Tax Practice AI - Cost & Pricing Detail¶
Version: 4.0 Last Updated: 2025-12-25 Audience: Internal (SaaS business planning)
Related Documentation¶
| Document | Purpose |
|---|---|
| AI_DELEGATION_STRATEGY.md | Model routing, batch API, caching implementation |
| PROCESS_FLOWS.md | Workflow states (PendingFinalQA, etc.) |
| ARCHITECTURE.md | System architecture, infrastructure |
Executive Summary¶
This document details the economics of Tax Practice AI as a SaaS product. We sell AI-powered tax analysis to accounting firms on a per-return fee basis.
The Business Model: - We charge accounting firms $20-30 per tax return - Our cost to deliver: $0.54-0.75 per return (AI + infrastructure) - Gross margin: 96-98%
At Scale (10 client firms, 10,000 returns/year): - Annual revenue: $200,000-300,000 - Annual cost: ~$7,600-9,700 - Annual profit: ~$190,000-292,000
Updated Dec 2025: Opus 4.5 pricing (3x cheaper) + batch/caching optimizations reduce AI costs significantly.
1. Understanding Tokens (Non-Technical Explanation)¶
What is a Token?¶
A token is how AI models measure text. Think of tokens like the "fuel" that powers AI—the more text processed, the more tokens consumed.
Simple rule of thumb: - 1 token ≈ 4 characters of English text - 1 token ≈ ¾ of a word - 100 tokens ≈ 75 words ≈ 1 short paragraph
Practical Examples¶
| Document | Approximate Size | Tokens |
|---|---|---|
| A single sentence | 15 words | ~20 tokens |
| One paragraph | 100 words | ~130 tokens |
| One page of text | 500 words | ~670 tokens |
| A W-2 form (extracted data) | ~200 words | ~270 tokens |
| A 1099-B with 50 transactions | ~800 words | ~1,100 tokens |
| Full conversation (10 Q&A exchanges) | ~2,000 words | ~2,700 tokens |
Tax Return Token Examples¶
| Return Type | Description | Est. Tokens |
|---|---|---|
| Simple 1040 | W-2 only, standard deduction, single state | 40,000-60,000 |
| Typical 1040 | W-2, 1099s, itemized deductions, single state | 90,000-120,000 |
| Complex Individual | K-1s, rental properties, multi-state | 150,000-200,000 |
| S-Corp (1120S) | Business return with K-1 generation | 200,000-300,000 |
| Partnership (1065) | Multiple partners, complex allocations | 250,000-400,000 |
Why Tokens Matter for Pricing¶
We pay Anthropic (the AI provider) based on tokens consumed: - Input tokens (what we send to AI): Reading documents, providing context - Output tokens (what AI sends back): Answers, analysis, worksheets
Different AI models have different token costs—we optimize by using cheaper models for simple tasks and expensive models only for complex analysis.
2. Our Cost Structure¶
Fixed Monthly Costs (We Pay Regardless of Volume)¶
| Item | Monthly | Annual | Notes |
|---|---|---|---|
| AWS Aurora PostgreSQL | $80 | $960 | Database for all client data |
| AWS S3 Storage | $3 | $36 | Document storage |
| AWS Lambda | $5 | $60 | Serverless compute |
| Airflow VM (EC2) | $23 | $276 | Workflow orchestration |
| API Gateway | $4 | $48 | API routing |
| CloudWatch | $15 | $180 | Monitoring & logs |
| Secrets Manager | $5 | $60 | Credential storage |
| Anthropic Base | $50 | $600 | Minimum monthly AI spend |
| Total Fixed | $185 | $2,220 |
Variable Costs (Per Return)¶
| Item | Cost/Return | Notes |
|---|---|---|
| AI tokens (blended) | $0.33 | With Opus 4.5 pricing, see Section 4 |
| Persona (identity verification) | $0.30 | 20% of returns are new clients × $1.50 |
| Twilio (SMS) | $0.12 | Notifications and 2FA |
| Total Variable | $0.75 | Per return processed |
With batch + caching optimizations, AI cost drops to ~$0.12/return. See Sections 5-6.
Cost Per Return at Different Scales¶
| Scale | Returns/Year | Fixed/Return | Variable/Return | Total/Return |
|---|---|---|---|---|
| 1 firm (500) | 500 | $4.44 | $0.75 | $5.19 |
| 1 firm (1,000) | 1,000 | $2.22 | $0.75 | $2.97 |
| 5 firms (5,000) | 5,000 | $0.44 | $0.75 | $1.19 |
| 10 firms (10,000) | 10,000 | $0.22 | $0.75 | $0.97 |
Key Insight: Fixed costs amortize quickly. At 10,000 returns, our all-in cost is <$1/return.
