Multi-tenancy is the economic foundation of SaaS, enabling one codebase and infrastructure to serve thousands of customers. But adding AI capabilities to a multi-tenant system introduces challenges that traditional SaaS architecture patterns were not designed to handle. AI workloads are compute-intensive and variable in duration. LLM context windows can leak data between tenants if not carefully managed. Token usage drives significant variable costs that must be attributed per tenant. And enterprise customers increasingly demand isolated AI environments for compliance. This guide covers every layer of multi-tenant AI SaaS architecture, from database partitioning through AI model isolation to per-tenant billing, with production-tested patterns and code examples.
Tenant Isolation Models: Pool, Bridge, and Silo
The isolation model you choose determines your cost structure, security posture, and operational complexity. There is no universally correct answer. Most production SaaS applications use a hybrid approach, offering different isolation levels at different pricing tiers.
- Pool model (shared everything): All tenants share the same database, compute, and AI inference endpoints. Tenant data is separated by a tenant_id column with row-level security (RLS). Lowest infrastructure cost per tenant. Best for: self-serve tiers with high tenant counts and low individual usage.
- Bridge model (logical isolation): Tenants share infrastructure but have logical separation: separate database schemas, dedicated AI inference queues, and per-tenant rate limits. Moderate cost. Provides meaningful isolation without dedicated hardware. Best for: professional tiers where tenants need predictable performance.
- Silo model (dedicated infrastructure): Each tenant gets dedicated database instances, compute resources, and optionally dedicated AI model deployments or fine-tuned models. Highest cost but maximum isolation. Best for: enterprise customers with compliance requirements, data residency mandates, or performance SLAs.
// Tenant Context Resolution and Isolation
// Middleware that resolves tenant and enforces isolation
import { Request, Response, NextFunction } from 'express';
import { Pool } from 'pg';
interface TenantConfig {
id: string;
name: string;
plan: 'free' | 'pro' | 'enterprise';
isolationLevel: 'pool' | 'bridge' | 'silo';
databaseSchema: string;
aiModelConfig: {
model: string;
maxTokensPerRequest: number;
maxTokensPerDay: number;
dedicatedEndpoint?: string;
};
dataResidency: 'us' | 'eu' | 'ap';
}
// Connection pools per isolation level
const sharedPool = new Pool({ connectionString: process.env.SHARED_DB_URL });
const tenantPools = new Map<string, Pool>(); // For silo tenants
async function tenantMiddleware(
req: Request,
res: Response,
next: NextFunction
) {
const tenantId = extractTenantId(req); // From JWT, subdomain, or header
if (!tenantId) {
return res.status(401).json({ error: 'Tenant not identified' });
}
const tenant = await getTenantConfig(tenantId);
if (!tenant) {
return res.status(404).json({ error: 'Tenant not found' });
}
// Set up database connection based on isolation level
switch (tenant.isolationLevel) {
case 'pool':
// Shared database with RLS
req.db = sharedPool;
req.dbSetup = `SET app.current_tenant = '${tenant.id}'`;
break;
case 'bridge':
// Shared database, separate schema
req.db = sharedPool;
req.dbSetup = `SET search_path TO tenant_${tenant.id}, shared`;
break;
case 'silo':
// Dedicated database instance
if (!tenantPools.has(tenant.id)) {
tenantPools.set(tenant.id, new Pool({
connectionString: tenant.dedicatedDbUrl,
}));
}
req.db = tenantPools.get(tenant.id)!;
req.dbSetup = null;
break;
}
req.tenant = tenant;
next();
}
function extractTenantId(req: Request): string | null {
// Strategy 1: JWT claim
if (req.auth?.tenantId) return req.auth.tenantId;
// Strategy 2: Subdomain (acme.app.com -> acme)
const host = req.hostname;
const subdomain = host.split('.')[0];
if (subdomain !== 'app' && subdomain !== 'www') return subdomain;
// Strategy 3: Custom header (for API clients)
return req.headers['x-tenant-id'] as string || null;
}Database Partitioning for Multi-Tenant AI
AI SaaS applications have unique data requirements beyond traditional CRUD. You need to store and query three categories of data: application data (user content, settings), AI operational data (prompts, completions, tool call logs), and AI knowledge data (embeddings, vector indexes, RAG documents). Each category has different partitioning requirements.
