tiyuvta inference
tiyuvta inference is a prepaid API endpoint that serves the Qwen 3.8 27B language model via OpenAI-compatible requests. Agencies provision an API key, set a prepaid credit balance, and pay per token consumed: 0.38 USD per 1,000,000 input tokens, 0.2 USD per 1,000,000 cached input tokens, and 2.6 USD per 1,000,000 output tokens. The service includes auto top-up to prevent service interruption at zero balance, exact token usage reporting per request, and streaming response support. No monthly subscription, no per-request fees, and no minimum spend apply.
tiyuvta inference is an AI infrastructure platform, integrating with OpenAI, Paddle, Google, and GitHub. InnovaAI scores it 4.5/10 for agency adoption, best for Developer, Project Manager, and Founder roles.
Agency Audit
tiyuvta inference is an OpenAI-compatible API endpoint that runs Qwen 3.8 27B, a 27-billion-parameter language model, on prepaid credits with no subscription or minimum spend. Agencies building internal AI-powered tools, automating client workflows, or prototyping LLM features can adopt it to reduce per-token inference costs and avoid vendor lock-in through OpenAI compatibility. The transparent per-token pricing, cached input discounts, and auto top-up system make it suitable for teams that want predictable LLM costs without monthly commitments.
3recommended
12/mo
No paid plan published
Low
Illustrative scenario. Not a guarantee. Net capacity needs a verified paid base plan, and none is published for this service, so it is not modeled. Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Developer handling internal AI tool development
- Project Manager handling LLM feature prototyping
- Founder handling infrastructure cost forecasting
- Your team exclusively uses GPT-4, Claude, or other closed-model APIs and has no use case for Qwen 3.8 27B, making the integration effort unjustified.
- Your developers lack API integration experience or your stack does not support OpenAI-compatible endpoints, requiring significant refactoring to adopt tiyuvta inference.
- Your LLM usage is sporadic or under 100,000 tokens per month, making the upfront credit purchase and account management overhead disproportionate to the cost savings.
Internal Adoption Path
No paid plan published
12 hr/mo
3 seats × 4 hr each
$900/mo
modeled at $75/hr labor rate
No paid plan published
Illustrative scenario. Not a guarantee. No verified paid base plan is published for this service, so subscription cost and net capacity are not modeled. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of tiyuvta inference
OpenAI-compatible API endpoint
Developers can swap the base URL and model name in existing OpenAI integrations without rewriting client code. Reduces migration friction for teams already using OpenAI SDKs or libraries.
Per-token metered billing with no minimums
Agencies pay only for tokens consumed, with no monthly subscription or seat-based fees. Eliminates unused-capacity waste and makes cost forecasting straightforward for project managers tracking infrastructure spend.
Cached input pricing for repeated prompts
Prompt tokens served from cached prefixes cost 0.2 USD per 1,000,000 tokens instead of 0.38 USD, automatically applied with no configuration. Reduces inference costs for workflows that reuse system messages or document context across multiple requests.
Prepaid credit system with auto top-up
Teams buy credit packs upfront and set automatic top-ups to avoid API failures at zero balance. Operations teams gain predictable monthly spend without surprise invoices or manual recharge cycles.
Exact token usage reporting in standard format
Every response includes input, cached input, and output token counts in the standard usage field, including streaming responses. Developers and PMs can audit costs per request and optimize prompts based on real consumption data.
Qwen 3.8 27B language model
A 27-billion-parameter open-weight model suitable for summarization, classification, and content generation tasks. Agencies can evaluate Qwen's output quality for client projects before committing to production deployments.
What Makes tiyuvta inference Different
Unique advantages vs similar tools in this niche
Transparent per-token pricing with no rounding up
vs Other providers that round up to nearest thousand tokensArithmetic only. Your bill is what the meter counted, with no rounding up to the nearest thousand tokens.
Cached input pricing automatically applied
vs Providers that charge full input rate for repeated prefixesCached input at $0.20 vs $0.38 input, applied automatically with no cache-write fee.
Prepaid credit with no expiry and no subscription
vs Subscription-based LLM APIs with monthly feesCredit does not expire and is not a subscription. No plan, no minimum, no monthly fee.
Value Equation
Outcome-likelihood-time-effort assessment for tiyuvta inference
Limited agency channel
tiyuvta inference scored below the agency-resellability threshold (agency_fit_score < 50). The Value Equation projects agency-side outcomes, which don't apply to tools without a clear resell pathway.
Contact tiyuvta inferencePricing
tiyuvta inference platform cost to your agency
Pay as you go
- No monthly subscription required
- Pay only for what you use — see per-unit rates below
- Cancel anytime, no contract lock-in
How usage-based pricing works
tiyuvta inference charges per consumption unit (per 1,000,000 cached input tokens). Below are the component rates the vendor publishes. Each row is a separate charge: your total cost combines them based on your configuration and volume. Component rates range from $0.20 per 1,000,000 cached input tokens.