With Full Optimization (batch + caching):
| Scale | Returns/Year | Fixed/Return | Optimized Variable | Total/Return |
|---|---|---|---|---|
| 1 firm (1,000) | 1,000 | $2.22 | $0.54 | $2.76 |
| 5 firms (5,000) | 5,000 | $0.44 | $0.54 | $0.98 |
| 10 firms (10,000) | 10,000 | $0.22 | $0.54 | $0.76 |
Optimized variable = $0.12 AI + $0.30 Persona + $0.12 Twilio = $0.54
3. Full Lifecycle Token Usage¶
A tax return isn't just processed once—it's referenced throughout the year. Our token estimates account for the full lifecycle.
Tax Season (January - April)¶
| Phase | AI Activities | Tokens/Return |
|---|---|---|
| Document Upload | Classification, extraction (15 docs avg) | 46,500 |
| Analysis | Prior year comparison, missing docs, anomalies | 23,800 |
| Preparer Q&A | Staff questions during review (5 avg) | 19,000 |
| Worksheet Generation | Export with citations | 23,000 |
| Subtotal | 112,300 |
Post-Season (May - December)¶
| Activity | Frequency | Tokens/Event | Annual Tokens |
|---|---|---|---|
| Estimated tax reminders | 4× per year | 800 | 3,200 |
| Client questions about filed return | 3× per year | 2,000 | 6,000 |
| Amendment analysis (10% of returns) | 0.1× | 50,000 | 5,000 |
| Subtotal | ~14,200 |
Full Year Token Budget Per Return¶
| Period | Tokens | % of Total |
|---|---|---|
| Tax Season (Jan-Apr) | 112,300 | 89% |
| Post-Season (May-Dec) | 14,200 | 11% |
| Annual Total | 126,500 | 100% |
Planning Note: Budget ~125,000 tokens per return per year, not just processing.
4. AI Model Pricing¶
Available Models (Anthropic Claude via AWS Bedrock)¶
| Model | Input Cost | Output Cost | Best For |
|---|---|---|---|
| Haiku 3 | $0.00025/1K | $0.00125/1K | Extraction, classification |
| Sonnet 4 | $0.003/1K | $0.015/1K | Analysis, Q&A |
| Opus 4.5 | $0.005/1K | $0.025/1K | Complex tax code, judgment |
Pricing as of Dec 2025 via AWS Bedrock. Opus 4.5 is 3x cheaper than previous Opus 4.1.
Cost Per Return by Strategy¶
| Strategy | Token Mix | Cost/Return |
|---|---|---|
| All Sonnet (simple) | 0% Haiku, 100% Sonnet | $0.64 |
| Delegation (recommended) | 60% Haiku, 35% Sonnet, 5% Opus | $0.33 |
| Expert-heavy | 40% Haiku, 40% Sonnet, 20% Opus | $0.55 |
Savings: Delegation strategy cuts AI cost by 48% vs. all-Sonnet approach.
For model routing rules and touchpoint assignments, see AI_DELEGATION_STRATEGY.md.
5. Optimization Cost Impact¶
This section summarizes the cost savings from our optimization strategies. For implementation details, see AI_DELEGATION_STRATEGY.md.
Batch API Savings¶
Batch processing via Anthropic's batch API is 50% cheaper than interactive.
| Scenario | % Batch | Annual Savings (1,000 returns) |
|---|---|---|
| Conservative | 30% | $72 |
| Moderate | 50% | $120 |
| Aggressive | 70% | $168 |
Realistic estimate: 50% batch = $0.12/return cost reduction.
For task classification and UX strategy, see AI_DELEGATION_STRATEGY.md.
Metadata Caching Savings¶
Extract once, reference forever. AI reads cached metadata instead of re-scanning original documents.
| Scenario | Without Caching | With Caching | Savings |
|---|---|---|---|
| First scan (W-2) | 2,500 tokens | 2,500 tokens | — |
| Q&A reference (×5) | 12,500 tokens | 1,500 tokens | 88% |
| Worksheet generation | 3,000 tokens | 600 tokens | 80% |
| Total per doc | 18,000 | 4,600 | 74% |
Per Return Impact:
| Metric | Without Cache | With Cache |
|---|---|---|
| Tokens/return | 126,500 | ~50,000 |
| AI cost/return (blended) | $0.40 | ~$0.16 |
| Savings | — | $0.24/return (60%) |
For cache structure and implementation, see AI_DELEGATION_STRATEGY.md.
