Same model, same quality. 71% cheaper token bill.

All your favorite AIs for about 71% cheaper. Tok Net AI is your drop-in API. It does much more than just reduce costs. See all features down below.

One-line SDK swap OpenAI · Anthropic · Google · open weights

toknet.ai8.4B tokens saved with us

Without Tok Net AI 41.8M tok

With Tok Net AI 9.2M tok

Spend avoided $4,113/day

quality floor: 97.2% held p50 overhead: 38 ms

There are already teams shipping with us today, and these are the numbers we achieved:

71%

Median token reduction across production accounts

4.2×

More usable context inside the same window

38ms

Added latency at p50, including routing

99.96%

Gateway uptime over the last 12 months

How it works

Three lines of setup, then it just gets cheaper.

No rewrites, no prompt migration, no proxy you have to babysit. Point your existing client at the gateway and keep shipping.

Swap the base URL

Keep your SDK, your model names, your streaming. Only the endpoint changes.

client = OpenAI(
  base_url="https://gw.tok-net.de/v1",
  api_key=TOKNET_KEY,
)

We compress and route

We do our magic.

How we do it

You watch the bill drop

Per-feature cost, every compression diff, every routing decision.

week 1  $18,402 → $5,338
evals   97.4% → 97.9%
alerts  0 floor breaches

Token & cost optimization

Our layer between your prompt and the invoice.

Every request passes through our systems before it reaches a model. Quality floors and budgets sit on top, so cheaper never means worse.

$100 of today's inference bill−71% end to end

$29 you still pay $71 never spent

quality floor held

Context optimization

Rephrasing model

Semantic caching

Cost-aware sub-agents

Quality floors

A minimum eval score per route. Nothing is ever traded below it.

Never more than the original API

What the provider would have charged you directly is a hard ceiling. We only bill a share of what we save you, so every request comes out below the original price. Never above it!

And ten smaller ways the bill goes down

Provider prompt-cache pass-throughReuse the provider's own cached prefix
Batch API discountsNon-urgent jobs go at the batch rate
Output token capsStop a runaway answer before it bills
Agent loop capsA stuck agent cannot spend all night
Tool schema slimmingUnused tool definitions come off the request
Embedding cacheNever embed the same text twice
Retry cost avoidanceA failed call does not bill you twice
Duplicate request collapsingIdentical concurrent calls share one answer
Cheaper-tier pinningPin routes that never needed the big model
Per-feature token accountingSee which feature is actually expensive

The platform.

Savings are the reason teams switch. This is the reason they stay: one gateway that already does the boring, load-bearing work. Open a category to see inside it.

Universal APIOne endpoint and one key in front of every provider, with the failure handling already written 10

200+ models, one schemaEvery provider behind a single request format
Drop-in OpenAI compatibilityKeep your existing SDK and model names
Chat, responses, embeddingsThe endpoints your app already calls
Batch & fine-tuning endpointsLong-running jobs at the cheaper batch rate
Fallback logic across providersA second provider takes over on error
Load balancingSpread across keys, regions and deployments
Retries with backoffTransient failures retried before your app sees them
Latency & least-busy routingEach call goes to the fastest healthy endpoint
TPM / RPM rate limitsStay inside provider quotas without hand-tuning
Tag-based routingRoute by team, feature or environment

ObservabilityLogs, traces, feedback, metadata, filters and alerts, for every call rather than a sampled slice 10

Request-level logsEvery call, not a sample of them
OpenTelemetry tracesSpans that slot into your existing tracing
Prometheus metricsScrape latency, errors and spend directly
Spend dashboardsCost broken down by day, model and endpoint
Attribution per key, team, user, tagKnow exactly which feature spent what
Custom metadata & filtersAttach your own fields and query on them
Feedback and eval scoresSend quality signals back against a request
Compression diff per requestSee what was removed and what stayed
Alerts to Slack, email, PagerDutyKnow when spend or errors move
Export to your warehouse or bucketStream raw logs to where you keep data

GuardrailsModel-side and partner guardrails, on the request going out and the response coming back 10

PII detection & maskingNames, emails and card numbers redacted in flight
Prompt-injection detectionCatch instructions hidden in retrieved content
Content moderationBlock unsafe input and output per policy
Blocked words and patternsYour own deny list and regex rules
LLM-as-a-judge checksA second model scores the answer before it ships
40+ partner guardrailsPlug in the vendor you already use
Tool permission policiesDecide which tools an agent may call
Bring your own guardrail APIPoint at your own service over HTTP
Pre- and post-call hooksRun checks going out and coming back
Enforced per key or per teamDifferent rules for different consumers

Prompt managementPrompts stop living in application code and start behaving like anything else you ship 4

Versioned templatesEvery change is a version you can point at
Typed variablesFill slots without string-concatenating prompts
PlaygroundTry a change against real models before shipping
Shared prompt libraryOne place instead of six codebases

Team memoryShared context your whole team's apps and agents read from, instead of each one relearning the same facts 10

Named memory entriesFacts stored under a key you choose
Per-user and per-team scopePrivate notes or shared team knowledge
Injected as request contextPulled into the prompt automatically
Full CRUD over the APICreate, read, update and delete programmatically
Markdown valuesWrite it the way the model reads best
Metadata tagsAttach your own structure to each entry
Visibility rulesPeople only see what their scope allows
Author and timestamp per editKnow who changed a fact and when
Survives sessions and restartsNot tied to a conversation or a process
Shared across agentsEvery agent starts from the same context

Pricing

You pay a slice of the savings. Never more.

Two ways to use Tok Net AI. Pick one to see how it is priced.

Drop-in proxy

One line changes. Nothing else does.

Your app

Tok Net AI gateway

compress · cache · route

Any model

GPT Claude Gemini Llama +200

Savings share

We bill 15% of the spend we take off your bill. Nothing else. You get the whole platform for free.

Promise: you always save money with us = You never pay more than you would have paid for the original API.

No seat fees, no minimums
Saving measured per request, auditable
Every provider through one key
Routing, caching, floors, budgets included

Self-hosted, private VPC and custom compressors: talk to us about Enterprise.

FAQ

The questions we actually get asked.

What if we don't save anything?

Then you pay us nothing. We bill 15% of the saving we measure, so no saving means no invoice, and the fee can never exceed the saving itself. That is the promise: your bill with us is never higher than the same traffic sent straight to your provider.

How do you work out what we saved?

Per request. We price the call as it would have run unchanged at your provider's list rate, subtract what it actually cost after compression, caching and routing, and bill 15% of the difference. Every line sits in the dashboard with the prompt diff and the model that answered it, and the whole ledger exports to your warehouse, so you can check our arithmetic against your provider invoice.

Will the answers get worse?

No. We have a quality floor of 97.2%. There will never be a request where we deliver a response that is below this threshold.

How much of our code has to change?

One line: the base URL. Your SDK, model names, streaming and tool calls all stay as they are, and you keep using your own provider keys. Compression and caching need no configuration, so the first reduction usually shows up within the hour; routing gains arrive after the first eval run, typically two or three days in.

Do you train on our prompts?

No. We do not train models on your traffic.

Which providers and models are supported?

Anthropic, OpenAI, Google, Mistral, and any OpenAI-compatible endpoint, including vLLM and Ollama behind your own network. Routing pools are yours to define — you choose which models are eligible, and we never route outside that set.

Get started

Start saving money today.

And not just that. You will get all the features from our platform for free.