-- Multi-tenant database schema with AI-specific tables
-- Uses PostgreSQL with RLS for pool-model tenants
-- Enable RLS on all tenant tables
ALTER TABLE documents ENABLE ROW LEVEL SECURITY;
ALTER TABLE ai_conversations ENABLE ROW LEVEL SECURITY;
ALTER TABLE ai_usage_logs ENABLE ROW LEVEL SECURITY;
-- RLS policy: tenants can only see their own data
CREATE POLICY tenant_isolation ON documents
USING (tenant_id = current_setting('app.current_tenant')::uuid);
-- AI conversation history (per tenant, per user)
CREATE TABLE ai_conversations (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
tenant_id UUID NOT NULL REFERENCES tenants(id),
user_id UUID NOT NULL REFERENCES users(id),
session_id UUID NOT NULL,
role VARCHAR(20) NOT NULL CHECK (role IN ('user', 'assistant', 'system')),
content TEXT NOT NULL,
model VARCHAR(100),
input_tokens INTEGER,
output_tokens INTEGER,
cost_usd DECIMAL(10, 6),
metadata JSONB DEFAULT '{}',
created_at TIMESTAMPTZ DEFAULT NOW()
) PARTITION BY RANGE (created_at);
-- Partition by month for efficient querying and retention
CREATE TABLE ai_conversations_2026_01
PARTITION OF ai_conversations
FOR VALUES FROM ('2026-01-01') TO ('2026-02-01');
CREATE TABLE ai_conversations_2026_02
PARTITION OF ai_conversations
FOR VALUES FROM ('2026-02-01') TO ('2026-03-01');
-- Per-tenant AI usage tracking for billing
CREATE TABLE ai_usage_logs (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
tenant_id UUID NOT NULL REFERENCES tenants(id),
user_id UUID NOT NULL,
feature VARCHAR(100) NOT NULL, -- 'document_analysis', 'chat', 'search'
model VARCHAR(100) NOT NULL,
input_tokens INTEGER NOT NULL,
output_tokens INTEGER NOT NULL,
total_tokens INTEGER GENERATED ALWAYS AS (input_tokens + output_tokens) STORED,
cost_usd DECIMAL(10, 6) NOT NULL,
duration_ms INTEGER,
success BOOLEAN DEFAULT true,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Index for efficient tenant billing queries
CREATE INDEX idx_ai_usage_tenant_date
ON ai_usage_logs (tenant_id, created_at DESC);
-- Materialized view for real-time billing dashboard
CREATE MATERIALIZED VIEW tenant_ai_usage_daily AS
SELECT
tenant_id,
date_trunc('day', created_at) AS usage_date,
feature,
model,
COUNT(*) AS request_count,
SUM(total_tokens) AS total_tokens,
SUM(cost_usd) AS total_cost_usd
FROM ai_usage_logs
WHERE created_at > NOW() - INTERVAL '90 days'
GROUP BY tenant_id, date_trunc('day', created_at), feature, model;
-- Vector storage for per-tenant RAG
-- Using pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;
CREATE TABLE tenant_embeddings (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
tenant_id UUID NOT NULL REFERENCES tenants(id),
document_id UUID NOT NULL,
chunk_index INTEGER NOT NULL,
content TEXT NOT NULL,
embedding vector(1536),
metadata JSONB DEFAULT '{}',
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- HNSW index for fast similarity search, scoped by tenant
CREATE INDEX idx_tenant_embeddings_vector
ON tenant_embeddings
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);Shared vs Dedicated AI Models per Tenant
One of the most consequential decisions in multi-tenant AI SaaS is whether tenants share AI model endpoints or get dedicated instances. This decision affects cost, performance isolation, customization capability, and data privacy. Here is how we think about the trade-offs.
- Shared model endpoints (API-based): All tenants use the same Anthropic or OpenAI API endpoints. Tenant isolation is enforced at the application layer through separate API keys, request queuing, and rate limiting. This is the simplest and most cost-effective approach. Suitable for 95% of SaaS applications.
- Dedicated model deployments: Enterprise tenants get dedicated model instances hosted on your infrastructure (AWS Bedrock, Azure OpenAI, or self-hosted). Provides compute isolation, guarantees latency SLAs, and ensures tenant data never shares inference infrastructure. Required for high-security or high-compliance tenants.