Final agency cost = (sum of selected component rates) × client usage volume. Confirm a usage estimate with each client before quoting.
Component Rates
Cost per unit: total depends on your configuration and volume
Add-ons
Optional extras priced on top of any main plan
No verified white-label program for tiyuvta inference: client-facing delivery runs under the platform's native branding.
Market Intelligence
Offer + scale economics for tiyuvta inference
Limited agency channel
tiyuvta inference scored below the agency-resellability threshold (agency_fit_score < 50). It's a useful tool but not designed for white-labeled or retainer-based reselling, so we don't publish productized offer economics for it.
Contact tiyuvta inferenceInvestment Decision Framework
Strategic vetting analysis for tiyuvta inference
Situational Fit
Fit depends on your client mix
Buy If
4Your developers spend 10+ hours per month integrating LLM inference into client-facing tools and want to reduce per-token costs by switching from OpenAI's standard pricing to tiyuvta's metered model.
Your strategists or PMs prototype AI features for client projects and need a low-friction, no-commitment API to test Qwen 3.8 27B outputs before recommending it to clients.
Your operations team manages multiple LLM integrations and wants a single prepaid credit pool that works across all models on the roster without per-model subscriptions or minimum spends.
Your founders are building an internal AI assistant or knowledge-base tool and need transparent per-token billing to forecast infrastructure costs without surprise overage charges.
Skip If
4Your team exclusively uses GPT-4, Claude, or other closed-model APIs and has no use case for Qwen 3.8 27B, making the integration effort unjustified.
Your developers lack API integration experience or your stack does not support OpenAI-compatible endpoints, requiring significant refactoring to adopt tiyuvta inference.
Your LLM usage is sporadic or under 100,000 tokens per month, making the upfront credit purchase and account management overhead disproportionate to the cost savings.
Your team requires HIPAA, SOC 2, or other compliance certifications that tiyuvta inference does not publicly document, creating legal or contractual blockers.
Bottom Line
tiyuvta inference is an OpenAI-compatible API endpoint that runs Qwen 3.8 27B, a 27-billion-parameter language model, on prepaid credits with no subscription or minimum spend. Agencies building internal AI-powered tools, automating client workflows, or prototyping LLM features can adopt it to reduce per-token inference costs and avoid vendor lock-in through OpenAI compatibility. The transparent per-token pricing, cached input discounts, and auto top-up system make it suitable for teams that want predictable LLM costs without monthly commitments.
Reality Check
tiyuvta inference requires your team to manage API credentials and integrate it into existing development workflows, which adds friction if your stack is already built on OpenAI. The model roster is limited to Qwen 3.8 27B and Step-3.7-Flash in bring-up, so teams needing GPT-4 or Claude variants must maintain dual integrations.
Low effort: self-service setup with guided onboarding
Academy for tiyuvta inference
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
tiyuvta Inference Agency Implementation, Cost-Optimized LLM Delivery
Learn how to architect and deliver AI-powered client projects using tiyuvta's prepaid token billing model. This course covers API integration, cost forecasting for retainers, leveraging cached input pricing to reduce infrastructure spend, and building transparent usage reporting dashboards that justify ongoing fees to clients.
Open the courseNo Academy modules are published for this service yet. Browse the full Academy
Core concepts
The mental model you need to price and scope the work.
- Inference Cost Pass-Through CeilingConcept
Inference Cost Pass-Through Ceiling is the point at which an agency can no longer absorb a model provider's price or latency change inside a fixed retainer, so the cost has to move to the client or the work has to shrink. The framework asks three questions per client engagement: what share of delivery cost is metered inference, how fast can that share be re-routed to a cheaper model, and what contract language lets you reprice. Forrester's 2027 predictions flag AI growth colliding with energy and infrastructure limits, which converts compute scarcity into API price movement on agency tools. A concrete case: an agency running document analysis on a frontier API can shift bulk classification to a smaller open-weight model served through Ollama or a gateway like Helicone, keeping the frontier model only for reasoning steps. That split is the ceiling defense.
- Provider Substitution WindowConcept
Provider Substitution Window is the interval during which an agency can move a client workload from one model provider to another without rewriting prompts, evals, or integration code. The window is widest at the orchestration layer and narrowest at the fine-tuned weights layer: a gateway swap takes hours, a retrained model takes a quarter. Agencies that measure this window per client account know exactly when they hold pricing leverage and when a vendor holds it. Forrester's 2027 predictions flag compute and energy constraints pushing API pricing upward, which turns a wide substitution window into a margin defense rather than an engineering nicety. A concrete case: an agency routing Claude and GPT traffic through a gateway such as Helicone or Portkey can shift a client's summarization workload in an afternoon when one provider raises rates, while a competitor with hardcoded SDK calls absorbs the increase on a fixed retainer.
