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The Forrester Wave™: Conversational AI Platforms for Customer Service, Q2 2026 evaluated 14 vendors across dozens of criteria. Rasa's overall profile in that evaluation reflects what Rasa is: a developer-first, deployable-on-premise platform built for enterprises that want to own their AI infrastructure from end to end. That's a deliberately focused story, and the scorecard reflects it.
Inside Forrester’s profile, three individual scores stand out.
Rasa scored a 5 out of 5 in resource orchestration and application execution, application lifecycle management, and pricing flexibility and transparency. Across all 14 vendors evaluated, no other platform matched Rasa’s value on all three. Let’s take a deeper look at each category.
Resource orchestration and application execution
In Forrester's evaluation framework, resource orchestration covers how a platform manages the moving parts of a conversational AI system at runtime. Those include coordinating skills, routing between deterministic flows and LLM-driven interactions, maintaining context across steps and systems, and ensuring that the right logic executes at the right moment.
For enterprise teams building customer-facing AI in regulated or complex environments, this matters more than it might appear in a feature checklist. A customer service agent that handles a billing dispute, authenticates a user, queries a backend system, and then escalates appropriately isn't just executing one task. They’re actually orchestrating a sequence of dependent actions across multiple systems, with compliance and data governance requirements attached to each step. When orchestration breaks down, the failure is usually invisible until something goes wrong in production.
Rasa's architecture treats orchestration as a top priority rather than a coordination layer bolted on afterward. The platform's unified runtime combines deterministic workflow logic (for the parts of a conversation where variance is unacceptable) with LLM-driven flexibility (for the parts where it's genuinely useful). Forrester's evaluation recognized this design choice explicitly, noting Rasa's strengths in resource orchestration and composability.
Rasa's own research supports why this resonates. In the 2026 State of Conversational AI report, 63% of enterprise leaders said they prefer hybrid architectures that combine LLM flexibility with deterministic logic. Only 13% favor fully agentic systems. For those buyers, a platform that can reliably orchestrate both is a must-have.
Application lifecycle management
Building a conversational AI application is one problem. Operating it over time (versioning updates, managing environments, tracking changes, testing before deployment, and maintaining audit trails) is a different and often harder one. This is what application lifecycle management covers in Forrester's framework, and it's where many platforms in the evaluation show visible gaps.
Rasa achieved a perfect score here as well, reflecting a development environment built around the needs of engineering teams running production-grade systems.
Rasa’s platform supports CI/CD pipeline integration, version control, environment separation, and the operational governance structures that IT and compliance teams require before they sign off on a deployment. For organizations where AI systems touch regulated processes like credit decisions, customer authentication, and claims handling, the ability to demonstrate what changed, when, and why isn't optional. It's the kind of auditability that separates AI that passes a compliance review from AI that doesn't.
This also connects to a broader finding from Rasa's 2026 survey: 60% of enterprise respondents cited "black box" issues or compliance concerns as their top challenge, ahead of integration complexity and deployment difficulty. The implication is that buyers aren't just evaluating whether a platform can build the application. They're also evaluating whether they can stand behind it once it's live.
Pricing flexibility and transparency
Rasa was the only vendor in the Q2 2026 evaluation to score a 5 in pricing flexibility and transparency.
The mechanics here matter. Rasa uses a freemium model that allows developer teams to build, test, and validate applications before any commercial commitment. That's a meaningful structural difference from most enterprise AI platforms, where pricing is opaque, tied to consumption metrics that are hard to predict at the start of a project, or only disclosed through a sales process. For engineering and AI teams making a build-versus-buy decision or evaluating multiple platforms in parallel, the ability to work with a platform in a real environment before signing a contract removes a significant source of friction and risk.
Forrester's evaluation weighted pricing flexibility and transparency at 5% of the overall strategy score. That’s a relatively modest share, which is one reason a perfect score here doesn't move the composite needle as much as equivalent scores in higher-weighted categories. But for buyers trying to get a proof of concept off the ground without a lengthy procurement process, that score reflects something concrete about how Rasa approaches the relationship with the teams building on its platform.
What these differentiators mean for you
Taken individually, each of these scores reflects a specific capability strength. Taken together, they describe a platform designed for teams that need to build AI they can govern, operate, and afford to evaluate properly.
That's not the right profile for every organization. Enterprise leaders looking for a broad, no-code toolset, an extensive partner ecosystem, or a managed deployment experience will find better fits elsewhere in the Wave. Forrester's own "fit for" framing is direct on this point: Rasa is built for organizations with developer resources who want to own their applications — or for those where on-premise deployment is a hard requirement.
For the organizations that fit this description, the three scores above should resonate. Read the full Forrester evaluation to see how Rasa's scorecard compares across all evaluated criteria.
Download the Forrester Wave™: Conversational AI Platforms for Customer Service, Q2 2026
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