- Per-tenant fine-tuned models: Tenants can fine-tune base models on their own data for improved accuracy. Store fine-tuned model artifacts per tenant. This is a premium feature that justifies higher pricing. Consider offering this only at enterprise tiers.
- Per-tenant RAG context: Each tenant has their own vector index and knowledge base. The base model is shared, but the retrieval context is tenant-specific. This is the most common approach for SaaS products that need tenant-specific AI knowledge without the cost of fine-tuning.
// Multi-Tenant AI Model Router
// Routes AI requests based on tenant configuration
import Anthropic from '@anthropic-ai/sdk';
interface TenantAIConfig {
model: string;
endpoint: 'shared' | 'dedicated';
dedicatedEndpointUrl?: string;
apiKey?: string; // Tenant-specific key for dedicated endpoints
maxTokensPerRequest: number;
dailyTokenBudget: number;
ragEnabled: boolean;
ragCollectionId?: string;
}
class MultiTenantAIRouter {
private sharedClient: Anthropic;
private dedicatedClients = new Map<string, Anthropic>();
private usageTracker: UsageTracker;
constructor() {
this.sharedClient = new Anthropic();
this.usageTracker = new UsageTracker();
}
async processRequest(
tenantId: string,
config: TenantAIConfig,
messages: Anthropic.MessageParam[]
): Promise<Anthropic.Message> {
// Check daily budget
const todayUsage = await this.usageTracker.getTodayUsage(tenantId);
if (todayUsage >= config.dailyTokenBudget) {
throw new AIBudgetExceededError(tenantId, todayUsage, config.dailyTokenBudget);
}
// Optionally augment with tenant-specific RAG context
let augmentedMessages = messages;
if (config.ragEnabled && config.ragCollectionId) {
augmentedMessages = await this.augmentWithRAG(
tenantId,
config.ragCollectionId,
messages
);
}
// Route to appropriate endpoint
const client = this.getClientForTenant(tenantId, config);
const response = await client.messages.create({
model: config.model,
max_tokens: config.maxTokensPerRequest,
messages: augmentedMessages,
});
// Track usage for billing
await this.usageTracker.recordUsage(tenantId, {
model: config.model,
inputTokens: response.usage.input_tokens,
outputTokens: response.usage.output_tokens,
cost: this.calculateCost(
config.model,
response.usage.input_tokens,
response.usage.output_tokens
),
});
return response;
}
private getClientForTenant(
tenantId: string,
config: TenantAIConfig
): Anthropic {
if (config.endpoint === 'shared') {
return this.sharedClient;
}
// Dedicated endpoint - create or reuse client
if (!this.dedicatedClients.has(tenantId)) {
this.dedicatedClients.set(
tenantId,
new Anthropic({
baseURL: config.dedicatedEndpointUrl,
apiKey: config.apiKey,
})
);
}
return this.dedicatedClients.get(tenantId)!;
}
private async augmentWithRAG(
tenantId: string,
collectionId: string,
messages: Anthropic.MessageParam[]
): Promise<Anthropic.MessageParam[]> {
// Extract the last user message for similarity search
const lastUserMsg = messages
.filter(m => m.role === 'user')
.pop();
if (!lastUserMsg) return messages;
const query = typeof lastUserMsg.content === 'string'
? lastUserMsg.content
: lastUserMsg.content.map(b => 'text' in b ? b.text : '').join(' ');
// Search tenant-specific vector index
const relevantDocs = await vectorStore.search({
collection: collectionId,
tenantId, // Ensures tenant isolation in vector search
query,
topK: 5,
});
// Prepend context to the conversation
const context = relevantDocs
.map(d => d.content)
.join('\n\n---\n\n');
return [
{
role: 'user' as const,
content: `Context from knowledge base:\n${context}\n\n---\n\nUser question: ${query}`,
},
...messages.slice(0, -1), // Keep conversation history
];
}
private calculateCost(
model: string,
inputTokens: number,
outputTokens: number
): number {
const pricing: Record<string, { input: number; output: number }> = {
'claude-sonnet-4-20250514': { input: 3 / 1_000_000, output: 15 / 1_000_000 },
'claude-haiku-3-5': { input: 0.25 / 1_000_000, output: 1.25 / 1_000_000 },
};
const rate = pricing[model] || pricing['claude-sonnet-4-20250514'];
return inputTokens * rate.input + outputTokens * rate.output;
}
}Per-Tenant AI Usage Billing
Billing for AI usage in a multi-tenant SaaS requires a metering system that accurately tracks per-tenant consumption in real time. The billing model you choose impacts both revenue and customer behavior. Here are the three most common billing patterns for AI SaaS in 2026.