- Margin Defense StackConcept
Margin Defense Stack treats AI infrastructure as a layered cost structure rather than a single line item. The bottom layer is raw compute and API tokens, the middle layer is routing and caching, and the top layer is the client-facing retainer price. Agencies that only negotiate the top layer absorb every shock from the layers beneath. Forrester's 2027 predictions flag that AI expansion is colliding with energy and infrastructure limits, which translates into API price increases for agency tools and compresses margins on AI-inclusive retainers. A concrete defense: route repeat prompts through a gateway such as Helicone or Portkey so cached responses cut token spend before it reaches the client invoice, and keep a local fallback like Ollama for privacy-sensitive work. When a client asks why the AI retainer costs what it does, the stack shows exactly which layer each dollar covers.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- When AI Margins Depend on Third-Party Compute, Price the Dependency Before You Sign the RetainerEvaluation Rule
Map every AI dependency in the delivery stack to a named provider, a fallback route, and a pass-through cost clause before quoting fixed-fee client work.
- AI Infrastructure Rule: Route Across Providers Before You Standardize on OneEvaluation Rule
Put a routing or gateway layer between your application and every model provider before any client deliverable depends on one vendor's endpoint.
- Multi-Model Orchestration vs Single-Provider CommitmentDecision Framework
IF client work spans more than one model family, more than one pricing tier, or more than one data-residency requirement, THEN route every request through an orchestration layer so a provider price change or capability shift becomes a routing edit rather than a rebuild. IF a single provider's model is the product itself and switching cost is already sunk into fine-tunes and evals, THEN a direct integration is cheaper and simpler than adding a gateway. The frame is not which vendor wins; it is whether the agency owns the routing decision or rents it.
- The Single-Provider Lock-In Trap in AI InfrastructureFailure Pattern
- The Token Bill Creep: Why AI Infrastructure Costs Outrun Agency RetainersFailure Pattern
Delivery system
Blueprints and procedures for running it as a service.
- Multi-Model Routing Layer Build (10-14 days)Implementation Blueprint
A delivery pattern for agencies that stand up a provider-agnostic routing and observability layer between client applications and frontier model APIs, so pricing changes, deprecations, or safety-policy shifts at any single lab become a config edit rather than a rebuild.
- Model Routing and Failover Drill (QA)Operating Procedure
- Multi-Provider Cost and Lock-In Review (Retention)Operating Procedure
- Provider Onboarding and Credential Isolation (Onboarding)Operating Procedure
13 modules selected for tiyuvta inference
Frequently Asked Questions
Answers about pricing, setup
tiyuvta inference offers a free plan; paid pricing is not published publicly.
Input tokens cost 0.38 USD per 1,000,000 tokens. Cached input tokens cost 0.2 USD per 1,000,000 tokens. Output tokens cost 2.6 USD per 1,000,000 tokens. There is no per-request fee, no monthly subscription, and no minimum spend. You buy credit packs upfront and requests draw down the balance at the metered rate. New accounts receive free credit on sign-up.
Developers and technical leads benefit most by integrating Qwen 3.8 27B into client-facing tools or internal AI assistants without OpenAI lock-in. Project managers and operations teams gain cost visibility and predictable billing for infrastructure planning. Strategists and founders can prototype AI features and evaluate Qwen's output quality before recommending it to clients.
Time savings depend on your current workflow. If your developers currently spend 2+ hours per week managing OpenAI integrations or cost overages, switching to tiyuvta inference's transparent metering and auto top-up system reclaims roughly 1 to 2 hours per month in billing administration. If you are prototyping new AI features, the no-commitment API eliminates approval cycles, saving 3 to 5 hours per project kickoff.
No. You can create an account with a one-time sign-in link via email, Google, or GitHub with no password or upfront payment. Free credit is added on sign-up, allowing you to test the API before purchasing additional credit packs.
The API returns a 402 status code instead of processing the request. If you have auto top-up enabled, a new credit pack is purchased automatically before the balance hits zero, preventing service interruption. Without auto top-up, you must manually purchase credit to resume requests.
Yes. Credit is not tied to a specific model. The same prepaid balance pays for Qwen 3.8 27B, Step-3.7-Flash, or any future models added to the roster. When new models launch, existing credit automatically becomes usable without migration steps.
When a request reuses a prompt prefix from a previous request, those tokens are served from cache at 0.2 USD per 1,000,000 tokens instead of the standard 0.38 USD input rate. Caching is applied automatically with no configuration or cache-write fees. The usage field in every response breaks down cached versus fresh input tokens so you can see the savings.