Backboard.io announced a major pricing update designed to address one of the fastest-growing challenges in AI adoption: unpredictable costs, fragmented infrastructure, and a lack of control over how compute is consumed in production systems.
As AI systems move from experimentation to mission-critical software, teams are discovering that token-based pricing alone fails to reflect how real, stateful systems behave in production. Costs fluctuate based on retries, prompt growth, orchestration logic, routing decisions, and context expansion—leaving developers unable to forecast spend and enterprises struggling to govern it.
Backboard’s updated pricing model introduces predictable entry costs, usage-level transparency, and fine-grained control over compute allocation, all delivered through a single API.
The Problem AI Teams Face Today
Most AI stacks suffer from three structural issues:
• Cost volatility that makes AI spend difficult to predict, budget, or explain
• Fragmented infrastructure across models, memory, orchestration, and monitoring
• Limited control over compute allocation, with low-value tasks often routed to expensive reasoning models
As systems scale, these issues compound—turning AI spend into an operational risk rather than a controllable engineering decision.
Backboard now uses a simple, transparent pricing model:
• $9 per month subscription
• Usage-based pricing
• A Free tier for tinkerers
There are no tiers to decode, no bundled plans, and no surprise minimums.
Free Tier for Real Evaluation
The Free tier includes credits that can be used across:
• Memory reads
• Memory writes
• Stored memories
• Tokens
This allows teams to test real workflows, state, and routing logic in production-like conditions before committing to a paid plan.
Modular by Design
Backboard is modular by design. Teams do not need to adopt the entire platform on day one.
Developers can start with only what they need—memory, orchestration, retrieval, model routing, or execution management—and integrate Backboard alongside existing infrastructure. Components can be added incrementally as systems evolve, reducing adoption risk and avoiding forced stack replacement.
This modularity makes Backboard suitable for both greenfield projects and existing production systems.
Backboard is built to give teams active control over AI compute.
Not every AI task requires an expensive reasoning model. With Backboard, deterministic or low-compute tasks can be routed to lower-cost or open-source models, while premium reasoning models are reserved for work that genuinely requires them. All routing, memory, orchestration, and execution is managed under a single API, allowing teams to intentionally allocate AI spend instead of passively absorbing it.
How Usage Is Priced
Usage is billed based on what the system actually does:
• Memory reads: $0.003 per read
• Memory writes: $0.0016 to $0.005 per write (batched when possible to reduce cost)
• Stored memories: $0.25 per 100,000 stored memories
• Tokens: billed at underlying provider rates
Backboard does not arbitrarily mark up token pricing. As platform efficiencies improve, savings are passed directly to users rather than hidden behind new tiers.
Built-In Visibility
All usage and charges are visible in real time. Users can see how much they have used, what they are being charged for, and how costs break down across memory, storage, orchestration, and tokens—without support tickets or manual reporting.
What Backboard Replaces (If You Want It To)
Backboard is not a standalone memory database or token proxy. The platform can consolidate:
• Stateful threads
• Memory reads and writes
• Retrieval-augmented generation (RAG)
• Orchestration
• Multi-provider LLM routing
• Execution and lifecycle management
Teams can replace multiple layers of their AI stack over time or use Backboard selectively where it delivers the most value.
Why This Matters Now
For startups, Backboard offers a low-friction entry point, cost discipline from day one, and the ability to scale without re-architecting later. For enterprises, it enables forecastable AI spend, governance, and flexibility across model providers—without lock-in.
As AI adoption matures, value is shifting away from raw model access toward control, efficiency, and system behavior. Backboard is designed to operate at that layer: the intelligence control plane above model providers.
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