Credits flow through four product areas. Each area is shipped and working. The question is where to focus to drive expansion from $500/month to $5–10K/month per org.
| Product Area | How It Generates Credits | Current Evidence | The Opportunity |
|---|---|---|---|
| SQL Execution | Query seconds → query credits. Pipelines run daily. | Marinade: 54K credits in 35 days. CMC/Stockholm: high volume during trials. | Proven credit driver. Orgs building data pipelines consume 1,800–6,000 credits/day. |
| Chat | AI tokens + triggered queries. Sessions are one-off. | 6 orgs active. Laminated, Avalanche, Monad, Aleo exploring. 79% AI, 21% query. | Entry point for new orgs. Low credits today ($50–$200/mo) because sessions don’t recur. |
| Agentic CLI | Agent reasoning + iterative queries. 3–60x more credits than UI. | Internal power users: 300–500 credits/day. Zero external adoption. | Highest credits per user. An agent session triggers 3–10 queries that iterate and self-correct. |
| Automations incl. alerts, briefings, scheduled queries |
Recurring credits without human action. Runs on schedule. | A few orgs have automations — all created by Flipside team. | Best NRR lever. Converts one-time exploration into baseline recurring consumption. |
Two months from now. Here’s what we should be able to point to.
| Metric | Current | April ’26 Target | Why This Matters for $500K/Mo |
|---|---|---|---|
| Orgs at $500+/mo | Active orgs across tiers | 5 | First 5 prove the $500/mo floor works. These are the top of the funnel. |
| External Agentic CLI users | 0 external | 3+ | Highest credits/user surface. Must prove it works outside internal team. |
| Orgs using agents to build queries | 0 external | 3+ | Core thesis: agents building pipelines drives AI + query credits together. |
| Self-serve Automations | Built, team-created only | Self-serve | Recurring credits = NRR. Orgs must create their own automations. |
| Orgs using 2+ product areas | Orgs siloed in one area | 3+ | Cross-surface usage (SQL + Chat, SQL + CLI) = sticky, higher ARPU. |
Thesis: programmatic usage → credit consumption → revenue. Credits are consumed in two categories: AI tokens (chat completions, agent reasoning) and query execution (SQL seconds). Across 21 external orgs, 86% of all credits come from programmatic access (API, CLI, automated pipelines) — not from users clicking around in the UI. If we want to grow revenue, we need to invest in product that drives programmatic usage.
| Signal | Classification | What It Means |
|---|---|---|
session_id = NULL | Programmatic — Direct API | Query or AI call made outside any chat session. Pure API/SDK usage. |
meta.source = “cli” | “api” | Programmatic — CLI/MCP | Chat session initiated from CLI, Cursor, or MCP tool. Has a session, but not human-in-the-UI. |
meta.source = “web” | “agents-page” | Manual — Web UI | User in the browser, chatting with agents or running queries manually. |
| Burst queries (<2s gaps) | Programmatic — Pipeline | Rapid-fire queries at machine speed. Marinade: 90% burst rate. CMC: 98%. |
session_id.
After incorporating meta.source, his classification moved to 97.7% programmatic (233 of 298 sessions are CLI-originated,
plus all sessionless query events). This confirmed the heuristic works.
| Pattern | Orgs | % of Credits | Programmatic % | What They Do |
|---|---|---|---|---|
| API Pipeline | 3 | 80% | ~100% | Automated SQL pipelines via API. Machine-speed burst queries. Zero AI. High volume, low stickiness. |
| AI Chat | 6 | 13% | ~15% | Exploring data through agents in the UI. Human-paced. High AI %, low query volume. Undermonetized. |
| Mixed | 3 | 11% | ~60% | Both AI exploration and query execution. Closest to the target pattern. Highest stickiness potential. |
Credit consumption follows a clear hierarchy. Each step up the programmatic ladder multiplies revenue per user:
| Access Pattern | Avg Credits/Day | Rev Multiplier | Why |
|---|---|---|---|
| Manual UI only | 50–100 | 1x | Browsing, occasional chat. Human-speed ceiling caps consumption. |
