Opportunities › Batch processing for cost-constrained AI workloads › Proposed app
Tidepool Batch
Queue big AI jobs and send each to the cheapest model that does the work
- Score
- 40
- confidence 41
- Evidence
- 2
- items on the page
- Rivals
- 0
- named in sources
- First version
- 40 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2024
- New market · AI estimate
ScreensConcept
Desktop app: Teams configure tasks, route models, review costs and results, and monitor batch jobs across the day from their workspace. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New batch
Ready to start$41.20
About this appAI proposal
Tidepool Batch is a job queue for teams that run the same AI task hundreds or thousands of times. You define a task, upload the items, and set a quality bar. It sends routine items to a small model running on your own machine or server, and hard items to a cloud API.
It is built for software teams who hit quota limits or high per-token costs on cloud APIs and are now setting up smaller open-source models by hand. Tidepool replaces the scripts, overnight runs and spreadsheets with one queue, one result set and one cost view.
Unlike a plain API wrapper, it shows what each task type costs on each model, and how often the small model's answer matched the larger one on a sample. You choose the routing from that evidence, not from a guess.
- Tagline
- Big batches, small bills.
- Voice
- PlainPracticalCalm
- Why this name
- A tidepool is a small, contained place where a lot of work happens quietly. It fits small local models handling big volumes.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Task queue with resumable runs
Upload thousands of items as a file or through the API. Runs survive crashes and quota errors, retry safely and pick up where they stopped.
No member yet
Local and cloud routing rules
Send each task type to a local model, a cloud API, or local first with cloud fallback when confidence is low.
No member yet
Side-by-side model trials
Run a sample of 50 items on two models and compare answers, time and cost before you commit a whole batch.
No member yet
Cost and quota view
See spend per task type, per model and per run, plus how close each cloud key is to its rate limit.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F3D4CDeep Tide · primary#F2F7F5Sea Foam · surface#12232BInk · text#E8664ACoral · accent#3E8E6BKelp · data, savings#E6D8B8Sand · secondary surface
Typography
Aa
Tidepool Batch
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test. Cloud API usage is billed by the provider on the customer's own keys, not by Tidepool.
Starter
Free
- 1 local worker
- 500 items per month
- Model trials
- CSV export
Team
$49 a month
- 5 workers
- 25,000 items per month
- Routing rules
- Scheduling
- Cost and quota view
Scale
$199 a month
- Unlimited workers
- 250,000 items per month
- API access
- Review queue
- Priority support
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. Tidepool is a back-office tool. Its output goes into the customer's own systems, so use does not expose it to new buyers. Growth comes from content, community posts and direct outreach.
- Do they become customers?
- Nobody outside the team sees the product in use. Test whether posts on cost savings from real batch runs bring in teams that sign up and pay.
- First customers
- Small software teams of 3 to 15 engineers that run a repeated AI task such as tagging, extraction or summarising over thousands of items and already pay a cloud API bill.
- Where to reach them
- Hacker News and developer forums: Post worked examples of cost per thousand items, local against cloud, and invite readers to try a free trial run.
- Open-source model communities: Join threads where people ask how to move batch work off paid APIs, and offer a free setup guide.
- Direct outreach to engineering leads: Message leads who have publicly posted about API quota or cost problems and offer a guided pilot.
- First milestone
- Five teams each run a real batch of at least 1,000 items, and three of them pay for the Team tier within 60 days.
Why people already pay for this
- Public claimTeams running batch inference at scale hit subscription quotas or prohibitive per-token costs when processing hundreds or thousands of tasks.
- Public claimWhen API costs or quotas become prohibitive, teams investigate and switch to running smaller open-source models locally to process large workloads.
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 · Job queuePostgres-backed queue with retriesRuns of thousands of items need leases, retries and resume. Supabase Postgres can hold this at first.
- This app · Local worker agentSmall installable worker in a containerLocal models run on the customer's hardware, outside a PWA. The worker pulls jobs over HTTPS.
RisksAI inference
- TechnicalCustomers' hardware varies widely. Supporting many GPUs, operating systems and model formats in the worker could take more time than the web app.
- AIA small model may match a large one on a sample but fail on rare items. Agreement scores can give false comfort, so review and fallback must be strong.
- CommercialTeams that already built scripts may see little reason to pay. The evidence is public claims only and has not been verified with buyers.
- PlatformCloud providers may offer their own discounted batch pricing, which narrows the savings from routing.
- DataItems may hold sensitive customer data. Teams will want proof that it stays on their own servers and is not stored in the cloud.
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.