GridQuery: Low-Cost Aggregated US Electricity Data API for Developers and Quants
Public US electricity market data is painful to use because it is fragmented across seven separate operator systems with different formats and restrictive rate limits. Meanwhile, existing commercial alternatives are locked behind enterprise sales calls and cost over $1,000/month, pricing out early-stage builders.
Technical founders spend months building complex technical products (like aggregated data APIs) in isolation without implementing any distribution or marketing, leading to zero discovery or adoption by potential users.
FREQUENCY
Multiple repeated complaints in the post and comments.
INTENSITY
Users explicitly describe existing tools as bloated/overkill and mention workaround behavior.
PAIN TRIGGERS
Founders expect product discovery or validation without having done any marketing or distribution.
Public US electricity market data is highly painful to use because it is scattered across seven systems with varying formats and rate limits.
Existing commercial platforms for energy data are prohibitively expensive for early-stage users and locked behind enterprise sales barriers.
Generic marketing pages fail because they don't target a specific buyer persona or show a clear path to production scale.
7 months building an electricity-data API. Stripe went live last week. 0 customers. What am I missing?
SaaS36
That buyer is probably not browsing Product Hunt for this. They're Googling a weird ISO acronym at 11pm because their script broke.
comment
$0 is expected if this is the first place you've mentioned it. That doesn't prove the product is bad, it just means the people with the painful version of this problem haven't been put in front of it yet.
I'd narrow the page around one buyer/use case before worrying too much about price. "US electricity market data API" is accurate, but it makes the visitor decide if they're a quant, energy analyst, climate startup, newsletter/data journalist, etc. Pick the one person most likely to search for this and write the page for them.
For distribution, I wouldn't start with generic SaaS marketing. Make the annoying public-data pain visible:
- "How to pull CAISO prices without fighting the portal"
- "ERCOT vs PJM API/data format differences"
- "Free ISO electricity price sample dataset"
- tiny example projects using your API in Python/Sheets
That buyer is probably not browsing Product Hunt for this. They're Googling a weird ISO acronym at 11pm because their script broke.
$29/mo doesn't read as toy to me. It reads like a developer-friendly wedge. The thing I'd add is a clear "when you're in production" path: limits, uptime expectations, support, and what happens if someone needs more than the starter plan. Serious users don't need enterprise theater, but they do want to know the ceiling isn't secretly one support email away.
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STAGE 02 · CUSTOMER
Who feels this pain?
TARGET USERS
solo technical foundersEarly Stage Climate Tech Developers And Quants
Software engineers and data analysts building infrastructure, financial, or climate applications requiring real-time and historical US ISO grid data.
Context
Get the first paying customers for a newly launched electricity-data API by reaching the right developer or analyst audience and making the landing page look trustworthy.
Developers build their own custom scrapers and fight with individual ISO portals and rate limits.
Searching Google for niche ISO technical acronyms and error solutions at late hours when custom internal scripts break.
Current Workarounds
Building and maintaining brittle custom scrapers for seven individual ISO portals
Dealing with scattered data formats, inconsistent rate limits, and frequent script breakages
Manually querying public portals or searching for obscure ISO acronyms late at night
3
STAGE 03 · MARKET
Where's the gap?
EXISTING SOLUTION GAPS
Public ISO portals are fragmented, difficult to scrape, and have restrictive rate limits.
Commercial energy data platforms require expensive $1,000+/month subscriptions and friction-filled enterprise sales calls, shutting out developers, quants, and early startups.
OPPORTUNITY & VALUE
Why Now
High agreement that public ISO data is notoriously difficult to scrape and normalize, matched with equal frustration over the steep pricing barrier of existing commercial energy data networks.
Value Proposition
Unlike expensive enterprise incumbents that require a sales call and $1,000+/mo commitments, GridQuery is 100% self-serve, documentation-first, and priced specifically for developers, indie hackers, and early-stage quants.