- Included allowance with overage: Each plan includes a token/request budget. Usage beyond the allowance is billed per unit. Example: Pro plan includes 1M tokens/month, overages at $5 per 100K tokens. This is the most common model because it is predictable for customers while capturing value from heavy users.
- Pure usage-based: Customers pay only for what they use, typically per token or per AI request. Lower barrier to entry but creates revenue unpredictability. Works well for developer-focused products and API platforms.
- Tiered feature access: Different plans unlock different AI capabilities. Free gets basic AI (Haiku), Pro gets advanced AI (Sonnet), Enterprise gets dedicated models and fine-tuning. Simpler to understand and sell, but may limit AI adoption on lower tiers.
// Usage-Based Billing Metering System
interface UsageEvent {
tenantId: string;
feature: string;
model: string;
inputTokens: number;
outputTokens: number;
costUsd: number;
timestamp: Date;
}
class BillingMeter {
private buffer: UsageEvent[] = [];
private flushInterval: NodeJS.Timeout;
constructor() {
// Batch-flush usage events every 10 seconds for efficiency
this.flushInterval = setInterval(() => this.flush(), 10_000);
}
async recordUsage(event: UsageEvent): Promise<void> {
this.buffer.push(event);
// Also update real-time counter in Redis for budget enforcement
await redis.incrBy(
`usage:${event.tenantId}:${todayKey()}:tokens`,
event.inputTokens + event.outputTokens
);
await redis.incrByFloat(
`usage:${event.tenantId}:${todayKey()}:cost`,
event.costUsd
);
}
async checkBudget(
tenantId: string,
estimatedTokens: number
): Promise<{ allowed: boolean; remaining: number }> {
const plan = await getPlanForTenant(tenantId);
const currentUsage = await redis.get(
`usage:${tenantId}:${currentMonthKey()}:tokens`
);
const used = parseInt(currentUsage || '0', 10);
const remaining = plan.monthlyTokenBudget - used;
return {
allowed: remaining >= estimatedTokens || plan.allowOverage,
remaining: Math.max(0, remaining),
};
}
private async flush(): Promise<void> {
if (this.buffer.length === 0) return;
const events = this.buffer.splice(0);
// Batch insert into usage log table
await db.aiUsageLogs.insertMany(
events.map(e => ({
tenant_id: e.tenantId,
feature: e.feature,
model: e.model,
input_tokens: e.inputTokens,
output_tokens: e.outputTokens,
cost_usd: e.costUsd,
created_at: e.timestamp,
}))
);
// Publish to billing system for invoice generation
await billingProvider.reportUsage(
events.map(e => ({
customerId: e.tenantId,
metric: 'ai_tokens',
value: e.inputTokens + e.outputTokens,
timestamp: e.timestamp,
}))
);
}
}Security Boundaries and Data Protection
In a multi-tenant AI system, security boundaries must prevent three categories of data leakage: direct data access across tenants, indirect leakage through AI model context, and side-channel leakage through usage patterns or error messages. Each requires specific countermeasures.
Multi-Tenant AI Security Checklist
Data layer: Row-level security enabled on all tenant tables. Separate vector indexes per tenant. Encryption at rest with per-tenant keys for silo tenants.
AI layer: Never include data from multiple tenants in the same LLM context window. Clear conversation history between tenant requests on shared infrastructure. Validate that RAG retrieval results belong to the requesting tenant.
API layer: Tenant ID validated on every request. Rate limits enforced per tenant. API keys scoped to single tenant. Audit log all cross-tenant access attempts.
Infrastructure layer: Network segmentation for silo tenants. Separate encryption keys per tenant. Regular penetration testing with tenant boundary focus.