Combined Optimization Summary¶
| Optimization | Starting Cost | After | Savings |
|---|---|---|---|
| Model delegation (60/35/5) | $0.64 | $0.33 | $0.31 |
| + Batch processing (50%) | $0.33 | $0.23 | $0.10 |
| + Metadata caching (75%) | $0.23 | $0.12 | $0.11 |
| Fully optimized | $0.12 | $0.52 |
Result: ~81% cost reduction vs. naive all-Sonnet interactive approach.
6. Tiered Validation & Revenue Opportunities¶
Tiered Validation Strategy (FUT-002)¶
Quality assurance without breaking the bank:
| Layer | Model | What It Does | Cost/Return |
|---|---|---|---|
| Self-validation | Haiku/Sonnet | Each model QAs own work | ~$0.002 |
| Pre-review | Sonnet | Catches issues before reviewer | ~$0.005 |
| Final QA (batch) | Opus | Metadata-only overnight review | ~$0.005 |
| Total QA | ~$0.012 |
Workflow: Approved → PendingFinalQA → (overnight) → PendingSignature or RevisionsNeeded
For workflow integration, see PROCESS_FLOWS.md and AI_DELEGATION_STRATEGY.md.
Expedited QA (Revenue Opportunity)¶
| Mode | Wait Time | Our Cost | Client Fee | Margin |
|---|---|---|---|---|
| Standard (batch) | 12-24 hrs | $0.005 | Included | — |
| Expedited | ~2 min | $0.03 | $5-10 | 97%+ |
Use cases: Filing deadline, rush returns, client priority.
Revenue impact at 1,000 returns (10% expedited): - 100 expedited × $7.50 avg = $750 incremental revenue - Cost: 100 × $0.03 = $3 - Pure margin: $747
Image Quality Escalation Cost Impact¶
Phone photos and low-quality scans escalate to higher-cost models:
| Scenario | % of Docs | Cost Impact |
|---|---|---|
| Native PDF/clean scan | 70% | Baseline |
| Phone photo | 20% | +$0.02/doc |
| Low-res/blurry | 8% | +$0.02/doc |
| Handwritten | 2% | → Human (no AI cost) |
Estimated impact: +$0.03/return average (already included in estimates above).
7. SaaS Pricing Model¶
Our Revenue: Per-Return Fee¶
We charge accounting firms based on returns processed. Customer pays per return, we deliver AI analysis.
Pricing Tiers¶
| Tier | What's Included | Our Price | Our Cost | Margin |
|---|---|---|---|---|
| Standard | Haiku extraction + Sonnet analysis | $20/return | $0.82 | 96% |
| Professional | Standard + Opus escalation (10%) | $30/return | $1.20 | 96% |
| Expert | Opus-primary for all analysis | $50/return | $3.50 | 93% |
Revenue Projections¶
Single Client Firm (1,000 returns/year)¶
| Tier | Revenue | Our Cost | Profit | Margin |
|---|---|---|---|---|
| Standard @ $20 | $20,000 | $3,040 | $16,960 | 85% |
| Professional @ $30 | $30,000 | $3,420 | $26,580 | 89% |
| Expert @ $50 | $50,000 | $5,720 | $44,280 | 89% |
Cost includes $2,220 fixed + variable per return
Scaling to Multiple Client Firms¶
| Clients | Returns/Year | Revenue @$25 | Total Cost | Annual Profit |
|---|---|---|---|---|
| 1 | 1,000 | $25,000 | $3,040 | $21,960 |
| 5 | 5,000 | $125,000 | $6,320 | $118,680 |
| 10 | 10,000 | $250,000 | $10,420 | $239,580 |
| 25 | 25,000 | $625,000 | $22,720 | $602,280 |
| 50 | 50,000 | $1,250,000 | $43,220 | $1,206,780 |
Annual Recurring Revenue (ARR) Targets¶
| Milestone | Client Firms | Returns | ARR @$25 | Annual Profit |
|---|---|---|---|---|
| Year 1 | 3 | 3,000 | $75,000 | $70,000 |
| Year 2 | 10 | 10,000 | $250,000 | $240,000 |
| Year 3 | 25 | 25,000 | $625,000 | $600,000 |
8. Competitive Positioning¶
Market Comparison¶
| Product | Price Model | Cost to Firm | What They Get |
|---|---|---|---|
| SurePrep | Per-return | $12-100/return | OCR + data extraction |
| UltraTax | Annual license | $7K-25K/year | Tax prep software (no AI) |
| Tax Practice AI | Per-return | $20-30/return | AI analysis + Q&A + worksheets |
Value Proposition¶
If we REPLACE SurePrep: - Client saves $0-80/return (depending on SurePrep tier) - We provide OCR + AI analysis (more than SurePrep offers)
If we COMPLEMENT SurePrep: - Client adds $20-30/return for AI assistant - Justified by reduced prep time and error catching
Pricing Strategy Recommendation¶
Launch at $25/return (Professional tier) - Competitive with mid-range SurePrep - Strong 96% margin - Room to discount for volume deals - Room to upsell to Expert tier
9. Client Perspective (Their P&L Impact)¶
While we focus on our margins, understanding client economics helps with sales.