| Manual chat + API queries | 200–350 | 3–5x | Chat for exploration, then direct API for extraction. Eric Stone’s pattern. |
| Agent-driven (CLI/Cursor/MCP) | 300–500 | 5–10x | One agent session triggers 3–10 downstream queries that iterate and self-correct. |
| Automated pipeline (API) | 1,800–6,000 | 20–60x | Machines run 24/7 at burst speed. No human bottleneck. |
| Organization | Credits | AI % | Qry % | Cred/Day | Est Rev/Mo | Pattern |
|---|---|---|---|---|---|---|
| Marinade Finance | 64,025 | 3% | 97% | 1,829 | $1,646 | Pipeline |
| CMC Capital SRL | 20,817 | 0% | 100% | 5,204 | $4,684 | Pipeline |
| Stockholm SSE | 18,066 | 0% | 100% | 6,022 | $5,420 | Pipeline |
| CoW Swap | 6,922 | 36% | 64% | 865 | $779 | Mixed |
| Laminated Labs | 6,242 | 91% | 9% | 223 | $201 | AI Chat |
| Near Foundation | 5,879 | 31% | 69% | 210 | $189 | Mixed |
| Avalanche | 3,591 | 81% | 19% | 257 | $231 | AI Chat |
| Monad | 2,684 | 71% | 29% | 112 | $101 | AI Chat |
| Aleo | 2,631 | 86% | 14% | 329 | $296 | AI Chat |
| Somnia | 1,302 | 74% | 26% | 93 | $84 | AI Chat |
| Karatage | 1,228 | 34% | 66% | 123 | $111 | Mixed |
| Flow | 824 | 16% | 84% | 59 | $53 | Light |
| Attestant | 628 | 88% | 12% | 45 | $41 | AI Chat |
| + 8 orgs under 500 credits | Circle, Solana, Ink, Polygon, Sui, Morpho, Kraken, Movement | |||||
The ideal customer uses AI agents to explore data and runs queries to extract it. Query-only users are high-volume but commoditized (any SQL warehouse will do). AI-only users are low-volume and substitutable (ChatGPT + a SQL tool). When both layers are engaged, AI drives queries and queries feed AI — a flywheel that’s hard to leave.
Two internal power users with completely different workflows independently converged on similar consumption patterns. This gives us the per-user building block — the question is how many users per org we can drive.
| Jim Myers — “Agent-First” | Eric Stone — “Explorer” | |
|---|---|---|
| AI : Query ratio | 52 : 48 | 44 : 56 |
| Credits / day | ~500 | 326 |
| Monthly projection | ~15,000 ($450) | ~9,800 ($294) |
| Workflow | CLI agents trigger cascading queries | Chat exploration, then direct API queries |
| Channel | 78% CLI/Cursor, 49% no-session queries | 11 chat sessions, 105 direct API queries |
| Input | Value | Basis |
|---|---|---|
| Credits per user per day | 300–500 | Eric = 326, Jim = 500. Conservative: 350. |
| AI : Query ratio | 40 : 60 | Eric = 44:56, Jim = 52:48. Midpoint. |
| Active days per month | 22 | Weekdays |
| Revenue per user per month | $198–$330 | 6,600–11,000 credits × $0.03 |
| Org Stage | Users | Cred/U/Day | Credits/Mo | Rev/Mo | Rev/Year | |
|---|---|---|---|---|---|---|
| Entry — small team | 2–3 | 350 | 16,700+ | $500 | $6,000 | Minimum. 2–3 users, team workflows. |
| Healthy — data team | 5 | 350 | 38,500 | $1,155 | $13,860 | Target. Pipelines + agents + analysts. |
| Strong — power team | 10 | 450 | 99,000 | $2,970 | $35,640 | Ideal. Multiple workflows, org-wide. |
| Enterprise — scaled | 20 | 500 | 220,000 | $6,600 | $79,200 | Enterprise. Deep integration across teams. |
Margins are identical (80%) on AI and query credits. The 40:60 target is about stickiness and moat, not profitability per unit. More AI usage compounds revenue — one agent session at ~$0.75 of AI credits triggers ~$1.20 of query credits.
| Bucket 1: Starting | Bucket 2: Graduated | Bucket 3: Enterprise | |
|---|---|---|---|
| Rev/mo | $500–$2K | $5–10K | $30–50K+ |
| Who | Small team, 2–3 users, running queries. | Data team, 8–15 users, agents + automations. | Fidelity-scale. Custom feeds, SLA, dedicated support. |
| How they arrive | Product-driven. Sign up at $500/mo floor. | Graduate from Bucket 1 by adding users & workflows. | Sales-driven. 6–12 month enterprise sales cycle. |
| What drives revenue | Volume. Many orgs at low ARPU. | Expansion. Fewer orgs, high ARPU. | Deal size. A few orgs, very high ARPU. |
An org enters at $500/month. The question is how many graduate to $5–10K and how long it takes.