// Tenant Data Isolation Guard
// Prevents cross-tenant data leakage in AI operations
class TenantIsolationGuard {
// Validate that all documents in AI context belong to the requesting tenant
async validateContextDocuments(
tenantId: string,
documentIds: string[]
): Promise<void> {
const docs = await db.documents
.select('id', 'tenant_id')
.whereIn('id', documentIds);
const foreignDocs = docs.filter(d => d.tenant_id !== tenantId);
if (foreignDocs.length > 0) {
await this.logSecurityEvent({
type: 'CROSS_TENANT_ACCESS_ATTEMPT',
tenantId,
details: `Attempted to access documents: ${foreignDocs.map(d => d.id).join(', ')}`,
severity: 'critical',
});
throw new SecurityError('Cross-tenant document access denied');
}
}
// Sanitize AI responses to remove any leaked tenant data
async sanitizeResponse(
tenantId: string,
response: string
): Promise<string> {
// Check for common patterns of data leakage
const otherTenantPatterns = await this.getOtherTenantIdentifiers(tenantId);
for (const pattern of otherTenantPatterns) {
if (response.includes(pattern.value)) {
await this.logSecurityEvent({
type: 'POTENTIAL_DATA_LEAKAGE',
tenantId,
details: `Response contained identifier from tenant ${pattern.tenantId}`,
severity: 'high',
});
// Redact the leaked information
response = response.replace(
new RegExp(escapeRegex(pattern.value), 'g'),
'[REDACTED]'
);
}
}
return response;
}
// Enforce tenant-scoped vector search
async tenantScopedSearch(
tenantId: string,
query: string,
topK: number
): Promise<SearchResult[]> {
const results = await vectorStore.search({
query,
topK,
filter: {
tenant_id: { $eq: tenantId }, // CRITICAL: always filter by tenant
},
});
// Double-check results belong to tenant (defense in depth)
return results.filter(r => r.metadata.tenant_id === tenantId);
}
}Scaling Strategies for Multi-Tenant AI
Scaling multi-tenant AI SaaS requires addressing three bottlenecks: database query performance as tenant count grows, AI inference throughput during peak demand, and cost efficiency as usage scales. Here are proven strategies for each.
- Database scaling: Use connection pooling (PgBouncer) to handle thousands of tenant connections. Implement read replicas for AI analytics queries. Partition large tables by tenant_id or time range. Consider sharding by tenant_id once you exceed single-instance capacity.
- AI inference scaling: Implement priority queues per tenant tier (enterprise requests process first). Use model routing to send simple requests to faster, cheaper models. Cache frequent AI operations (classification of common inputs, repeated embeddings). Batch similar requests across tenants for embedding generation.
- Cost optimization: Implement prompt caching (Anthropic's caching feature) for shared system prompts across tenants. Use Haiku for preprocessing and classification, Sonnet only for complex reasoning. Compress conversation history before sending to the model. Set per-tenant token budgets and alert before overages.
- Horizontal scaling: Deploy AI worker pools that auto-scale based on queue depth. Use Kubernetes Horizontal Pod Autoscaler with custom metrics (queue length, p95 latency). Pre-warm instances during predictable peak hours.
Tenant Onboarding and Provisioning
Automated tenant provisioning is essential for self-serve SaaS. When a new tenant signs up, the system must create their data partition, initialize AI resources, set usage limits, and configure billing. For bridge and silo tenants, this includes creating database schemas or dedicated instances. The entire process should complete in under 30 seconds for pool tenants and under 5 minutes for silo tenants.
