules — so you optimize tokens, enforce guardrails, and keep audits simple. Swap providers without losing cost signals or safety posture.
Get free accessLifecycle tools
Test overrides, inspect per-step metrics, and correlate runs with traces — low-friction operations for platform teams and app owners alike.
See use casesArchitecture
Embed anywhere your apps run — console, OpenAI-compatible clients, or native API / SDK — supervise usage centrally, enforce org boundaries, and keep provider keys off end-user devices.
Custom apps and agents use the native API; LibreChat, Open WebUI, and similar tools use the OpenAI-compatible base path — one product behind both.
Developers · Open source
tokensaver-sdk is open source (MIT) and free for the open-source community, published on PyPI. TokenSaver builds on LangChain and LangGraph to run governed LLM pipelines and adds a governance overlay on top — access control, budgets, semantic cache, RAG, compression, PII policies, and observability. The SDK is the HTTP client to that stack: same behaviour and limits as the console, whether you trigger runs from code or the UI.
Orchestration inside the platform leans on LangChain / LangGraph patterns; TokenSaver enforces policy and cost before traffic reaches commercial model APIs.
Use cases
Same pipeline, different jobs — customer-facing or internal.
Grounded answers with RAG, redacted transcripts with PII policies, and cache for repeat intents.
Benefit: Lower cost per ticket, faster replies.
Central keys, workspace isolation, and spend visibility across teams — without shadow IT keys.
Benefit: Governance without blocking builders.
Long-context compression before the model call, retrieval over your knowledge base, full run history.
Benefit: Deeper context, controlled spend.
Trace IDs, token and cost breakdowns, export-friendly metrics for FinOps and security reviews.
Benefit: One source of truth for LLM spend.
Trust
Guardrails and visibility that stay aligned as you scale traffic.
Fixed module order, plan-aware feature flags, and configurable PII strategies — reduce drift between environments.
Org-scoped provider keys, user-scoped cache and retrieval, and masking before payloads leave your policy boundary.
Per-run steps, cache hit types, RAG attribution hooks, and OTel — so incidents are short and audits are boring.
Getting started
Register OpenAI, Anthropic, Google, or Mistral keys at org level. Applications call TokenSaver only, so provider credentials stay off app servers and client devices.
Outcome: one secure gateway for all teams and environments.
Define governed defaults for semantic cache, RAG, compression, and PII controls. Keep safe per-request overrides for advanced cases, without losing consistency.
Outcome: lower token cost, predictable behavior, faster delivery.
Track usage, spend, cache hit types, and step-by-step traces in one place. Detect anomalies quickly and provide audit-ready visibility for platform, security, and finance teams.
Outcome: confident rollout across products, teams, and providers.
A single API and policy layer in front of multiple model providers. Your applications call TokenSaver; TokenSaver applies cache, retrieval, compression, and safety, then calls the right model with your org's keys.
Yes. Configure the client's OpenAI base URL to point at TokenSaver's OpenAI-compatible path (…/openai/v1), and authenticate with your TokenSaver API key. The same governed pipeline runs as for the console and the native API; optional X-Tokensaver-* headers adjust cache, RAG, compression, and PII per request. Code examples: OpenAI-compatible docs.
The platform is designed for both cloud SaaS and self-managed deployments — keep data and keys inside your perimeter when required.
Runs record tokens, estimated cost, and cache outcomes so finance and engineering can reconcile usage without exporting raw logs.
Yes — cache, RAG, compression, PII, then LLM, then metrics. That predictability is what makes debugging and compliance reviews tractable.
Yes. tokensaver-sdk is open source (MIT), free to install from PyPI. It calls the TokenSaver HTTP API: the product uses LangChain and LangGraph as part of its pipeline implementation and layers governance (cache, RAG, compression, PII, quotas, observability) on those foundations — you integrate from Python without re-implementing that stack yourself.