| Milestone | Time from Entry | Users | Rev/Mo | What Triggers It |
|---|---|---|---|---|
| Entry | Month 1 | 2–3 | $500 | Small team sets up queries + AI sessions. |
| First expansion | Month 2–3 | 3–5 | $1–2K | More analysts join. Pipelines forming. Someone tries the CLI. |
| Graduated — Bucket 2 | Month 6–9 | 8–10 | $5–7K | Agents building pipelines. Automations running. Multiple daily workflows. |
| Scaled Bucket 2 | Month 9–12 | 10–15 | $8–10K | Full org adoption. Embedded in daily workflows. 10+ active users. |
Not every org graduates. Of every 10 orgs that enter at $500/month:
| Outcome | % of Entrants | Orgs (of 10) | What Happens |
|---|---|---|---|
| Churn | ~35% | 3–4 | Don’t find value. Gone in 2–3 months. |
| Stay in Bucket 1 | ~40% | 4 | Using the product at $500–$2K. Not expanding. 1–3 users. |
| Graduate to Bucket 2 | ~25% | 2–3 | Reach $5–10K/month. 8+ users. Agents + automations. |
| Assumption | Value | Why |
|---|---|---|
| Sales cycle | 6–12 months | Enterprise procurement. Legal, security, POC, then contract. |
| Pipeline start | Now (Feb ’26) | First close can’t happen before August at earliest. |
| Deal size | $30–50K/month | Custom data feeds, dedicated agents, SLA. Could be higher for Fidelity-scale. |
| Deals closed in 12 months | 2–4 | 1 by month 6–8, 1–2 by month 9–10, 0–1 by month 12. |
Assuming 10 new orgs/month average, 25% graduation rate, 6–9 month graduation time, 3 enterprise deals.
| Bucket | Orgs | Avg ARPU | Rev/Mo | How They Got Here |
|---|---|---|---|---|
| Bucket 1: Starting $500–$2K/mo |
~55 | $1,100 | $60K | Mix of recent entrants ($500) and growing orgs ($1–2K) that haven’t graduated yet. |
| Bucket 2: Graduated $5–10K/mo |
~10 | $7,500 | $75K | ~25% of the 60 orgs entering months 1–6, surviving and expanding over 6–9 months. |
| Bucket 3: Enterprise $30–50K/mo |
3 | $40K | $120K | Sales-driven. First deal by month 6–8, 2 more by month 12. |
| Total at Month 12 | ~68 | $255K | ||
| ARR | $3.1M | GP: $2.4M at 80% margin |
Three levers. Graduation rate matters most for the funnel; enterprise deals move the total fastest.
| Scenario | New Orgs/Mo | Grad Rate | Enterprise | Bucket 1 | Bucket 2 | Bucket 3 | Total |
|---|---|---|---|---|---|---|---|
| Conservative | 8 | 20% | 2 deals | $50K | $48K | $80K | $178K |
| Base case | 10 | 25% | 3 deals | $60K | $75K | $120K | $255K |
| Strong execution | 12 | 30% | 4 deals | $70K | $108K | $160K | $338K |
| Enterprise upside | 10 | 25% | 3 + 1 Fidelity | $60K | $75K | $200K | $335K |
| Stretch | 15 | 33% | 5 deals | $85K | $150K | $200K | $435K |
| B1 Orgs | B1 Rev | B2 Orgs | B2 Rev | B3 Rev | Total | What Has to Be True | |
|---|---|---|---|---|---|---|---|
| Today Feb ’26 |
early | — | 0 | — | — | — | 21 active orgs. Zero graduations. Zero enterprise pipeline. |
| Month 2 Apr ’26 |
5 | $4K | 0 | $0 | $0 | $4K | Gate. 5 orgs at $500+. CLI packaged. Enterprise pipeline started. |
| Month 4 Jun ’26 |
15 | $14K | 0 | $0 | $0 | $14K | Early orgs expanding to $1K+. Still too early for graduations. Enterprise conversations active. |
| Month 6 Aug ’26 |
25 | $30K | 1–2 | $8K | $40K | $78K | Checkpoint. First graduations happening. First enterprise deal closing. Graduation rate becoming visible. |
| Month 9 Nov ’26 |
40 | $48K | 5–6 | $38K | $80K | $166K | Graduation rate validated. 2–3 enterprise deals live. Earliest cohorts at $7–10K. |
| Month 12 Feb ’27 |
~55 | $60K | ~10 | $75K | $120K | $255K | Base case. 120 entered, ~65 active in B1, ~10 graduated to B2, 3 enterprise deals. |
Everything is built. The work is packaging, onboarding, and directing users toward the actions that drive credits. The single question for every initiative: does this convert data usage into AI usage?
Agentic CLI
The problem: The CLI works — Jim generates 500 credits/day through Cursor.
But zero external users have adopted it because it’s not clear what it is, how to set it up, or what you’d use it for.
There’s no onboarding, no setup guide, no “here’s what you get.”
Do: Setup guide + quick-start tutorial. Onboarding flow that gets a user from zero to first agent invocation in 10 minutes.
Clear packaging: “Access Flipside agents from Cursor, Claude Code, or any MCP-compatible tool.”
