Opportunities › Inference API for open-source and fine-tuned models › Proposed app
Tidepool
Per-token inference for open-source and fine-tuned models. No idle GPU bills.
- Score
- 54
- confidence 61
- Evidence
- 10
- items on the page
- Rivals
- 4
- named in sources
- First version
- 60 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2021
- Emerging market · AI estimate
ScreensConcept
Desktop app: Software teams manage API endpoints, billing, and model benchmarking through dashboards and comparison tables requiring sustained focus. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New endpoint
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About this appAI proposal
Tidepool is a managed inference API for open-source and fine-tuned models. You upload or pick a model, call an OpenAI-style endpoint, and pay for input and output tokens only. There are no reserved GPUs and no capacity fees when traffic is quiet.
It is built for software teams that run custom or fine-tuned models and are stuck between two options: managed providers that bill for always-on GPUs, or DIY setups where you configure vLLM yourself and live with slow cold starts.
Tidepool tunes its runtime for specific accelerator hardware and keeps popular weights warm, so you get managed convenience with usage-based cost. A cost and latency view per model shows what each deploy really costs you.
- Tagline
- Pay for the tokens you use. Nothing for the time you don't.
- Voice
- PlainPreciseCalm
- Why this name
- A tidepool fills when water arrives and sits still when it leaves. Usage-based capacity with no idle cost.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Pure per-token billing
Input and output tokens are priced separately. Nothing is charged for idle time, reserved capacity or standby replicas.
No member yet
Bring your fine-tune
Upload adapters or full weights from your own training run and get a private endpoint in minutes, without writing serving config.
No member yet
Warm-start routing
Models you call often stay loaded, and rarely used ones load fast, so first-request delay is visible and tracked rather than hidden.
No member yet
Drop-in compatible API
Use the familiar chat and completions request shape, so most apps switch by changing a base URL and a key.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0B3C49Deep Tide · primary#F2F7F5Sea Foam · surface#10242BInk · text#F26B4FCoral · accent#2E9E83Kelp · data#E3D9C6Sand · secondary surface
Typography
Aa
Tidepool
PricingAI proposal
The material gives no competitor prices. Token rates are not set here; they must be tested against real hardware cost. The Team fee is a starting point to test.
Free trial
Free
- Small starter credit
- Shared open models
- Community support
Usage
Free
- Per-token billing, input and output
- Private fine-tune endpoints
- Spend caps and alerts
- No idle or capacity fees
Team
$99 a month
- Everything in Usage
- Benchmark runs and shared dashboards
- Multiple projects and keys
- Priority support
Would you pay Team · $99 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- No loop in the product. Developers find it through content, benchmarks and community posts, then call the API from their own products. Their end users do not see Tidepool, so use does not spread by itself.
- Do they become customers?
- Reached developers are the buyers, but only through sales and content. Test whether public benchmarks and a free credit convert teams into paying usage.
- First customers
- Small AI product teams of 2 to 15 engineers that run one or two fine-tuned open models and now pay for idle GPUs or maintain their own vLLM setup.
- Where to reach them
- Hacker News and dev forums: Post a transparent benchmark and cost write-up on a real fine-tune workload, and answer questions in the thread.
- Open-model communities: Offer free credits in model-tuning Discords and forums where people share fine-tunes.
- Direct outreach: Message teams who publicly describe self-hosted inference pain and offer to migrate one model for free.
- First milestone
- Five teams move a production fine-tune to Tidepool and pay a metered bill for two consecutive months.
Why people already pay for this
- VerifiedHacker News post received 72 points and 37 comments
- Public claimEvery inference provider is either fast-but-expensive (always-on GPU costs) or cheap-but-DIY (requiring self-configuration and dealing with cold starts)
- Public claimTeams want managed inference without the tradeoff between cost and convenience
- Public claimIonRouter achieves significantly higher throughput on multimodal pipelines compared to Together AI
- Public claim
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · GPU computeRented accelerator capacity (e.g. GH200-class)Inference needs GPU hardware outside Vercel; the runtime is tuned for specific accelerators.
- This app · Inference runtimeOpen-source serving engine with custom tuningHandles batching, model loading and warm-start behaviour for open and fine-tuned weights.
RisksAI inference
- CommercialPer-token pricing with no idle fee means Tidepool carries the idle GPU cost. Margins depend on high utilisation, which is hard with few customers.
- TechnicalFast loading of many custom fine-tunes on shared hardware is difficult. Cold-start delay may remain for rarely used models.
- CommercialEstablished providers named in the material already serve this audience. Speed claims come from a public post and are unverified.
- PlatformDepends on access to scarce specific accelerators from cloud suppliers. Supply or price changes would hit costs directly.
- RegulatoryCustomers send prompts and proprietary weights. Data handling, retention and licence terms of open models need clear policies.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.