Product Direction
A self-serve, low-cost unified API that aggregates, cleans, and normalizes public electricity data from all seven US ISOs with a transparent, developer-friendly tier.
4
STAGE 04 · BUSINESS
How does it make money?
MONETIZATION
$49/moDeveloper tier up to 50,000 requests/mo
Model
SaaS subscription
WILLINGNESS TO PAY
Users are spending valuable hours fixing broken scrapers late at night. Spending $49/mo is significantly cheaper than engineering hours spent dealing with fragmented public ISO data and restrictive rate limits.
5
STAGE 05 · EXECUTION
How do you ship it?
MVP PLAN
“All seven US electricity markets, cleaned and unified into a single API starting at $49/mo.”
A self-serve, low-cost unified API that aggregates, cleans, and normalizes public electricity data from all seven US ISOs with a transparent, developer-friendly tier.
Core Features
Unified REST API normalizing data across all 7 US ISO networks
Real-time and historical pricing, load, and generation data streams
Developer-first documentation with copy-pasteable curl and Python examples
Self-serve Stripe billing with an instant API key generation flow
Weekly Roadmap
1
W1-W2
Core ingestion pipelines for top 3 ISOs functional and normalized in central database.
•Build ingestion scrapers for PJM, ERCOT, and CAISO
•Design unified relational database schema for pricing and load data
•Create internal automated tests to validate incoming data cleanliness
2
W3-W4
Complete remaining ISOs and wrap database in clean, documented REST API.
•Build ingestion scrapers for NYISO, MISO, ISONE, and SPP
•Develop lightweight API service layer with authentication keys
•Write clear API documentation with code snippets for Python and Node.js
3
W5
Integrate billing, rate-limiting, and launch a landing page optimized for long-tail SEO.
•Implement Stripe Checkout and API key authorization middleware
•Deploy global rate limiting per tier
•Build programmatic landing pages targeting niche ISO acronyms and data types
4
W6
Public launch on developer-centric networks and direct outreach to target users.
•Launch on Hacker News, r/datasets, and r/energy
•Directly message users on GitHub who maintain open-source or broken ISO scrapers
•Monitor API reliability and conversion metrics for the first 10 paid accounts
Launch Strategy
Target developers directly via programmatic SEO optimizing for obscure ISO technical acronyms and error codes. Launch and answer highly specific data questions on Hacker News, StackOverflow, and niche subreddits like r/datasets and r/energy.
RISKS & ASSUMPTIONS
Top Risks
Upstream Data Pipeline Brittleness
ISO operators regularly update their web portals or API schemes without notice, which could break ingestion pipelines and damage API reliability.
SEV 4
Data Egress Cost Cannibalization
Quants demanding large historical dumps might generate bandwidth and database costs that exceed their $49/mo subscription fee.
SEV 3
Invisible Target Audience Distribution
Energy analysts and developers do not frequent generic product platforms; distribution depends entirely on catching them via search exactly when their scripts break.
SEV 4
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STAGE 06 · DECISION
Should you build it?
NEED A CLEARER CALL?
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
This opportunity scores well above the median for ideas surfaced by MonetScope, with a validation sub-score of 8/10 against 3 independently sourced evidence signals. A "strong" rating in this band typically means the pain signal is consistent and recurring across multiple discussions, but one of the three pillars (severity, willingness to pay, or competitor weakness) is somewhat softer than top-tier opportunities. Founders evaluating this should focus customer discovery on the softest pillar first — confirming the gap before committing engineering time to a build.
Why this matters for SaaS founders
It sits at the intersection of "analytics", "api", "climatetech", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
Frequently asked questions
Is "GridQuery: Low-Cost Aggregated US Electricity Data API for Developers and Quants" a real validated startup idea or just an AI-generated suggestion?
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
How recent is the underlying data for analytics?
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
What's the difference between "overall score" and "validation score"?
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