Client Cost Structure (1,000 returns/year)¶
| Item | Without Us | With Us | Change |
|---|---|---|---|
| UltraTax | $15,000 | $15,000 | — |
| SurePrep ($20/return) | $20,000 | $0* | -$20,000 |
| SmartVault | $1,800 | $1,800 | — |
| Tax Practice AI | $0 | $25,000 | +$25,000 |
| Total Tech | $36,800 | $41,800 | +$5,000 |
If we replace SurePrep. If complementary, client pays both.
Client ROI Calculation¶
Time savings assumption: 30 minutes per return in reduced prep time - Preparer cost: $50/hour → $25 saved per return - 1,000 returns × $25 = $25,000 labor savings - Our cost: $25,000 - Net ROI: Break-even on labor alone, plus quality improvements
10. Existing Software Context¶
The following are costs our client firms already pay. These are NOT our costs, but understanding them informs our competitive positioning.
| Service | Purpose | Client's Annual Cost | Notes |
|---|---|---|---|
| UltraTax CS | Tax prep software | $7,000-25,000 | Thomson Reuters, includes e-filing |
| SurePrep | OCR/extraction | $2,000-15,000 | $12-100/return + setup |
| SmartVault | Client portal | $1,300-2,400 | $45-65/user/mo |
Integration approach: We integrate with these tools, not replace them (except potentially SurePrep).
11. Assumptions & Open Questions¶
Assumptions (To Be Validated)¶
| ID | Assumption | Impact if Wrong | Validation Method |
|---|---|---|---|
| A1 | 15 documents per return average | ±30% on token costs | Analyze pilot data |
| A2 | 5 Q&A interactions per return | ±20% on token costs | Track actual usage |
| A3 | 20% of returns are new clients | ±10% on Persona costs | Client data analysis |
| A4 | Haiku handles 60% of tokens | ±15% on AI costs | Monitor model routing |
| A5 | $25/return is acceptable price | Revenue risk | Market testing |
| A6 | Batch processing saves 50% | ±5% on total costs | Measure actual savings |
Open Questions for Business Partner Discussion¶
- Pricing strategy: Fixed per-return fee vs. tiered by return complexity?
- Volume discounts: Offer breaks at 2,500 / 5,000 / 10,000 returns?
- Annual contracts: Require annual commitment or allow month-to-month?
- SurePrep positioning: Market as replacement or complement?
- Expert tier demand: Will firms pay $50/return for Opus-level analysis?
- Year-round usage: Charge separately for post-season Q&A support?
- Overages: How to handle firms that exceed token estimates?
Follow-Up Analysis Needed¶
- Pilot with 2-3 firms to validate token usage assumptions
- Survey target market on price sensitivity
- Benchmark SurePrep pricing for different firm sizes
- Model churn scenarios and customer lifetime value
- Calculate break-even point (how many clients to cover fixed costs)
Document History¶
| Version | Date | Changes |
|---|---|---|
| 4.0 | 2025-12-25 | Document restructure: Moved implementation details to AI_DELEGATION_STRATEGY.md. Cost-only focus with references. Consolidated sections 5-6. |
| 3.1 | 2025-12-25 | Reconciliation: Opus 4.5 pricing, tiered validation, expedited QA, cross-ref AI_DELEGATION_STRATEGY.md |
| 3.0 | 2024-12-25 | Major revision: SaaS perspective, token explainer, lifecycle usage, assumptions |
| 2.1 | 2024-12-25 | Add model delegation strategy |
| 2.0 | 2024-12-25 | Add AI model strategy, token breakdown, vendor pricing |
| 1.1 | 2024-12-25 | Add cost per return analysis and sample P&L |
| 1.0 | 2024-12-25 | Initial version, extracted from ARCHITECTURE.md |