Measure: 3+ external Agentic CLI users by April.
SQL Execution Chat Agentic CLI
The insight: People come here for the data. Marinade is writing SQL by hand and spending $1,646/month.
They’re getting value — but doing the hard work themselves. The pitch: let agents build your pipelines.
An agent writes the SQL, iterates on it, deploys it as an automation. Faster for the user, and it drives both AI and query credits.
Do: Hands-on sessions with pipeline orgs. Demo the agent building a pipeline they already have —
“you wrote this in SQL, the agent does it in 30 seconds.”
Make “build with an agent” the suggested path inside the query interface.
Measure: At least 1 pipeline org with AI% >10% by April.
Automations
The problem: Automations are built and working — briefings, alerts, scheduled queries — but users don’t know what they’d create one for or how.
We provide examples but there’s no clear path from “I just ran a query” to “this runs every day and sends me a briefing.”
Every automation deployed was created by our team.
Do: Clear CTAs in the product after a query or chat session: “Want this as a daily briefing?” “Set up an alert when this crosses X.”
Templates for common use cases. Self-serve creation flow that doesn’t require our team.
Measure: 3+ orgs with at least one self-created Automation by April.
Why now: 5 orgs at $500+/month is the first proof point that the floor works as a business.
These need to be teams (2–3+ users) using multiple product areas, not individuals in one.
Do: Identify from existing pipeline (10 sub-$500 orgs) or net new.
Onboard them across SQL + Chat + CLI. Direct them — don’t give them five options, give them one path.
Measure: 5 orgs above $500/month by April.
Why: Most orgs have 1–2 users. The floor is $500/mo (2–3 users). Healthy is $1,155+ (5 users).
Every additional user is linear ARPU growth. New users should land in the product area that fits their role:
analysts into SQL + Chat, engineers into the Agentic CLI.
Do: Team invite flow, role-based onboarding, shared dashboards/queries, org-level billing visibility.
The first experience for user #2 on an org should be as directed as user #1.
Measure: Avg users per paying org > 4 by August.
Automations
Why: Credits consumed without human action are the purest revenue —
and the product already supports it. Once self-serve Automations work (item 3), the goal is adoption:
every paying org should have at least one recurring automation. This is what makes NRR > 120%.
Do: Automation templates, suggested automations based on usage, “set it and forget it” flows.
Measure: 30%+ of credits from Automations by August.
| Risk | Why It Matters | How to Validate |
|---|---|---|
| Pipeline orgs churn | Highest-volume orgs run raw SQL pipelines with zero switching cost — any SQL warehouse works. Without AI adoption, there’s no moat. | Get pipeline orgs using agents to build their workflows. AI usage = switching cost. Track AI % per pipeline org monthly. |
| Per-user economics don’t replicate | 300–500 credits/day is validated on 2 internal power users. External users may consume less, use AI differently, or not adopt agents at all. | Instrument per-user daily credit consumption for external orgs. Need 10+ external data points in 3 months. |
| Orgs don’t expand | The funnel depends on orgs growing from $500 to $5–10K. If most orgs stay at $500–$1K, the funnel produces ~$50K/month instead of $164K — and the entire model breaks. | Track users-per-org and ARPU monthly. If no expansion signal by month 6, reassess the target. |
| Acquisition engine doesn’t exist yet | The funnel needs 120 orgs over 12 months (~10/month avg). More manageable than 245, but still requires a repeatable channel that doesn’t exist yet. | Identify top-of-funnel channel by month 4. Test: content, partnerships, PLG viral loops, outbound. Need 5+ qualified orgs/month by month 6. |
| Metric | Today | Apr ’26 (2 mo) | Aug ’26 (6 mo) |
|---|---|---|---|
| Funnel orgs at $500+/mo | early | 5 | 30 |
| Orgs building with agents | 0 external | 3+ | 50%+ |
| External Agentic CLI users | 0 | 3+ | 15+ |
| AI % across paying orgs | ~0% | >10% | 30% |
| Orgs with self-created Automations | 0 (team-created) | 3+ | 10+ |
| Orgs using 2+ product areas | siloed | 3+ | majority |
| Orgs > $1K/month | early | 3+ | 10+ |
OrgUsageEvent table via internal-cli -e prod admin usage-events.
21 external organizations, Flipside internal org (538,644 credits) excluded.category field: ai_usage (chat completions) or query_execution (SQL execution).
Credits are calculated by the usage event service based on token counts and query seconds respectively.session_id = NULL → Direct API (no chat session involved)ChatSession.meta.source = "cli" | "api" → CLI/MCP-drivenChatSession.meta.source = "web" | "agents-page" → Web UIsession_id presence/absence.