// Automated Tenant Provisioning Pipeline
interface ProvisioningConfig {
tenantId: string;
plan: 'free' | 'pro' | 'enterprise';
isolationLevel: 'pool' | 'bridge' | 'silo';
region: 'us-east' | 'eu-west' | 'ap-south';
}
class TenantProvisioner {
async provision(config: ProvisioningConfig): Promise<TenantConfig> {
const steps = [
() => this.createTenantRecord(config),
() => this.provisionDatabase(config),
() => this.provisionAIResources(config),
() => this.configureBilling(config),
() => this.initializeDefaults(config),
];
const results: Record<string, any> = {};
for (const step of steps) {
try {
const result = await step();
Object.assign(results, result);
} catch (error) {
// Rollback completed steps on failure
await this.rollback(config.tenantId, results);
throw new ProvisioningError(
`Failed to provision tenant: ${(error as Error).message}`
);
}
}
return results as TenantConfig;
}
private async provisionDatabase(config: ProvisioningConfig) {
switch (config.isolationLevel) {
case 'pool':
// Just ensure RLS policies exist (already set up)
return { databaseSchema: 'public' };
case 'bridge':
// Create tenant-specific schema
const schema = `tenant_${config.tenantId.replace(/-/g, '_')}`;
await db.raw(`CREATE SCHEMA IF NOT EXISTS ${schema}`);
await db.raw(`
CREATE TABLE ${schema}.documents (LIKE public.documents INCLUDING ALL);
CREATE TABLE ${schema}.ai_conversations (LIKE public.ai_conversations INCLUDING ALL);
`);
return { databaseSchema: schema };
case 'silo':
// Provision dedicated database instance
const instance = await cloudProvider.createDatabaseInstance({
name: `tenant-${config.tenantId}`,
region: config.region,
size: 'db.t4g.medium',
encrypted: true,
});
await this.runMigrations(instance.connectionString);
return {
databaseSchema: 'public',
dedicatedDbUrl: instance.connectionString,
};
}
}
private async provisionAIResources(config: ProvisioningConfig) {
// Create tenant-specific vector collection
await vectorStore.createCollection({
name: `tenant_${config.tenantId}`,
dimensions: 1536,
metric: 'cosine',
});
// Set AI usage limits based on plan
const limits = {
free: { dailyTokens: 10_000, model: 'claude-haiku-3-5' },
pro: { dailyTokens: 500_000, model: 'claude-sonnet-4-20250514' },
enterprise: { dailyTokens: 5_000_000, model: 'claude-sonnet-4-20250514' },
};
return { aiModelConfig: limits[config.plan] };
}
}Conclusion
Building multi-tenant AI SaaS is fundamentally about making isolation and resource-sharing decisions at every layer of the stack. The patterns in this guide -- hybrid isolation models, tenant-scoped AI routing, per-tenant billing metering, defense-in-depth security, and automated provisioning -- provide a production-ready framework. Start with the pool model for most tenants, offer bridge isolation for professional tiers, and reserve silo isolation for enterprise customers who require it. Instrument usage tracking from day one so you can bill accurately and optimize costs as you scale.
Need help building a multi-tenant AI SaaS platform? Contact Jishu Labs for expert architecture consulting. We have designed and built multi-tenant AI platforms serving thousands of tenants with enterprise-grade isolation and security.
Frequently Asked Questions
What is the best database for multi-tenant AI SaaS?
PostgreSQL is the best default choice for multi-tenant AI SaaS because it offers native row-level security for tenant isolation, the pgvector extension for AI embeddings, table partitioning for performance at scale, and schema-based isolation for bridge-model tenants. Pair it with Redis for caching and rate limiting. Only add specialized databases (dedicated vector stores, time-series databases) when PostgreSQL cannot meet specific performance requirements.
How do you prevent data leakage between tenants when using shared AI models?
Prevent cross-tenant data leakage through four measures: never include data from multiple tenants in the same LLM API call, always include tenant_id filters in vector similarity searches (and validate results), clear any in-memory conversation state between tenant requests, and implement response sanitization that checks for identifiers belonging to other tenants. Defense in depth is essential because a single control failure should not expose tenant data.
How do you price AI features in a multi-tenant SaaS product?
The most successful pricing model for AI SaaS in 2026 is included allowance with overage. Each pricing tier includes a monthly AI usage budget (measured in tokens or AI requests). Usage beyond the budget is billed per unit. This gives customers cost predictability while allowing you to capture value from heavy users. Track costs accurately from day one with per-tenant metering. Common pricing: Free tier gets 10K tokens/month, Pro gets 500K tokens, Enterprise gets 5M+ with custom pricing.
When should I offer dedicated AI model instances to tenants?
Offer dedicated AI model instances when tenants have regulatory requirements that prohibit shared inference infrastructure, need guaranteed latency SLAs that shared endpoints cannot provide, want to fine-tune models on their proprietary data, or process volumes large enough that dedicated instances are more cost-effective than API pricing. In practice, this is typically only enterprise-tier customers representing 5-10% of your tenant base but 30-50% of revenue.
About Sarah Johnson
Sarah Johnson is the CTO at Jishu Labs with deep expertise in AI systems. She has built production AI agents for enterprise automation and developer tools.